Police Integrity Lost - A Study of Law Enforcement Officers Arrested, Stinson et al, 2016

• June 28, 2016 • Locations: United States of America • Topics: Police Misconduct, Statistics/Trends, Police

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The author(s) shown below used Federal funds provided by the U.S.
Department of Justice and prepared the following final report:

Document Title:

Police Integrity Lost: A Study of Law
Enforcement Officers Arrested

Author(s):

Philip Matthew Stinson, Sr., J.D, Ph.D., John
Liederbach, Ph.D., Steven P. Lab, Ph.D., Steven
L. Brewer, Jr., Ph.D.

Document No.:

249850

Date Received:

April 2016

Award Number:

2011-IJ-CX-0024

This report has not been published by the U.S. Department of Justice.
To provide better customer service, NCJRS has made this federally
funded grant report available electronically.

Opinions or points of view expressed are those
of the author(s) and do not necessarily reflect
the official position or policies of the U.S.
Department of Justice.

POLICE INTEGRITY LOST: A STUDY OF
LAW ENFORCEMENT OFFICERS ARRESTED
Final Technical Report
Award Number: 2011-IJ-CX-0024
National Institute of Justice
Office of Justice Program
U.S. Department of Justice
Principal Investigator:
Philip Matthew Stinson, Sr., J.D, Ph.D.
Telephone: 419-372-0373
E-Mail: stinspm@bgsu.edu
Co-Investigators:
John Liederbach, Ph.D.
Telephone: 419-372-1053
E-Mail: jlieder@bgsu.edu
Steven P. Lab, Ph.D.
Telephone: 419-372-2326
E-Mail: slab@bgsu.edu
Consultant:
Steven L. Brewer, Jr., Ph.D.
Telephone: 724-983-2954
E-Mail: slb64@psu.edu
Criminal Justice Program
Department of Human Services
College of Health & Human Services
Bowling Green State University
Bowling Green, OH 43403-0148
http://www.bgsu.edu/policeintegritylost
January 2016
This project was supported by Award No. 2011-IJ-CX-0024, awarded by the National Institute of
Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and
conclusions or recommendations expressed in this report are those of the authors and do not
necessarily reflect those of the Department of Justice.

This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

1
ABSTRACT
There are no comprehensive statistics available on problems with police integrity, and no
government entity collects data on all criminal arrests of law enforcement officers in the United
States. Police crimes are those crimes committed by sworn law enforcement officers with the
general powers of arrest. These crimes can occur while the officer is either on- or off-duty and
include offenses committed by officers employed by state and local law enforcement agencies.
This study provides a wealth of data on a phenomena that relates directly to police integrity—
data that previously did not exist in any useable format.
The first goal of the study is to determine the nature and extent of police crime in the
United States. The objective for this goal is to determine the incidence and prevalence of
officers arrested. A second goal is to determine what factors influence how an agency responds
to arrests of its officers. Objectives for this goal are to determine whether certain factors
influence agency response and employment outcomes: (a) severity of crimes for which officers
are arrested; (b) level of urbanization for each employing agency; (c) geographic location for
each employing agency; (d) length of service and age of arrested officers; and, (e) criminal case
outcomes. A final goal is to foster police integrity by exploring whether officer arrests correlate
with other forms of police misconduct. Objectives for this goal are to determine whether
arrested officers were also named as a civil defendant in any 42 U.S.C. §1983 federal court
actions during their careers, and to inform practitioners and policymakers of strategies that will
better identify problem officers and those at risk for engaging in police crime and its correlates.
The advent of nationwide, objective, and verifiable data on the law-breaking behavior of
sworn officers and provides potential benefits to law enforcement agencies that connect the
technical expertise of researchers to criminal justice policymakers and practitioners. These data

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provide direct guidance in three areas. First, the study provides agencies information on the
types of crime that are most frequently perpetrated by police officers. Second, the research
provides information on the relationship between police crimes and other types of misbehavior
that collectively comprise the problem officer. Third, nationwide data on police crimes and the
manner in which arrested officers are organizationally sanctioned provides points of comparison
for law enforcement agencies that confront these problems, as well as information on the degree
to which law enforcement agencies tend to sanction or ignore certain crimes committed by
officers.
This is a quantitative content analysis study of archived records reporting several
thousand arrests of police officers during the years 2005-2011. The primary information source
is the Google News search engine and its Google Alerts email update service. Chi-Square was
used to measure the statistical significance of the association between two variables measured at
the nominal level. Cramer’s V was utilized to measure the strength of the Chi-Square
association. Stepwise binary logistic regression was used to determine which of the predictor
variables are statistically significant in multivariate models. Classification tree analysis was
utilized to uncover the causal pathways between independent predictors and outcome variables.
The Google News searches resulted in the identification of 6,724 cases in which sworn
law enforcement officers were arrested during the years 2005 through 2011. The cases involved
the arrests of 5,545 individual sworn officers employed by 2,529 nonfederal state and local law
enforcement agencies located in 1,205 counties and independent cities in all 50 states and the
District of Columbia. The findings indicate that nonfederal law enforcement officers were
arrested nationwide during 2005-2011 at a rate of 0.72 officers arrested per 1,000 officers, and at
a rate of 1.7 officers arrested per 100,000 population nationwide.

3
TABLE OF CONTENTS
EXECUTIVE SUMMARY ...............................................................................................14
The Problem of Police Crime ..................................................................................14
The Purpose of the Study .........................................................................................16
Research Design.......................................................................................................17
Findings....................................................................................................................21
Conclusion ...............................................................................................................25
INTRODUCTION .............................................................................................................29
Statement of the Problem .........................................................................................33
Literature Citations and Review ..............................................................................41
Statement of Rationale for the Research ..................................................................60
METHODS ........................................................................................................................65
Coding and Content Analysis ..................................................................................65
Statistical Analysis ...................................................................................................69
Strengths and Limitations ........................................................................................72
RESULTS ..........................................................................................................................76
Part I: Full Police Crime Data Set Models...............................................................76
Part II: Sex-related Police Crime Data Set Models ...............................................104
Part III: Alcohol-related Police Crime Data Set Models .......................................124
Part IV: Drug-related Police Crime Data Set Models............................................139
Part V: Violence-related Police Crime Data Set Models .......................................151
Part VI: Profit-motivated Police Crime Data Set Models ......................................167
Part VII: Employing Law Enforcement Agencies of Arrested Officers ................177

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CONCLUSION ................................................................................................................188
Discussion of Findings...........................................................................................189
Implications for Policy and Practice ......................................................................199
Implications for Further Research .........................................................................207
REFERENCES ................................................................................................................210
DISSEMINATION OF RESEARCH FINDINGS...........................................................232
Publications ............................................................................................................232
Presentations ..........................................................................................................235
TABLES ..........................................................................................................................229
Table 1 – Arrested Officers and Agencies .............................................................238
Table 2 – Most Serious Offense Charged ..............................................................239
Table 3 – Victim Characteristics............................................................................240
Table 4 – Police Crime Arrest Cases: Bivariate Associations of Conviction ........241
Table 5 – Police Crime Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................244
Table 6 – Police Crime Arrest Cases: Bivariate Associations of Job Loss ...........245
Table 7 – Police Crime Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................249
Table 8 – Police Crime:
Bivariate Associations of Sex-related Arrest Cases...............................................250
Table 9 – Police Crime:
Logistic Regression Model Predicting Sex-related Arrest Cases ..........................254
Table 10 – Police Crime:
Bivariate Associations of Alcohol-related Arrest Cases ........................................255
Table 11 – Police Crime:
Logistic Regression Model Predicting Alcohol-related Arrest Cases ...................259

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Table 12 – Police Crime:
Bivariate Associations of Drug-Related Arrest Cases ...........................................260
Table 13 – Police Crime:
Logistic Regression Model Predicting Drug-related Arrest Cases ........................263
Table 14 – Police Crime:
Bivariate Associations of Violence-related Arrest Cases ......................................264
Table 15 – Police Crime:
Logistic Regression Model Predicting Violence-related Arrest Cases ..................268
Table 16 – Police Crime:
Bivariate Associations of Profit-motivated Arrest Cases ......................................269
Table 17 – Police Crime:
Logistic Regression Model Predicting Profit-motivated Arrest Cases ..................273
Table 18 – Civil Rights Litigation as a Correlate of Police Crime:
Bivariate Associations of Being Named as a Party Defendant
Pursuant to 42 U.S.C. §1983 at Some Point during
Law Enforcement Career .......................................................................................274
Table 19 – Logistic Regression Model Predicting Being Named
as a Party Defendant in a Section 1983 Action at Some Point
during Law Enforcement Career ............................................................................277
Table 20 – Arrested Officers and Agencies in Sex-related Cases .........................278
Table 21 – Most Serious Offense Charged in Sex-related Cases ..........................279
Table 22 – Victim Characteristics in Sex-related Cases ........................................280
Table 23 – Bivariate Associations of Conviction in
Sex-related Arrest Cases ........................................................................................281
Table 24 – Sex-related Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................282
Table 25 – Bivariate Associations of Job Loss in
Sex-related Arrest Cases ........................................................................................283
Table 26 – Sex-related Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................284

6
Table 27 – Bivariate Associations of Child Victims in
Sex-related Arrest Cases ........................................................................................285
Table 28 – Sex-related Arrest Cases:
Logistic Regression Model Predicting Child Victims ...........................................287
Table 29 – Bivariate Associations of Conviction in
Police Sexual Violence Arrest Cases .....................................................................288
Table 30 – Police Sexual Violence Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................289
Table 31 – Bivariate Associations of Job Loss in
Police Sexual Violence Arrest Cases .....................................................................290
Table 32 – Police Sexual Violence Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................291
Table 33 – Bivariate Associations of Conviction in
Driving-While-Female Arrest Cases......................................................................292
Table 34 – Driving-While-Female Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................293
Table 35 – Bivariate Associations of Job Loss in
Driving-While-Female Arrest Cases......................................................................294
Table 36 – Driving-While-Female Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................295
Table 37 – Arrested Officers and Agencies in Alcohol-related Cases ..................296
Table 38 – Most Serious Offense Charged in Alcohol-related Cases ...................297
Table 39 – Victim Characteristics in Alcohol-related Cases .................................298
Table 40 – Police DUI Arrest Cases: Incident Events ...........................................299
Table 41 – Police DUI Arrest Cases: Drug-related ...............................................300
Table 42 – Bivariate Associations of Conviction in
Alcohol-related Arrest Cases .................................................................................301
Table 43 – Alcohol-related Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................302

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Table 44 – Bivariate Associations of Job Loss in
Alcohol-related Arrest Cases .................................................................................303
Table 45 – Alcohol-related Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................305
Table 46 – Bivariate Associations of Conviction in
DUI Arrest Cases ...................................................................................................306
Table 47 – DUI Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................307
Table 48 – Bivariate Associations of Job Loss in DUI Arrest Cases ....................308
Table 49 – DUI Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................309
Table 50 – Arrested Officers and Agencies in Drug-related Cases .......................310
Table 51 – Most Serious Offense Charged in Drug-related Cases ........................311
Table 52 – Specific Drugs......................................................................................312
Table 53 – Victim Characteristics in Drug-related Cases ......................................313
Table 54 – Patterns of Drug-related Police Corruption .........................................314
Table 55 – Drug-related Arrest Cases:
CART Model Predicting Patterns of Corruption ...................................................315
Table 56 – Bivariate Associations of Conviction in
Drug-related Arrest Cases ......................................................................................316
Table 57 – Drug-related Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................317
Table 58 – Bivariate Associations of Job Loss in
Drug-related Arrest Cases ......................................................................................318
Table 59 – Drug-related Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................319
Table 60 – Arrested Officers and Agencies in
Violence-related Cases...........................................................................................320

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Table 61 – Most Serious Offense Charged in
Violence-related Cases...........................................................................................321
Table 62 – Victim Characteristics in
Violence-related Cases...........................................................................................322
Table 63 – Bivariate Associations of Conviction in
Violence-related Arrest Cases................................................................................323
Table 64 – Violence related Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................326
Table 65 – Bivariate Associations of Job Loss in
Violence-related Arrest Cases................................................................................327
Table 66 – Violence-related Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................330
Table 67 – Bivariate Associations of Conviction in
Officer-involved Domestic Violence Arrest Cases................................................331
Table 68 – Officer-involved Domestic Violence Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................332
Table 69 – Bivariate Associations of Job Loss in
Officer-involved Domestic Violence Arrest Cases................................................333
Table 70 – Officer-involved Domestic Violence Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................335
Table 71 – Arrested Officers and Agencies in
Profit-motived Cases ..............................................................................................336
Table 72 – Most Serious Offense Charged in
Profit-motivated Cases ...........................................................................................337
Table 73 – Victim Characteristics in Profit-motivated Cases ................................338
Table 74 – Bivariate Associations of Conviction in
Profit-motivated Arrest Cases ................................................................................339
Table 75 – Profit-motivated Arrest Cases:
Logistic Regression Model Predicting Conviction ................................................341
Table 76 – Bivariate Associations of Job Loss in
Profit-motivated Arrest Cases ................................................................................342

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Table 77 – Profit-motivated Arrest Cases:
Logistic Regression Model Predicting Job Loss....................................................344
Table 78 – 200 Largest State and Local Law Enforcement
Agencies in Study: Rates of Officers Arrested, 2005-2011
(Sorted by # of Full-Time Sworn Personnel) .........................................................345
Table 79 – Nonmetropolitan State and Local Law Enforcement
Agencies in Study: Rates of Officers Arrested, 2005-2011
(Sorted Alphabetically by Agency Name) .............................................................351
Table 80 – Primary State Police Agencies in Study:
Rates of Officers Arrested, 2005-2011
(Sorted Alphabetically by Agency Name) .............................................................367
Table 81 – Sheriff’s Offices in Study:
Rates of Officers Arrested, 2005-2011
(Sorted Alphabetically by Agency Name) .............................................................369
Table 82 – County Police Departments in Study:
Rates of Officers Arrested, 2005-2011
(Sorted Alphabetically by Agency Name) .............................................................382
Table 83 – 500 Largest Municipal Police Departments in Study:
Rates of Officers Arrested, 2005-2011
(Sorted Alphabetically by Agency Name) .............................................................384
Table 84 – Special State & Local Law Enforcement Agencies in
Study: Rates of Officers Arrested, 2005-2011
(Sorted Alphabetically by Agency Name) .............................................................399
FIGURES .........................................................................................................................403
Figure 1 – Police Crime Arrest Cases:
CART Model Predicting Conviction .....................................................................403
Figure 2 – Police Crime Arrest Cases:
CART Model Predicting Job Loss .........................................................................404
Figure 3 – Police Crime:
CART Model Predicting Sex-related Arrest Cases ...............................................405
Figure 4 – Police Crime:
CART Model Predicting Alcohol-related Arrest Cases.........................................406

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Figure 5 – Police Crime:
CART Model Predicting Drug-related Arrest Cases .............................................407
Figure 6 – Police Crime:
CART Model Predicting Violence-related Arrest Cases .......................................408
Figure 7 – Police Crime:
CART Model Predicting Profit-motivated Arrest Cases .......................................409
Figure 8 – CART Model Predicting Being Named as a
Party Defendant in a Section 1983 Action at
Some Point during Law Enforcement Career ........................................................410
Figure 9 – Sex-related Arrest Cases:
CART Model Predicting Conviction .....................................................................411
Figure 10 – Sex-related Arrest Cases:
CART Model Predicting Job Loss .........................................................................412
Figure 11 – Sex-related Arrest Cases:
CART Model Predicting Child Victims ................................................................413
Figure 12 – Police Sexual Violence Arrest Cases:
CHAID Model Predicting Conviction ...................................................................414
Figure 13 – Police Sexual Violence Arrest Cases:
CART Model Predicting Job Loss .........................................................................415
Figure 14 – Driving-While-Female Arrest Cases:
CHAID Model Predicting Conviction ...................................................................416
Figure 15 – Driving-While-Female Arrest Cases:
CHAID Model Predicting Job Loss .......................................................................417
Figure 16 – Alcohol-related Arrest Cases:
CART Model Predicting Conviction .....................................................................418
Figure 17 – Alcohol-related Arrest Cases:
CART Model Predicting Job Loss .........................................................................419
Figure 18 – DUI Arrest Cases:
CART Model Predicting Conviction .....................................................................420
Figure 19 – DUI Arrest Cases:
CART Model Predicting Job Loss .........................................................................421

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Figure 20 – Drug-related Arrest Cases:
CART Model Predicting Conviction .....................................................................422
Figure 21 – Drug-related Arrest Cases:
CART Model Predicting Job Loss .........................................................................423
Figure 22 – Violence-related Arrest Cases:
CART Model Predicting Conviction .....................................................................424
Figure 23 – Violence-related Arrest Cases:
CART Model Predicting Job Loss .........................................................................425
Figure 24 – Officer-involved Domestic Violence Arrest Cases:
CART Model Predicting Conviction .....................................................................426
Figure 25 – Officer-involved Domestic Violence Arrest Cases:
CART Model Predicting Job Loss .........................................................................427
Figure 26 – Profit-motivated Arrest Cases:
CART Model Predicting Conviction .....................................................................428
Figure 27 – Profit-motivated Arrest Cases:
CART Model Predicting Job Loss .........................................................................429
APPENDIX A: Employing Law Enforcement Agencies:
Rates of Officers Arrested Sorted by # of Full-Time Sworn Personnel ..........................430
Appendix A-1 – Nonmetropolitan State and Local Law Enforcement
Agencies in Study: Rates of Officers Arrested, 2005-2011
(Sorted by # Full-Time Sworn Personnel) .............................................................430
Appendix A-2 – Primary State Police Agencies in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by # Full-Time Sworn Personnel) .............................................................446
Appendix A-3 – Sheriff’s Offices in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by # Full-Time Sworn Personnel) .............................................................448
Appendix A-4 – County Police Departments in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by # Full-Time Sworn Personnel) .............................................................461
Appendix A-5 – 500 Largest Municipal Police Departments in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by # Full-Time Sworn Personnel) .............................................................463

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Appendix A-6 – Special State & Local Law Enforcement Agencies in
Study: Rates of Officers Arrested, 2005-2011
(Sorted by # Full-Time Sworn Personnel) .............................................................478
APPENDIX B: Employing Law Enforcement Agencies:
Rates of Officers Arrested Sorted by Rate per 1,000 Officers ........................................482
Appendix B-1 – 200 Largest State and Local Law Enforcement
Agencies in Study: Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 1,000 Officers) .......................................................................482
Appendix B-2 – Nonmetropolitan State and Local Law Enforcement
Agencies in Study: Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 1,000 Officers) .......................................................................488
Appendix B-3 – Primary State Police Agencies in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 1,000 Officers) .......................................................................504
Appendix B-4 – Sheriff’s Offices in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 1,000 Officers) .......................................................................506
Appendix B-5 – County Police Departments in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 1,000 Officers) .......................................................................519
Appendix B-6 – 500 Largest Municipal Police Departments in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 1,000 Officers) .......................................................................521
Appendix B-7 – Special State & Local Law Enforcement
Agencies in Study: Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 1,000 Officers) .......................................................................536
APPENDIX C: Employing Law Enforcement Agencies:
Rates of Officers Arrested Sorted by Rate per 100,000 Population ................................540
Appendix C-1 – 200 Largest State and Local Law Enforcement
Agencies in Study: Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 100,000 Population) ...............................................................540
Appendix C-2 – Nonmetropolitan State and Local Law Enforcement
Agencies in Study: Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 100,000 Population) ...............................................................546

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Appendix C-3 – Primary State Police Agencies in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 100,000 Population) ...............................................................562
Appendix C-4 – Sheriff’s Offices in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 100,000 Population) ...............................................................564
Appendix C-5 – County Police Departments in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 100,000 Population) ...............................................................577
Appendix C-6 – 500 Largest Municipal Police Departments in Study:
Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 100,000 Population) ...............................................................579
Appendix C-7 – Special State & Local Law Enforcement
Agencies in Study: Rates of Officers Arrested, 2005-2011
(Sorted by Rate per 100,000 Population) ...............................................................594
APPENDIX D: State and Local Law Enforcement Agencies:
Police Crime Arrest Cases ...............................................................................................598
APPENDIX E: Entity Relationship Diagram of
Stinson’s Police Crime Database .....................................................................................669

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EXECUTIVE SUMMARY
This study is a quantitative content analysis of archived news articles and court records
reporting on the arrest(s) of law enforcement officers in the United States from 2005-2011.
Police crimes are those crimes committed by sworn law enforcement officers given the general
powers of arrest at the time the offense was committed. These crimes can occur while the officer
is on or off duty and include offenses committed by state, county, municipal, tribal, or special
law enforcement agencies. Police crimes damage the occupational integrity of police, the
organizational legitimacy of the employing agency, and the overall authority and legitimacy of
the law enforcement enterprise. Three distinct but related research questions are addressed in
this study. First, what is the incidence and prevalence of police officers arrested across the
United States? Second, how do law enforcement agencies discipline officers who are arrested?
And, third, to what degree do police crime arrests correlate with other forms of police
misconduct?
The Problem of Police Crime
Surprisingly little is known about the crimes committed by law enforcement officers, in
part because there are virtually no official nationwide data collected, maintained, disseminated,
and/or available for research analyses. Researchers have utilized other methodologies to study
police misconduct and crime in the absence of any substantive official data, including surveys,
field studies, quasi-experiments, internal agency records, and the investigative reports of various
independent commissions delegated to report on this phenomenon within particular jurisdictions.
These methodologies have thus far failed to produce systematic, nationwide data on police
crime.

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The lack of data on police crime is clearly a problem, since the development of strategies
to mitigate police crime in the least requires that they be documented and described in some sort
of systematic and generalizable manner. From an organizational perspective, more
comprehensive data could provide comparisons among agencies on rates of police crime, and
subsequently contribute to the development and implementation of policies to deter police crime
and lessen damage to police-community relations in their aftermath. From a scholarly
perspective, the collection, analysis, and dissemination of more comprehensive police crime data
could instigate studies designed to identify significant correlates, explore relationships between
police crimes and more general forms of police deviance, and provide information on how police
culture and socialization potentially contribute to the problem. Scholars have yet to fully pursue
these and other important issues associated with the problem of police crime because we lack
any sort of comprehensive data on the types of crime that police commit and how frequently they
commit them.
The current study demands a conceptual framework that reflects both the broad range of
offenses committed by law enforcement officers and the generalized nature of our research
questions. Thus, the finding of the current study are organized within a conceptual framework
that incorporates five key types of police crime:


Sex-related Police Crime



Alcohol-related Police Crime



Drug-related Police Crime



Violence-related Police Crime



Profit-motivated Police Crime

16
The study is organized around these five types of police crime. We provide data pertinent to the
three broad research questions within each area, as well as additional data that further describes
the nature and character of police crime both in general and within each of the five types. The
conceptual framework serves to organize data that describes a phenomenon that encompasses a
wide range of criminal behavior. The framework also allows for the presentation and discussion
of data specifically focused on answering the stated research questions; but also, the wealth of
data collected on police crime as part of this project that does not readily fit under one or more of
the specific research questions.
The Purpose of the Study
The purpose of the current research project is to promote police integrity by gaining a
better understanding of police crime and agency responses to officer arrests. The study provides
a wealth of data on a phenomena that relates directly to police integrity—data that law
enforcement executives did not previously have access to because they did not exist in any
useable format. The first goal of this research is to determine the nature and extent of police
crime in the United States. A second goal of this research is to determine what factors influence
how a nonfederal law enforcement organization responds to arrests of officers. A third and final
goal of the research is to foster police integrity by exploring whether police crime and officer
arrests correlate with other forms of police misconduct.
The purpose for the current study evolved from long-standing obstacles associated with
the collection of data on police crime. The underlying reasons for the current study reflect the
ongoing lack of empirical data on the crimes committed by law enforcement officers—a
situation that relates directly to the limitations of previous methodologies used to collect data on
the phenomenon. Researchers have resorted to a variety of methodologies to learn more about

17
the nature and extent of police misconduct, corruption, or crime. Empirical scholarship has
usually incorporated one or more of the following: (a) officer surveys, (b) agency records, and
(c) sociological field studies. Studies that utilize one or more of these methodologies have
clearly contributed to the knowledge base on police crime, but the methodology used in the
present study offers clear advantages in terms of both the scope and quality of potential data on
police crime.
Research Design
Data for the current study were collected as part of a project designed to locate cases in
which sworn law enforcement officers had been arrested for any type of criminal offense(s).
Data were derived from published news articles using the Google News search engine and its
Google Alerts email update service. Google Alerts searches were conducted using the same 48
search terms developed by Stinson (2009). The Google Alerts email update service sent a
message each time one of the automated daily searches identified a news article in the Google
News search engine that matched any of the designated search terms. The automated alerts
contained a link to the URL for the news articles. The articles were located, examined for
relevancy, printed, logged, and then scanned, indexed, and archived in a digital imaging database
for subsequent coding and content analyses. The present study focuses on the identification and
description of the cases in which police officers were arrested during the years 2005-2011.
Content analyses were conducted in order to code the cases in terms of (a) arrested
officer, (b) employing nonfederal law enforcement agency, (c) each of the charged criminal
offenses, (d) victim characteristics, (e) organizational adverse employment outcomes, and (f)
criminal case dispositions. Each of the charged criminal offenses was coded using the data
collection guidelines of the National Incident-Based Reporting System (NIBRS) as the coding

18
protocol for each criminal offense category (see U.S. Department of Justice, 2000). Fifty-seven
criminal offenses are included in the NIBRS, consisting of 46 incident-based criminal offenses in
one of 22 crime categories as well as 11 additional arrest-based minor criminal offense
categories. In each case every offense charged was recorded on the coding instrument as well as
the most serious offense charged in each police crime arrest case. The most serious offense
charged was determined using the Uniform Crime Report’s (UCR) crime seriousness hierarchy
(see U.S. Department of Justice, 2004). An additional eight offenses were added following an
earlier pilot study (see Stinson, 2009) because police officers who were arrested often were
charged with criminal offenses not included in the NIBRS (e.g., online solicitation of a child,
indecent exposure, official misconduct / official oppression / violation of oath, vehicular hit-andrun, perjury / false reports / false statements, criminal deprivation of civil rights).
The primary unit of analysis in this study is criminal arrest case. One of the primary
issues in coding was differentiating between arrest cases with multiple victims and officers who
were arrested on multiple occasions within the study years 2005-2011. Arrest incidents that
involved multiple victims with corresponding criminal charges were assigned an individual case
for each respective victim. Additionally, law enforcement officers who were arrested on
multiple occasions had an arrest case generated in the project database for each respective arrest.
Cases were also coded on Stinson’s (2009) typology of police crime, which posits that
most crime committed by police officers is alcohol-related, drug-related, sex-related, violencerelated, and/or profit-motivated. The types of police crime are not mutually-exclusive
categories. Rather, each type of police crime is coded as a dichotomous variable because crimes
committed by officers often involve more than one type of police crime. Additionally, cases
were coded for the presence of police sexual violence and/or driving while female encounters.

19
Police sexual violence is operationalized as “those situations in which a female citizen
experiences a sexually degrading, humiliating, violating, damaging, or threatening act committed
by a police officer through the use of force or police authority” (Kraska & Kappeler, 1995, p.
93). Driving while female is operationalized as instances where a police officer stops a female
driver under the pretext of an alleged traffic violation and then abuses the power and authority of
his position to take advantage of a vulnerable motorist (Walker & Irlbeck, 2002, 2003). In some
cases, driving while female encounters escalate into sexual harassment, sexual assault, and in
rare instances, forcible rape.
Secondary data were employed from the Census of State and Local Law Enforcement
Agencies (CSLLEA) (U.S. Department of Justice, 2008) to ascertain demographic data including
the number of full-time sworn personnel and part-time sworn personnel employed by each
agency where arrested officers served. County and independent city were used to verify location
of arrested officers’ employing law enforcement agencies, as well as for use as a key variable to
merge other data sources into the project’s master database and data set. The U.S. Department of
Agriculture’s (2003) county-level urban to rural nine-point continuum scale was used to measure
rurality. Population data from the U.S. Census Bureau’s decennial census in years 2000 and
2010 were utilized for county, independent city, and state populations.
Analytic procedures were undertaken to ensure reliability of the data. An additional
coder was employed to independently code a random sample of five percent of the total number
of cases in the study. Intercoder reliability was assessed by calculating the Krippendorf’s alpha
coefficient across 195 variables of interest in this study on a random sample (n = 290, 4.3%) of
the cases in the study (N = 6,724) (see Hayes & Krippendorff, 2007). The Krippendorf’s alpha
coefficient (Krippendorf’s α = .9153) is strong across the variables in this study. The overall

20
level of simple percentage of agreement between coders across all of the variables in this study
(97.7%) also established a degree of reliability well above what is generally considered
acceptable in content analysis research.
Chi Square was used to measure the statistical significance of the association between
two variables measured at the nominal level. Cramer’s V measures the strength of that
relationship with values that range from zero to 1.0 and allows for an assessment of the
importance of the relationship. Stepwise binary logistic regression was used to determine which
of the predictor variables are statistically significant in multivariate models. Stepwise logistic
regression models are appropriate where the study is purely exploratory and predictive.
Classification tree analysis was utilized as a statistical technique to uncover the causal pathways
between independent predictors and various outcome variables of interest, including job loss, and
conviction. This is an exploratory study because little is known empirically about police crime
arrests and the specific factors responsible for conviction and job loss subsequent to the arrest of
a sworn law enforcement officers.
Strengths and Limitations
The news search methodology utilizing the Google News search engine and the Google
Alerts email update service provided an unparalleled amount of information on police crime
arrests at law enforcement agencies across the United States. The Google News search engine
algorithm offers some clear advantages over other aggregated news databases and the
methodologies employed by previous studies that used news-based content analyses to document
cases of sex-related police misconduct. The Google Alerts email update service provides the
ability to run persistent automated queries of the Google News search engine and deliver realtime search results.

21
There are four primary limitations of the data. First, this research study includes every
case known to the research team of a nonfederal sworn law enforcement officer who was
arrested during the years 2005-2011. Thus, this study is a census of the universe of police crime
arrest cases identified through our search methodology. We do not purport to include every
single instance of a law enforcement officer being arrested. Second, our research is limited by
the content and quality of information provided for each case. The amount of information
available on each case varied, and data for several variables of interest were missing for some of
the cases. Third, the data are limited to cases that involved an official arrest based on probable
cause for one or more crimes. We do not have any data on police officers who engaged in
criminal activity if their conduct did not result in an arrest. Fourth, we note that these data are
the result of a filtering process that includes the exercise of discretion by media sources in terms
of both the types of stories covered and the nature of the content devoted to particular stories.
Despite the noted limitations, the use of news articles as the primary data source is a long
established method of analyzing deviant/illegal police behavior (see, e.g., Lawrence, 2000;
Lersch & Feagin, 1996; Rabe-Hemp & Braithwaite, 2013; Ross, 2000).
Findings
Findings from the study provide three general observations about the nature of police
crime overall. First, police crimes are not uncommon. The study identified 6,724 arrest cases
from 2005-2011 involving 5,545 sworn law enforcement officers. The arrested sworn law
enforcement officers were employed by 2,529 state and local law enforcement agencies located
in 1,205 counties and independent cities in all 50 states and the District of Columbia. Sworn law
enforcement officers were arrested at a rate of 0.72 per 1,000 officers and 1.7 per 100,000 of the
population nationwide. Second, police crime is an occupationally-derived phenomenon. Police

22
work is conducive to all sorts of criminal behavior, largely because of plentiful opportunities
provided by the nature of the work and police-citizen interactions. Third, police crime is
complex and multivariate. Police crime can be alcohol-related, drug-related, sex-related,
violence-related, or profit-motivated. These five types of police crime are not mutually
exclusive, and there are numerous significant predictors of (a) each type of police crime, (b)
adverse employment outcomes, and (c) criminal case dispositions. Distinctions between on- and
off-duty police crime are often difficult to make. Aside from these general observations, the
findings can be summarized in terms of both the full data set and the five types of police crime.
The most common most serious offense charged in the cases overall were simple assault
(13%), driving under the influence (12.5%), aggravated assault (8.5%), forcible fondling (5.2%),
and forcible rape (4.8%). Slightly more than one-half of the cases (54%) ultimately resulted in
job loss for arrested officers. The factors that influence whether an arrested officer will be
criminally convicted or lose his or her job are numerous and complex, and include both legal
factors (e.g., most serious offense charged) and extralegal factors (e.g., age, years of service as a
sworn law enforcement officer, relationship of victim to the arrested officer). In terms of case
outcomes, the events of job loss and criminal conviction are not isolated. Job loss provides a
context for the incidence of criminal conviction and vice versa. The number of cases and
officers arrested in terms of the five types of police crime were as follows:


Sex-related police crime included 1,475 arrest cases of 1,070 sworn officers



Alcohol-related police crime included 1,405 arrest cases of 1,283 sworn officers



Drug-related police crime included 739 arrest cases of 665 sworn officers



Violence-related police crime included 3,328 arrest cases of 2,586 sworn officers



Profit-motivated police crime included 1,592 cases of 1,396 sworn officers

23
Sex-related Police Crime
The two most important points in regard to the sex-related crimes include the serious
nature of these events and the prevalence of relatively young victims. Serious cases of police
sexual violence are not isolated events. The study identified a total of 422 forcible or statutory
rapes, 352 cases of forcible fondling, and 94 sodomy arrest cases. Children seem to be
particularly vulnerable to law enforcement officers who perpetrate sex crimes. Almost one-half
of the known victims were children, and the second-most commonly occurring category in terms
of the victim's relationship to the arrested officer was an unrelated child. Arrested officers were
criminally convicted on at least one charge in four-fifths (80%) of the sex-related cases in which
conviction data were available. The simple odds of job loss are 2.8 times greater if the arrested
officer was ultimately convicted of a sex-related crime.
Alcohol-related Police Crime
Job loss in the aftermath of being arrested for an alcohol-related police crime is tied to
organizational characteristics of an arrested officer’s employing state or local law enforcement
agency. Arrested officers employed by law enforcement agencies with 1-99 full-time sworn
personnel lost their jobs in less than half (44.6%) of the alcohol-related cases, and officers
employed by agencies with 100 or more full-time sworn personnel lost their jobs in less than
one-third (29.8%) of the alcohol-related cases.
Sworn law enforcement officers engaging in drunk driving is a major problem and
concern. There were 960 cases of police DUI (driving under the influence) arrests. These police
DUI arrest cases largely provide examples of officers who have lost their exemption from law
enforcement. That is to say, state and local sworn law enforcement officers do not typically
arrest other sworn law enforcement officers, especially for drunk driving. In many of the police

24
DUI arrest cases in this study, however, the drunk driving officer did something in terms of the
incident events that led to being arrested. For example, many of the police DUI arrest cases
involved traffic accidents (51%) often resulting in victim injury (24.1%) or fatalities (4%).
Arrested officers are known to have lost their jobs as sworn law enforcement officers in less than
one-third (29.8%) of the police DUI arrest cases.
Drug-related Police Crime
Drug-related crime by sworn law enforcement officers tends to spawn all sorts of other
kinds of police misconduct and police crime. Personal use of drug does not seem to be the
primary problem in drug-related police crime. Drug-related police crime often involves drug
trafficking, facilitation of the drug trade, and shakedowns of citizens most often associated with
the trade of cocaine and crack. Taken together, cocaine and marijuana account for over one-half
of the drug-related police crime arrest cases. Well over one-half of the drug-related police crime
arrest cases were also profit-motivated police crime.
Violence-related Police Crime
Policing is often violent. A major problem identified in this study, however, is officerinvolved domestic violence. There were 961 cases of officer-involved domestic violence.
Approximately one-third of the cases involving a sworn law enforcement arrested for officerinvolved domestic violence are known to have resulted in the arrested officer losing his or her
job as a result of the arrest. When the victim is currently involved (i.e., current spouse, current
boyfriend/girlfriend), the arrested officer is less likely to be convicted than when the victim is
somebody else in officer-involved domestic violence arrest cases. The simple odds of an officer
losing his or her job after being arrested for a crime relating to officer-involved domestic
violence are greater when the relationship with the victim is more distant. Serious concerns

25
regarding compliance with the gun and ammunition prohibitions of the Lautenberg Amendment
(1996) to the Federal Gun Control Act of 1968 for persons convicted of qualifying crimes of
domestic violence are raised by officer-involved domestic violence arrest cases in this study.
There is no exception to the gun prohibition in the federal statute for sworn law enforcement
officers who have been convicted of qualifying misdemeanor crimes of domestic violence.
Profit-motivated Police Crime
Most of the profit-motivated crimes occurred at the street-level and involved patrol
personnel. These crimes are more like street crimes than white collar crimes. The most common
most serious offense charged in the profit-motivated police crime arrest cases were unclassified
thefts (16%), false pretenses (theft by deception) (12.5%), drug offenses (11.9%), robbery (6.4%)
thefts from buildings (5.8%), and extortion or blackmail (5.3%). More than two-thirds of the
sworn law enforcement officers arrested for profit-motivated crime lost their jobs (67%) and
more than half of the profit-motivated arrest cases resulted in conviction (57.4%). The single
largest predictor of job loss in profit-motivated police crime arrest cases is when the profitmotivated crime is also drug-related.
Conclusion
Cases in which sworn law enforcement officers act as criminals—whether dealing drugs,
or driving drunk, or sexually molesting a vulnerable citizen—strike a direct blow to the law
enforcement enterprise and the essence of what it means to be a law enforcement officer: protect
and serve. These cases threaten to undermine public trust in both the authority and legitimacy of
state and local law enforcement organizations, and the work of law-abiding sworn officers who
go about their job selflessly, efficiently, and professionally every day. Police crime as a topic

26
worthy of empirical study however is not clearly understood, and would probably best be
described as untapped or at the very least not sufficiently explored.
The contrast between the topic's substantive weight and comparatively light coverage
within the scholarship is mostly due to an absence of suitable data. The traditional sources of
data and methods of study, whether official statistics, self-report surveys, or direct observations,
either do not exist in any usable format or are ill-equipped to identify, count, or provide the basis
for empirical analyses of instances in which police perpetrate crimes themselves. These cases
have thus far escaped large-scale empirical scrutiny, but they are intrinsically newsworthy
events. Those in the news media need to identify stories that will be of interest to their audience,
and cases of police crime typically include storylines that are clearly newsworthy. This project
utilized a methodology designed to capitalize on the newsworthy character of police crime,
identify these events, and subject them to analyses that have thus far been impossible. Given the
previous unavailability of data and the relative absence of empirical studies dedicated to the
topic, our work should be considered exploratory. The primary aim was to uncover cases of
police crime arrests and to provide the basis for what we hope will become an important
contribution to the establishment of a more substantive and useful line of research on the topic.
Implications for Policy and Practice
The findings of the current study have several direct implications for policy and practice
in state and local law enforcement agencies. First, employing law enforcement agencies should
have written policies to compel mandatory disclosure whenever a sworn officer is arrested for a
crime, as well as whenever a court issues an order of protection against a sworn officer. Second,
it is imperative that employing law enforcement agencies implement a policy of conducting
annual criminal background checks of all current sworn personnel to ascertain if any sworn

27
officer has been arrested or convicted of any crime. The practice of conducting annual criminal
background checks on every sworn law enforcement officer employed will ensure compliance
with the gun and ammunition prohibition provisions of the Lautenberg Amendment (1996) to the
federal Gun Control Act of 1968. Third, law enforcement agencies should have written policies
in place regarding standard agency responses to when a sworn law enforcement officer is
arrested, and the policy should address procedures for both situations where a sworn officer
employed by that agency is arrested as well as when the agency effectuates the criminal arrest of
a sworn law enforcement officer employed by some other law enforcement agency. Finally,
many arrested officers experience an unraveling of their lives, and mental health problems can be
present in some instances. Early intervention and warning systems should be utilized to track
instances of officers being arrested and officers should be referred to employee assistance
programs when appropriate.

28
Acknowledgements
Adam Watkins provided statistics expertise. Hannah Brewer, Michael Buerger, Melissa
Burek, Chris Dunn, Judith Leary, and Brooke Mathna provided technical expertise. Dan Lee,
Dennis Giever, Paul McCauley, and David Myers offered early encouragement.
The following graduate student research assistants at Bowling Green State University
worked on this project: Evin Carmack, Paige Crawford, Jacob Frankhouser, Maria Gardella,
Breanne Hitchens, Jessica Kirkpatrick, Krista Long, Matthew Roberts, Dennis Roehrig, Andrew
Rudnik, Adam Sierra, Scott Stevenson, Erin Thomson, Natalie Todak, Georgianna Whitely, and
Mallorie Wilson.
The following undergraduate student research assistants at Bowling Green State
University also worked on this project: Christy Adams, Warifa Azeez, Joelle Bridges, Zachary
Calogeras, Vincent Crews, Natalie DiChiro, Charles Eberle, Rachel Fettinger, Madeline Fisher,
Joanna Hanson, Breanne Hitchens, Ryan Hunter, Tanya Korte, Theresa Lanese, Mariah Lax,
Raven Ory, Tiffany Pleska, Ashley Roberts, Taylor Szalkowski, and Mallorie Wilson.
This project was made possible through the guidance of Chief Information Officer John
Ellinger and assistance from numerous Information Technology Services staff at Bowling Green
State University, including Adam Arthur, Chad Brandeberry, Kyle Butler, Patrick Enright, Chad
Fletcher, Michael Good, Danee Gunka, Lauren Hall, Matthew Haschak, Katrina Horvath, Margo
Kammeyer, Clinton LaForest, Bridget Place, Nick Rodgers, Thomas Shuman, Andries Smith,
Chris Wammes, and Deb Wells.

29
INTRODUCTION
Police crimes are those crimes committed by sworn law enforcement officers who are
given the general powers of arrest at the time the offense was committed. These crimes can
occur while the officer is either on- or off-duty and include offenses committed by officers
employed by state, county, municipal, tribal, or special law enforcement agencies. Anecdotes
and journalistic investigations indicate that police officers commit various criminal offenses
including larceny/theft, drug trafficking, driving while intoxicated, domestic assault, and
predatory sex offenses (see, e.g., Kappeler, Sluder, & Alpert, 1998). Further evidence of the
crimes committed by police is also contained in the reports of several independent commissions
on police misconduct (Knapp Commission, 1972; Mollen Commission, 1994; Pennsylvania
Crime Commission, 1974).
The occurrence of police crime should be a concern to police executives, scholars, and
the general public. Police crimes damage both the occupational integrity of police and the
legitimacy of the employing police agency. But, surprisingly little is known about the crimes
committed by law enforcement officers. There are no comprehensive statistics available on the
phenomena, and no government entity collects data on criminal arrests of police officers in the
United States (Anechiarico & Jacobs, 1996; Barak, 1995; Kane, 2007). There have been very
few studies that provide specific data on the nature and prevalence of police crime. Reiss' (1971)
classic field research found that officers commonly engaged in on-duty crime, mostly bribetaking and petty thefts; but, no other large-scale observational studies attempt to determine the
prevalence of police crime. Scholars have instead been more likely to broach the topic within
the context of more general studies on police corruption or misconduct (Fyfe & Kane, 2006).
Some studies use officer surveys to identify police attitudes toward misconduct or to measure

30
their propensity for criminal behavior under particular scenarios, but these methods are unlikely
to produce valid data on actual police crime because police tend to maintain a code of silence or
provide socially desirable responses to sensitive questions in regard to their own criminal
behavior and/or the criminal behavior of fellow officers (Fishman, 1978; Maguire & Mastrofski,
2000; Maguire & Uchida, 2000).
The lack of statistics and empirical studies on police crime is problematic. First, more
comprehensive data could be used to develop policies to deter police crimes and/or mitigate
damage to police-community relations in their aftermath. Second, data on the correlates of
police crime could provide information on the relationship between police crime and more
general forms of police deviance including corruption, discrimination, and other forms of abuse.
Third, an expansion of this line of research would provide information on the role of police
culture and socialization processes, particularly if these data include both on-duty and off-duty
crimes committed over the course of the career. Scholars have yet to fully pursue these
questions because we lack any sort of comprehensive statistics on the types of crime committed
by police and when and how often they commit them. These exploratory data are necessary to
advance the understanding of police crime.
This study is a quantitative content analysis of archived records consisting of news
articles and court records reporting the arrests of police officers during the years 2005-2011. The
overall purpose of the study is to identify and describe crimes committed by police officers.
Three distinct but related research questions guide this research study. First, what is the
incidence and prevalence of police officers arrested across the United States? Second, how do
law enforcement agencies discipline officers who are arrested? And, third, to what degree do
police crime arrests correlate with other forms of police misconduct?

31
The research questions relate to different aspects relevant to the study of police crime and
police integrity more generally. The crimes committed by police officers that are identified and
described in this study encompass the broad nature of crime in general and range from the
mundane to the most serious, a situation that reflects what Ross (2001) observed as the
multidimensional character of police crime. The wide array of criminal offenses committed by
police officers also obviously varies in terms of the research questions identified above,
including the frequency with which they occur, the frequency and types of discipline imposed,
and the degree to which they are associated with other forms of misconduct.
The study of off-duty police misconduct and crime has been complicated by a debate on
whether these concepts include acts committed while an officer is technically off-duty. Several
policing scholars emphasize the occupational origins of police crime and focus on those acts that
occur on-duty under the guise of police authority (Barker & Carter, 1994; Foster, 1966;
Stoddard, 1968) and during the course of an officer’s normal work activities (Barker, 1978;
Ross, 2001). Kappeler et al. (1998) argue that many off-duty crimes should not be considered
police crimes because they do not involve some aspect of an officer’s occupational position to
carry them out. This situation begs questions as to whether off-duty misconduct arises from
specialized law enforcement training, skills, and knowledge, or even prevailing occupational
norms that may serve to legitimize such behavior.
Fyfe and Kane (2006) make a compelling case for the inclusion of off-duty acts in their
study of career-ending police misconduct, an argument that also applies to police crimes. First,
the job provides officers unique criminal opportunities that can be taken advantage of either onor off-duty. Second, police officers are more likely to engage in either on- or off-duty crimes in
part because they believe their status as sworn law enforcement officers affords them some

32
degree of exemption from prosecution (see Reiss, 1971; Stinson, Liederbach, Brewer, & Todak,
2014). Third, most jurisdictions grant full law enforcement powers to off-duty sworn law
enforcement officers and permit them to carry service weapons. These factors make it difficult
to distinguish between on- and off-duty police behavior because “the knowledge, gun, and badge
that comes with being a police officer” often facilitates the off-duty crimes of police officers
(Mollen Commission, 1994, p. 30). Other studies have raised concerns about the off-duty guninvolved misconduct of sworn officers (e.g., Commission to Combat Police Corruption, 1998a,
2001a, 2001b, 2010; Fyfe, 1980, 1987, 1988; Kane & White, 2009; Stinson, Liederbach, &
Freiburger, 2012).
The current study demands a conceptual framework that reflects both the broad range of
offenses committed by law enforcement officers and the generalized nature of our research
questions. Thus, the contents of our report are organized within a conceptual framework that
incorporates five key types of police crime:


Sex-related Police Crime



Alcohol-related Police Crime



Drug-related Police Crime



Violence-related Police Crime



Profit-motivated Police Crime
The remainder of this report is organized around these five key types. We provide data

pertinent to the three broad research questions within each area, as well as additional data that
further describes the nature and character of police crime both in general and within each of the
five types. The conceptual framework serves to organize data that describes a phenomenon that
encompasses a wide range of criminal behavior. The framework also allows for the presentation

33
and discussion of data specifically focused on answering the stated research questions; but also,
the wealth of data collected on police crime as part of this project that does not readily fit under
one or more of the specific research questions. The latter part of this section introduces the
problems associated with these different types of police crime in sequence. Likewise, the
Literature Citations and Review section of the report presents overviews of the relevant research
literature organized in terms of the five key types of police crime. The Findings portion of this
report also incorporates the same conceptual framework and organizing themes. The final
section of the report discusses our findings and implications of the study both within and across
the key types of police crime.
Statement of the Problem
Scholars have struggled with the task of measuring police integrity because there are
virtually no official nationwide data collected, maintained, disseminated, and/or available for
research analyses (see, e.g., Anechiarico & Jacobs, 1996; Collins, 1998; Kane, 2007; Klockars,
Kutnjak Ivkovic, Harver, & Haberfield, 1997; Kutnjak Ivkovic, 2003; Sherman & Langworthy,
1979; Stinson, Liederbach, & Freiburger, 2010). Complicating the matter is the reality that
police officers are largely exempt from law enforcement because there is a reluctance to enforce
the law against fellow officers (Klockars, Kutnjak Ivkovic, & Haberfield, 2006; Reiss, 1971).
As a result, researchers have resorted to a variety of methodologies in their efforts to learn more
about the nature and extent of police crime, corruption, and misconduct. These include surveys
(e.g., Barker, 1978; Greene, Piquero, Hickman, & Lawton, 2004), quasi-experiments (e.g., Fyfe
& Kane, 2006; Kane & White, 2009, 2013), sociological field studies (e.g., Reiss, 1971;
Sherman, 1978), investigation reports of independent commissions (e.g., Christopher
Commission, 1991; Knapp Commission, 1972; Mollen Commission, 1994; e.g., Pennsylvania

34
Crime Commission, 1974), analyses of internal agency records (e.g., Fyfe & Kane, 2006; C. J.
Harris, 2009, 2011a, 2011b, 2012; Kane & White, 2009, 2013; Liederbach, Boyd, Taylor, &
Kawucha, 2007), and analysis of criminal justice system records (e.g., Collins, 1998; Kutnjak
Ivkovic, 2005).
These various methodologies have thus far failed to produce systematic, nationwide data
on police crime. These lines of literature do make clear, however, that the phenomenon covers a
wide range of criminal behavior that is difficult to generalize. Ross (2001) was among the first
to recognize the multidimensional nature of police crime. He developed a taxonomy that in part
distinguished the phenomenon in terms of four bipolar distinctions of police criminality.
Likewise, Stinson (2009) developed a typology of police crime as a means to both organize data
on the wide range of acts committed by police officers and to distinguish various types of police
crime. Stinson’s typology of police crime is incorporated in the current study. The section
below includes a statement of the problem associated with the five types of police crime
including: (a) sex-related police crime, (b) alcohol-related police crime, (c) drug-related police
crime, (d) violence-related police crime, and (e) profit-motivated police crime.
The Problem of Sex-related Police Crime
Sex-related police crimes encompass a continuum of acts ranging from less serious forms
of sexual misconduct to more egregious cases that involve violence, including acts that have
been referred to in the research literature as police sexual violence (Rabe-Hemp & Braithwaite,
2013). Police sexual violence has been defined to include acts that are officially recognized and
involve violence or the use of police force (Maher, 2003). Police sexual violence occurs when
the victim experiences “sexually degrading, humiliating, violating, damaging or threatening acts
committed by a police officer through the use of force, fear, intimidation, or police authority”

35
(Kraska & Kappeler, 1995, p. 93). Less serious forms of sexual misconduct include acts
whereby a police officer uses his or her position to “initiate or respond to some sexuallymotivated cue for the purpose of sexual gratification” (Maher, 2003, p. 357).
Opportunities for sex-related police crime abound because officers operate in a low
visibility environment with very little supervision. The potential victims of sex-related police
crime include criminal suspects but also unaccompanied victims of crime (Rabe-Hemp &
Braithwaite, 2013). Cases of sex-related police crime often do not involve direct forms of
violence, because police may be able to perpetrate these crimes on the basis of threats or other
forms of intimidation given their position of authority. Police sexual misconduct and cases of
police sexual violence are often referred to as hidden offenses, and studies on police sexual
misconduct are usually based on small samples or derived from officer surveys that are
threatened by a reluctance to reveal these cases. Rabe-Hemp and Braithwaite (2013) recently
identified the need for national level studies on the phenomenon.
The Problem of Alcohol-related Police Crime
A second type of police crime explored in this study is alcohol-related police crime. In
August 1998, the (New York City) Commission to Combat Police Corruption released a
comprehensive report on the misconduct of New York Police Department (NYPD) officers. The
Commission found that a significant number of cases arose out of misconduct that occurred
while the officers were off-duty. A large number of the misconduct cases analyzed by the
Commission to Combat Police Corruption involved off-duty officers who were intoxicated
(Commission to Combat Police Corruption, 2004). The most prevalent charges in these cases
were driving under the influence and cases where officers had consumed enough alcohol to be
considered unfit for duty. The Commission to Combat Police Corruption report indicates the

36
magnitude of alcohol-related problems among NYPD officers and suggests that these problems
primarily involve both the off-duty consumption of alcohol and cases involving police officers
who drive drunk, a phenomenon we refer to as police driving under the influence (DUI).
Following the outline of the Commission’s findings, this report describes the problem of alcoholrelated police crime in two parts: (a) police crimes that involved the consumption of alcohol, and
(b) police crimes that specifically involve cases of police DUI.
One problem for scholars interested in the study of alcohol-related police crime stems
from the fact that much of it—or at least many of the most visible cases of alcohol-related police
crime—seems to occur while police are technically off-duty (Commission to Combat Police
Corruption, 2004). Virtually all of the existing data on off-duty police misconduct and crime
describes the misbehavior of NYPD police officers. The data are based on either the findings of
independent commissions designed to investigate the city's unique cycle of police scandals or
published research derived from a data set on career-ending misconduct among NYPD officers
from 1975 to 1996 (Fyfe & Kane, 2006; Kane & White, 2009). The existing line of research
provides coverage on the off-duty misbehavior of NYPD officers, but the absence of data on the
phenomenon as it occurs elsewhere raises legitimate concerns in regard to generalizability. The
current study provides much needed data on the alcohol-related police crimes perpetrated by offduty police officers as these crimes occur nationwide.
Police DUI—or cases in which police are arrested for crimes associated with driving
while intoxicated—comprises the second part of our description of alcohol-related police crime.
Drunk and/or impaired driving is unquestionably an enormous societal concern (Commission to
Combat Police Corruption, 2009; Shults, Beck, & Dellinger, 2011), and evidence from anecdotes
and journalistic investigations demonstrate that some police drive while impaired themselves

37
(e.g., Castaneda, 2008; Davis, 2008; Fazlollah, 2001, 2003). Aside from anecdotes and the
reports of local journalistic investigations however, there are no systematic data on the problem
of police DUI's. There are no official statistics on the number of officers arrested for DUIrelated offenses, and only one study provides data that describe cases involving police who drive
impaired on a national scale (see Stinson, Liederbach, Brewer, & Todak, 2014). Additional data
are clearly necessary to understand and mitigate the problem.
The Problem of Drug-related Police Crime
A third type of police crime explored in this study is drug-related police crime. Police
scandals during the last two decades of the twentieth century exposed dramatic cases of drugrelated corruption in several major American cities. A report of the United States General
Accounting Office (1998) outlined several different drug-related corruption scandals across the
United States during this period. The most widely-recognized scandals occurred in Miami
(Sechrest & Burns, 1992), Los Angeles (Los Angeles Police Department, 2000), and New York
City (Baer, Jr. & Armao, 1995). The scandals within the NYPD gained particular notoriety
because of both the visibility of the NYPD and the egregious crimes perpetrated by the involved
officers. Investigations into the NYPD scandal discovered widespread drug-related corruption
including police who burglarized drug dens, trafficked in stolen drugs, and robbed drug dealers
and their customers (Baer, Jr. & Armao, 1995).
Twenty years ago the Mollen Commission (1994) highlighted the role of cocaine and
crack markets in the production of drug-related police corruption in New York City. Whereas
previous scandals usually arose within the context of payoffs tied to gambling or prostitution
rackets, the Commission described how the burgeoning narcotics trade had become the source
for more “aggressive, extortionate, and often violent” corruption that “parallel[ed] the violent

38
world of drug trafficking” (Baer, Jr. & Armao, 1995, p. 76). The Mollen Commission revealed
how drug corruption tends to instigate various forms of misconduct and in some cases violent
crimes perpetrated by police, at least as these cases unfolded within the largest police agency in
the nation during the 1980s and 1990s. The patterns identified by the Mollen Commission
suggest the need for more recent data to explore the incidence of drug-related police crime since
the 1990s, and the degree to which drug-related police crime occurs within agencies across the
nation.
The Problem of Violence-related Police Crime
A fourth type of police crime explored in this study is violence-related police crime.
Crimes committed by police officers are by their nature special and deserving of scholarly
attention because the law affords police unique rights and responsibilities, including the legal
authority to use coercive force and access to weapons not available to ordinary citizens. The
police are afforded unique opportunities for misconduct and crime that potentially involve
violence including the excessive and/or illegal use of violence against criminal suspects and
other citizens. This report describes the problem of violence-related police crime in two parts:
(a) police crimes involving violence, and (b) cases in which police are arrested for crimes
associated with domestic and/or family violence.
Police use of physical force is synonymous with police violence. Sherman (1980) defines
police violence as behavior by any officer—acting pursuant to their authority and/or power as a
sworn law enforcement officer—that includes any use of physical force (including, but not
limited to, the application of deadly force), whether justified or unjustified, against any person.
Many acts of police violence are never brought to the attention of law enforcement authorities
and never disclosed to the public.

39
A second aspect of the problem of violence-related police crime involves what is
commonly referred to as officer-involved domestic violence (Blumenstein, Fridell, & Jones,
2012; Gershon, 2000; Johnson, 1991; Johnson, Todd, & Subramanian, 2005; Sgambelluri, 2000;
Stinson & Liederbach, 2013; Wetendorf, 2000). The movement to recognize officer-involved
domestic violence gained momentum through the last two decades; and, incorporates provisions
of the 1994 Violence Against Women Act that defined domestic violence as a national crime
problem and the 1996 enactment of the Lautenberg Amendment to the federal Gun Control Act
(18 U.S.C. § 925) prohibiting individuals—including police officers—from owning or using a
firearm if they have been convicted of a misdemeanor crime of domestic violence (Lonsway &
Harrington, 2003). In 1999, the International Association of Police Chiefs promulgated a model
policy on the handling of officer-involved domestic violence cases and described domestic
violence in police families as a problem that “exists at some serious level and deserves careful
attention” (International Association of Chiefs of Police, 2003, p. 2). These and other initiatives
have worked to establish officer-involved domestic violence as an important issue for police
scholars and executives as well as the general public; but, there is still a lack of empirical data on
the phenomenon.
The Problem of Profit-motivated Police Crime
Profit-motivated police crime involves officers who use their authority of position to
engage in crime for personal gain (see, e.g., Kane & White, 2009, 2013; Stinson et al., 2010;
Stinson, Liederbach, et al., 2012; Stinson, Todak, & Dodge, 2013). The Mollen Commission
(1994) noted that greed is the primary motive behind corruption-based police crime. This is
likely true in the traditional conceptualization of police corruption in the context of, for example,
an officer accepting a bribe as payoff to refrain from law enforcement. It is less clear, however,

40
when an officer while either on- or off-duty sells drugs, shoplifts, commits burglaries or
robberies, or engages in acts of insurance fraud. Fyfe and Kane (2006) recognized this
conceptual problem and—in labeling their reconceptualization as profit-motivated police
misconduct—urged future policing scholars and police administrators to rethink the notion of
police corruption because “police corruption is not as easy to define as we formerly may have
believed” (Fyfe & Kane, 2006, p. xv). Carter (1990a, 1990b) also conceptualized drugmotivated police corruption as characterized by a profit-driven cycle as one of two behavioral
motivations. Stinson (2009) adopted profit-motivated police crime as the fifth type of police
crime and noted that it is consistent with Ross’ (2001) taxonomy of police crime.

41
Literature Citations and Review
Our review of the relevant research literature proceeds from the general to the specific.
First, we cover scholarship relevant to the conceptualization of police crime. These studies
provide definitions for the focus of our research and an identification of the features that
distinguish the phenomenon from the more general topics of police deviance, police misconduct,
or police corruption. This part of the review also outlines studies that describe and specifically
measure the more general forms of police deviance. These studies provide data on behaviors that
sometimes—but not always—involve specific violations of the criminal law by police officers.
The second portion of the review covers research literature more relevant to each of the five key
types of police crime outlined in the preceding section of the report.
The Conceptualization of Police Crime
The study of police crime has thus far been hampered by a degree of conceptual
confusion, mostly due to the tendency of police scholars to consider crimes perpetrated by
officers within studies focused on the more generalized topics of police corruption, deviance, or
misconduct. A quick review identifies the myriad of terms used to define each of these distinct
topics. Wilson (1963) defines police criminality as “illegally using public office for private gain
without the inducement of a bribe, whereas acts of corruption do involve the acceptance of bribes
(p. 190). He further distinguishes police criminality and corruption from brutality, which
includes “mistreating civilians or otherwise infringing their civil liberties” (p. 190). Punch
(2000) distinguishes “crimes committed by criminals in uniform” from acts of police
misconduct, which involve violations of administrative rules that are typically investigated and
sanctioned internally by the police organization (pp. 302–303). Ross (2001) provides a
multidimensional taxonomy of police crimes based in part on whether the act was (a) violent, (b)

42
motivated by profit, and/or (c) perpetrated on behalf of the individual or the organization.
Sherman (1978) focuses on corruption as a form of organizational deviance, and does not
distinguish between police corruption and police crime.
Scholars have tended to consider acts of law violation together with other forms of police
deviance that do not involve specific violations of the criminal law, as well as those whose legal
status as crimes tends to shift over time, such as bribery (see, e.g., Anechiarico & Jacobs, 1996,
pp. 6–7). Fyfe & Kane (2006) point out that the various forms of police deviance are not
mutually exclusive—some forms of corruption and misconduct are police crime and all forms of
police crime constitute misconduct. Crimes, however, that do not involve the misuse of
authority cannot be defined as acts of corruption (Fishman, 1978). The distinction among the
various forms of deviance is that all police crimes involve a violation of criminal statutes and are
subject to criminal prosecution, but not all acts of police corruption or misconduct violate
criminal laws (Wilson, 1963). The conceptual confusion stems largely from the failure to
distinguish specific violations of the criminal law whether or not they constitute an abuse of
authority. The current study focuses on crimes committed by police officers that involved an
arrest on at least one criminal charge to avoid this confusion and focus scholarly attention on
behaviors that have thus far remained invisible to researchers because they have often been
lumped together with more general acts of corruption, deviance, and/or misconduct (Box, 1983;
Jupp, Davies, & Francis, 1999; Kutnjak Ivkovic, 2005).
Most of what we know about police corruption and other forms of police deviance is
from the investigations of independent commissions in the wake of police scandals including the
Knapp Commission (1972) report, the Pennsylvania Crime Commission (1974) report, and the
Mollen Commission (1994) report. These investigations were not designed to investigate police

43
crimes per se, but their findings in regard to corruption and other forms of misconduct shed some
light on the crimes committed by officers. The commissions collectively demonstrated a
problem that went well beyond the usual claim that police deviance is limited to a few “rotten
pockets” of morally deficient officers, but their findings supplied only limited information on the
true nature and extent of crimes committed by officers (Sherman, 1974, p. 7; Skogan & Frydl,
2004).
The Knapp Commission (1972) acknowledged that bribe-taking and petty thefts were
pervasive. The investigation also identified small groups of officers referred to as “meat-eaters”
who “spend a good deal of their working hours aggressively seeking out situations they can
exploit for financial gain, including gambling, narcotics, and other serious offenses” (p. 65).
Twenty years later, the Mollen Commission recognized a shift in the nature of corruption
“primarily characterized by serious criminal activity” closely associated with the drug trade,
including wide-scale drug abuse and trafficking among officers (Mollen Commission, 1994, p.
17). The trend was closely tied to an explosion of crime opportunities provided by open-air
markets for crack cocaine that sprouted during the early 1990s.
Researchers have used police agency records to study officer misconduct that in some
cases includes specific violations of the criminal law. Fyfe and Kane (2006) studied the careerending misconduct of a sample of 1,543 New York City Police Department officers employed
during the period 1975 through 1996 (see also Kane & White, 2009, 2013). They identified
eight separate categories of career-ending misconduct, and found that officers commonly
engaged in several different types of profit-motivated crime including bribe-taking, grand
larceny, insurance fraud, burglary, petit larceny, receiving stolen property and welfare fraud.

44
Officers also engaged in a wide variety of crimes while they were off-duty, including domestic
violence, driving while intoxicated, bar fights, and sexual offenses.
Discussions on the conceptualization of police crime lead naturally to debates on
appropriate responses to the phenomenon including various forms of organizational and courtbased dispositions. There is a limited body of empirical evidence in regard to factors that
influence the imposition of organizational sanctions in cases of career-ending police misconduct
based on the study of a sample of 1,543 NYPD officers employed from 1975 through 1996 (Fyfe
& Kane, 2006; Kane & White, 2009, 2013). The research demonstrated that the NYPD was
more likely to terminate employment in cases of serious misconduct where there was evidence
that an officer violated the criminal law and/or a major administrative policy. The NYPD
commonly used suspension(s) to respond to less serious forms of misconduct. The best
predictors of career-ending police misconduct were disciplinary and reliability problems at
previous jobs, prior criminal involvement, and the mean number of complaints against an
individual officer per year. Overall, the imposition of some form of organizational discipline
was most likely in cases where officers: (a) exhibited serious behavioral problems, and (b)
violated rules that “conflicted with the proper functioning of the organization” (Kane & White,
2013, p. 163).
The existing research on organizational dispositions—based almost exclusively on the
experience of NYPD officers—remains difficult to interpret in regard to generalizability and how
police agencies nationwide respond to cases of police misconduct. The logical source of data on
organizational dispositions are agency records and personnel files; but, these data are not
commonly made available to researchers, the media, or the public. Kane and White (2013)
summarize some more general limitations associated with the use of agency records in the study

45
of organizational responses to police misconduct. These data are in some ways inherently biased
since they largely represent the organizational perspective on the process of discipline. Agency
records also commonly present an incomplete picture of the disciplinary process. For example,
police organizations often suggest resignation as an alternative to termination for officers
involved in serious forms of misconduct, a situation that threatens the validity of data based
exclusively on the final documented organizational disposition of these sorts of cases. Problems
associated with generalizability and an overall dearth of data are exacerbated in regard to the
issue of legal rather than organizational dispositions. We are aware of no existing studies
focused on the legal disposition of police crime cases in terms of the imposition of adverse
employment outcomes aside from those derived from data collected as part of the current project
(e.g., Stinson, Liederbach, et al., 2013, 2012; Stinson, Brewer, Mathna, Liederbach, &
Englebrecht, 2014; Stinson & Liederbach, 2013; Stinson, Liederbach, Brewer, & Mathna, 2014;
Stinson, Liederbach, Brewer, & Todak, 2014; Stinson et al., 2010; Stinson, Todak, & Dodge,
2015; Stinson, Reyns, & Liederbach, 2012; Stinson & Watkins, 2014). One primary goal of this
project is to describe and analyze the organizational and legal disposition of cases of police
crime, including officers arrested for crimes that related to sex, alcohol, drugs, violence, and/or
were profit-motivated.
In Garrity v. New Jersey (1967) the United States Supreme Court discussed the
difference between adverse employment actions in the nature of disciplinary investigations
against officers for police misconduct and initiation of criminal proceedings against a police
officer for the same underlying event. The Court held that a police officer is required to answer
questions truthfully in internal disciplinary investigations where the officer would be subject to
termination for failing to answer the questions, but that statements made in that context by a

46
police officer cannot then be used in any criminal proceeding against the officer because the
statements were coerced. As a result, many investigations into police misconduct that potentially
involve police crime are stymied because a decision must be made by police administrators
whether to (a) elect to gather information by questioning an officer in a disciplinary matter, or
(b) forego any questions of an officer administratively and proceed with a criminal investigation
against the officer. If the second option is elected, then the officer would be entitled to the right
against self-incrimination that is afforded to any suspect or defendant in a criminal investigation
pursuant to the Court’s holding in Miranda v. Arizona (1966). In some instances, law
enforcement agencies simply allow an officer to resign in lieu of initiation of criminal
proceedings against a sworn officer as an effort by the agency to quietly resolve a matter without
public disclosure of police misconduct.
Review on Sex-related Police Crime
The first type of police crime explored in this study is sex-related police crime.
Scholarship on the topic has been comparatively sparse. Early studies focused broadly on
behaviors that constituted police sexual misconduct and acts such as on-duty consensual sex
between officers and female adult citizens. Barker (1978) surveyed police officers who indicated
that consensual sex on-duty was quite prevalent, especially in patrol cruisers. Sapp (1994)
provided data derived from in-depth interviews of police. The study included qualitative—
sometimes lurid—descriptions of seven different forms of police sexual misconduct, including
cases wherein police spent entire shifts seeking opportunities to view unsuspecting females
partially clad or nude, the sexual harassment of crime victims and criminal suspects, and sexual
contacts between officers and underage females. Some of the behaviors described within the

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Sapp study clearly involved sexual coercion rather than consensual sex between police and
willing citizens.
Kraska and Kappeler's (1995) exploratory study on police sexual violence incorporates a
wide continuum of behaviors that range from comparatively unobtrusive forms of sex-related
misconduct (e.g. voyeurism and other invasions of privacy) to obtrusive forms of sexual violence
(e.g. sexual assault and rape). Kraska and Kappeler study the phenomenon within the context of
existing police scholarship and scholarship derived from the feminist literature that more clearly
defines police sexual violence as a form of gender bias and the systematic differential treatment
of females in the criminal justice system. They identified 124 cases of police sexual violence
through both published news reports and federal lawsuits arising under 42 U.S.C. §1983
involving police accused of sexual misconduct. Close to one-third of the cases (30%) identified
involved rape and/or sexual assault. More than one-half of the cases involved strip searches.
Published news reports tended to identify more serious forms of police sexual violence, while
cases that involved strip searches and less serious acts were more likely to be identified through
the federal lawsuit data. Cases of police sexual violence were widely dispersed geographically,
and Kraska and Kappeler (1995) suggest that the cases identified in their study were likely the
“tip of the iceberg” (p. 97). They indicated that the organizational and occupational culture of
policing provides officers ample opportunity to engage in sex-related misconduct and crime, and
note the obstacles to reporting these forms of misconduct confronted by victims including the
fear of retaliation and forms of secondary victimization similar to that experienced more
generally by victims of sexual assaults (see, e.g., LaFree, 1989).
More recently, Maher provided data on police sexual misconduct derived from surveys of
both officers (Maher, 2003) and police chiefs (Maher, 2008). Surveys of officers demonstrate

48
that they believe less serious forms of sexual misconduct occur frequently, and are facilitated by
the opportunity structure provided by the job. The majority of officers indicated that they had
not been pressured to engage in acts of sexual misconduct, but, they were unlikely to report less
serious forms of the phenomenon. Surveyed police chiefs believed that less serious forms of
sexual misconduct were common and serious forms of sexual misconduct and crime were rare.
The data suggest that most police agencies do not have any written policies that expressly
prohibit sexual misconduct (Maher, 2008). Walker and Irlbeck (2002) describe cases that they
refer to as "driving while female," wherein police initiate bogus traffic stops to harass,
intimidate, and/or sexually assault female motorists.
Rabe-Hemp and Braithwaite (2013) recently published a study focused on police sexual
violence and the problem of officer shuffle, wherein police involved in various forms of sexual
misconduct and crime escape punishment and maintain their law enforcement career through
employment with another police agency. Data were derived through a content analysis of
published newspaper accounts of police sexual violence from 1996-2000. They identified 106
cases of police sexual violence. Close to one-half (41.5%) of the cases involved repeat police
perpetrators. Repeat offenders were more likely than first time offenders to victimize juveniles.
Review on Alcohol-related Police Crime
The second type of police crime covered in this study is alcohol-related police crime.
This report describes the problem of alcohol-related police crime in terms of both the off-duty
consumption of alcohol and the phenomenon of police DUI. As we previously described,
virtually all of the existing empirical data on off-duty police misconduct describes the
misbehavior of NYPD officers primarily through the reports of the Commission to Combat
Police Corruption (1998b, 2004). The Commission recommended specific policies designed to

49
mitigate problems associated with off-duty alcohol abuse, including provisions to expand the
definition of unfit for duty to include intoxicated off-duty officers and prohibitions against
carrying an off-duty weapon while intoxicated (Commission to Combat Police Corruption,
1998b). Fyfe and Kane's (2006) classification of police misconduct includes driving while
intoxicated within the category of off-duty public order crimes, but they do not provide specific
data that distinguishes misconduct that includes the abuse of alcohol. Kane and White (2009)
provide several descriptions of cases that involved intoxicated off-duty officers engaged in bar
fights, drunk driving, and personal disputes.
Despite the findings of Kane and White (2009), the lack of existing scholarship on
alcohol-related police misconduct and crime is even more acute in the specific case of police
DUI. Reviews of the empirical literature failed to uncover any scholarly research specifically
focused on the phenomenon of police DUI other than the research by Stinson, Liederbach,
Brewer, and Todak (2014) using data from the current study on police crime. Some recent
evidence indicates that officers and agencies often minimize or ignore the problem of impaired
driving among police. A Bureau of Justice Statistics (BJS) survey reported that over one-third of
responding state and local police agencies would consider police applicants with a prior DUI
conviction (Reaves, 2012). Additionally, a majority of officers responding to a survey on police
integrity said they would not report a fellow officer who had a minor traffic accident while
driving under the influence of alcohol (Klockars, Kutnjak Ivkovic, Harver, & Haberfield, 2000).
Recent journalistic investigations in Milwaukee (Barton, 2011), Denver (McGhee, 2011), and
New York City (Paddock & Lesser, 2010) reported disturbing cases in which police found to
have driven drunk were either not arrested and/or otherwise minimally punished.

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Scholars have long recognized the existence of alcohol-related problems in policing and
have identified factors that seem to promote excessive drinking among police (Dietrich & Smith,
1986; Dishlacoff, 1976; Hurrell, Jr. et al., 1984; Violanti, Marshall, & Howe, 1985). Violanti et
al. (2011) group these factors in terms of (a) officer demographics, (b) stress, and (c) police
culture. Excessive alcohol consumption is certainly due at least in part to demographics and the
over-representation of young males among police officers, in particular patrol officers. Men are
more likely to have problems with alcohol than women, and alcohol use disorders are most
prevalent among 18-24 year-olds (National Institute on Alcohol Abuse and Alcoholism, 2008).
Age and gender have also been specifically correlated with drinking and driving. Alcohol
dependence at the age of 21 has been found to significantly predict persistent driving while
drinking episodes (Begg, Langley, & Stephenson, 2003) and young adult males have been found
to be significantly more likely to engage in driving while drinking than other cohorts (Chou et
al., 2006).
There is an extensive line of research on how stress may influence excessive drinking
among police. Abdollahi (2002) provides a comprehensive overview of this literature in terms of
factors that include (a) intra-interpersonal stressors, (b) job-related stressors, and (c)
organizational stressors. Violanti et al.'s (2011) survey of police found that stress derived from
failed interpersonal relationships increased the likelihood of hazardous drinking behavior,
especially among male officers. A number of studies attribute excess alcohol consumption to the
failure to properly cope with stress that is related more specifically to the job, including those
derived from a perceived lack of organizational support and problematic encounters with citizens
(Anshel, 2000; Ayres, Flanagan, & Ayres, 1992; Kohan & O’Connor, 2002). Leino et al. (2011)
recently explored how job-related exposure to violence and the absence of adequate debriefing

51
procedures may increase levels of drinking among police. Swatt, Gibson, and Piquero (2007)
utilize Agnew's general strain theory to explain how anxiety/depression mediate the relationship
between work-related strain and drinking prevalence among police officers.
Studies focused on the drinking behavior of police also underscore the impact of police
culture. Police culture has been referred to as essentially a drinking culture that often includes
frequent social interactions that involve the consumption of alcohol (Violanti et al., 1985).
Various occupational factors have been found to increase the risk of alcohol and drug problems
(Fennell, Rodin, & Kantor, 1981; Hingson, Mangione, & Barrett, 1981). Indeed, Macdonald,
Wells, and Wild (1999) specifically found that the existence of a drinking subculture at work was
associated with the development of alcohol problems. Lindsey and Shelley (2009) found that
police officers most at risk for drinking problems admitted that fitting in was the primary reason
they engaged in alcohol consumption. Officers may also be discouraged from reporting problem
drinking or drunk driving among fellow officers in order to uphold other widely-recognized
values shared among police including secrecy and solidarity (Banton, 1964; Skolnick, 1994).
Review on Drug-related Police Crime
The third type of police crime covered in this study is drug-related police crime. This
line of research covers three relevant issues including (a) the etiology of drug-related
misconduct, (b) the classification of drug-related misconduct, and (c) the prevalence of drug use
by police. Police scholars typically attribute drug-related corruption to factors associated with
the organization of police work and the occupational culture of police. Stoddard (1968)
emphasizes the role of police culture and the nature of police work in the causation of what he
referred to as blue coat crime. Kraska and Kappeler (1988) underscore the prevalence of on-thejob opportunities for patrol officers to become involved in the drug trade in some fashion,

52
including lack of direct supervision, drug availability, and exposure to drug users and dealers.
The report of the General Accounting Office (1998) highlights the influence of deviant police
subcultures that both promote drug corruption and protect police who adhere to deviant
subcultural norms such as secrecy, loyalty, and cynicism about police work and the criminal
justice system. Carter and Stephens (1994) view substance abuse as primarily a “job-related
condition” among police, particularly those working undercover vice in drug-infested beats (p.
107).
Carter (1990b) provides the most often cited classification scheme for drug-related
corruption. His typology identifies two forms of drug corruption. Type 1 or traditionallyconceptualized drug corruption involves officers motivated by illegitimate goals including
personal profit. This form of drug corruption includes the extortion and robbery of drug dealers
and the acceptance of bribes to protect them. Type 2 drug corruption is comprised of officers
motivated by organizationally-derived legitimate goals ostensibly tied to the arrest and
conviction of dealers and users. This form of drug corruption includes perjury, violations of
criminal procedure, and the planting of criminal evidence. Aside from its utility as an
organization scheme, Carter's typology demonstrates how the drug trade tends to give rise to
many different forms of police crime. The drug trade provides opportunities for personal gain
through payoffs, shakedowns, robberies, and opportunistic thefts, as well as types of misconduct
tied to drug enforcement goals and violations of the rights of criminal defendants including
perjury and the planting of evidence. Officers who are recreational drug users expose
themselves to street-level dealers and associated manipulation and coercion (Kappeler et al.,
1998). Carter's (1990b) conclusions about the impact of these markets on the nature of police
corruption closely mirror those of the Mollen Commission (1994) by noting that “the nature of

53
corruption has changed, particularly with respect to police drug users, the emergence of crack
houses as easy targets, and the frustration with drug-law enforcement associated with the
extraordinarily high volume of drug traffic” (Carter, 1990b, pp. 96–97).
Very little is known about the prevalence of drug use among police officers
(Mieczkowski, 2002). Kraska and Kappeler (1988) provide what is widely-cited as the only
empirical description of on-duty drug use by police through their study of one medium-sized
police agency. They found that 20% of the officers used marijuana on duty at least two times per
month, and that 10% had used other non-prescribed controlled substances while on duty,
including hallucinogens, stimulants, and/or barbiturates. The study provides initial evidence to
suggest that the problem of on-duty drug use by police is not limited to large urban departments,
although data derived from a much larger sample of agencies is needed to assess the prevalence
of drug use among police across various types of jurisdictions.
Official drug tests provide another source of data on the degree to which police use drugs,
but most of this evidence describes testing results from a single agency, and no entity collects
comprehensive data on the number of police who test positive overall. One journalistic
investigation reported a 1.1% failure rate (75 officers) among Boston Police Department police
tested from 1999-2006, as well as the failure of 14 Los Angeles Police Department officers drug
tested from 2000-2006. Over 81% of the positive tests in Boston involved the use of cocaine,
and police executives indicated that they believed cocaine had surpassed marijuana as the drug of
choice among police (Smalley, 2006). More recent data derived from self-report surveys of
officers in a single agency in Australia suggest that police use a wider variety of drugs including
marijuana, amphetamines, cocaine, ecstasy, heroine, ketamine, and non-prescribed steroids
(Gorta, 2009).

54
Lersch and Mieczkowski (2005a) report results of drug tests conducted in a large police
agency in the Eastern United States in which very few officers tested positive (~ .005%).
Scholars often point out, however, that official drug tests are likely influenced by (a)
“announcement effects” and the fact that officers employed by these agencies are aware of
testing protocols (Lersch & Mieczkowski, 2005a, p. 292), and (b) the method of drug testing.
Cocaine is the most frequently detected drug based on hair analysis, and marijuana is the most
frequently detected drug based on urinalysis (Lersch & Mieczkowski, 2005a; Mieczkowski,
2002; Mieczkowski & Lersch, 2002).
Review on Violence-related Police Crime
The fourth type of police crime explored in this study is violence-related police crime.
According to Sherman (1980), police use of physical force is synonymous with police violence,
defining police violence as behavior by any police officer—acting pursuant to their authority
and/or power as a sworn law enforcement officer—that includes any use of physical force
(including, but not limited to, the application of deadly force), whether justified or unjustified,
against any person. Based on Sherman’s definition, explanations for police violence in the
existing literature have been quite varied (e.g., Alpert & MacDonald, 2001; Garner, Maxwell, &
Heraux, 2002; Griffin & Bernard, 2003; Lersch & Mieczkowski, 2005b; Manzoni & Eisner,
2006; Terrill & Mastrofski, 2002; Terrill, Paoline, & Manning, 2003; Terrill & Reisig, 2003).
Stinson, Reyns, and Liederbach (2012) use data from the current study to identify and describe
cases that involve the criminal misuse of conductive energy devices—purportedly a less-thanlethal alternative to firearms—so studies that describe the factors that influence police use of
force more generally during citizen encounters seem particularly relevant.

55
Police scholars have examined the factors that influence the use of force more generally
since the 1960s, and quantitative studies have focused on the relationship between police force
and a wide range of predictors including situational, individual, organizational, and communitylevel variables. Overall, virtually all studies that compare situational factors to others such as
officer, organizational, and community-level factors have found that situational factors exert the
most powerful influence on the decision to use coercive force (Skogan & Frydl, 2004). Police
are more likely to resort to violence in encounters that include physically aggressive suspects and
citizens who resist officer attempts to control the situation. Researchers have most often
investigated the influence of situational factors in cases that involve the use of deadly force by
police (e.g., Alpert & Smith, 1999; Binder & Scharf, 1982; Blumberg, 1983; Fyfe, 1981). This
line of research has primarily emphasized the direct relationship between the level of situational
risk faced by an officer and the specific decision to employ deadly force. Situational risk refers
to the immediate scenario within which police must decide to shoot or not shoot. Did the suspect
assault the police? Was the suspect armed? Did the suspect shoot at police? These situational
factors appear to explain the use of deadly force more directly than other variables. Terrell's
(2003) research based on observational data suggests that situational factors are also the primary
determinants of the use of non-deadly force by police. He examined the relationship between
five levels of suspect resistance (none, passive, verbal, defensive, and active), and four levels of
non-lethal force (none, verbal, restraint, and impact) and found that force levels were
significantly related to levels of suspect resistance.
The review to this point has outlined prior research that examines the general use of
police force and its predictors, whether situational, individual, organizational, and/or communitylevel factors. Distinctions as to whether the use of police force in a particular case will be

56
defined as “justified” or “excessive” or perhaps even “criminal” are subjective and are made
case-by-case based largely on the discretionary decisions of actors within the criminal justice
system, including police and prosecutors. Most states provide guidelines for the police use of
force in existing criminal codes, and local prosecutors may choose to prosecute police who
engage in behavior that violates the criminal law. The level of force used to complete an arrest
or otherwise protect public safety must be commensurate with the crime committed and
proportionally related to its necessity (Cheh, 1996; Klockars, 1996). Police officers who use
“excessive” levels of force may be criminally prosecuted for a range of offenses including
homicide, manslaughter, and assault. Comprehensive statistics on the criminal prosecution of
police are generally neither maintained nor available to scholars or the public, so an empiricallybased identification of the specific factors that distinguishes cases on the basis of whether force
was “justified” or “criminal” is not currently possible. One primary goal of the this project is to
utilize media-based data in order to identify and describe cases of violence-related police crime
that are distinguished by the fact that they involved the criminal arrest of a police officer(s) and
were thus defined by the system as “criminal,” at least prior to the adjudication phase of the case.
This type of police crime also incorporate cases that involve police arrested for crimes
associated with domestic and/or family violence, a phenomenon increasingly referred to in the
research literature as officer-involved domestic violence. There have been very few empirical
studies designed to estimate the prevalence of officer-involved domestic violence—all of them
based on self-administered surveys of police and/or their spouses. Johnson (1991) reported that
40% of responding officers admitted that they had behaved violently toward their spouse at least
once during the previous six months, and 20% of the spouses in a concurrent survey reported that
their spouse had abused either them or their children in the previous six months. Neidig, Russell,

57
and Seng (1992) and Neidig, Seng, and Russell (1992) reported that 41% of responding male
officers admitted that at least one incident of physical aggression occurred in their marital
relationship during the previous year, and 8% of those reported the occurrence of “severe”
physical aggression including choking, strangling, and/or the use or threatened use of a knife or
gun (Neidig, Russell, et al., 1992, p. 32). Reported rates of officer-involved domestic violence
are highest among officers who are currently divorced or separated from their spouses.
The literature on officer-involved domestic violence is filled with anecdotes that
underscore the occupationally-derived etiology of violence within police families. Factors
associated with police culture and the job that have been discussed within the context of officerinvolved domestic violence include: (a) violence exposure, (b) authoritarianism, and (c) problem
drinking. Researchers suspect higher rates of domestic violence among police most exposed to
work-related violence based on studies that document a relationship between violence exposure
and the personal well-being of police officers; and, the research literature on the relationship
between work and family and the possibility of spillover effects (Johnson et al., 2005; Mullins &
McMains, 2000). Officer surveys identify violence exposure as one of the most significant
work-related stressors for police (Gershon, 2000), and studies based on clinical trials describe
links between police stress and poor family functioning (Neidig, Russell, et al., 1992). Police
training in authoritarian styles and the regular exercise of coercive force may influence marital
interactions and promote domestic and/or family violence (Greene-Forsythe, 2000; Johnson,
1991; Johnson et al., 2005; Sgambelluri, 2000).
Police are trained to exert power and use coercive force to accomplish their goals and
gain compliance from citizens (Bittner, 1978; Skolnick, 1994). The regular exercise of coercion
and authoritarianism may spillover to the home in cases where police treat family members as

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criminal suspects. Johnson (1991) identified a link between authoritarian spillover and high
levels of strain in police families; and, Johnson et al.'s (2005) survey of police indicates that
authoritarian spillover mediates the relationship between exposure to violence on the job and
domestic violence among police. Problem drinking is another aspect of the work-family
spillover that may contribute to the incidence of violence in police families. The research that
associates officer-involved domestic violence with problem drinking cite many of the same
occupationally-derived factors covered in the previous section on alcohol-related police crime
including (a) intra-interpersonal stressors, (b) job-related stressors, and (c) organizational
stressors.
Much of the remaining research literature on officer-involved domestic violence focuses
on the unique vulnerabilities of domestic violence victims in police families and factors that
conspire to discourage exposure of these crimes and contribute to the hidden nature of the
problem. While scholars have long demonstrated that victims of domestic and/or family
violence are often reluctant to officially report these crimes, the fact that perpetrators of officerinvolved domestic violence are police themselves creates particular problems and barriers to
reporting. First, they must consider the fact that perpetrators possess and are trained to use lethal
weapons. Second, police perpetrators are likely to know the location of domestic violence
shelters and have professional experience that can be used to manipulate the system and shift
blame to the victim (Gershon, 2000; National Center for Women & Policing, 2005). Third,
decisions to report incidents of officer-involved domestic violence are also likely influenced by
the cultural values of secrecy and loyalty, wherein police (and their families) are expected to
“never blow the whistle” and expose police perpetrators (Johnson et al., 2005; Shernock, 1995,
p. 623). Fourth, domestic violence victims in police families also may be concerned about

59
consequences associated with provisions of the Lautenberg Amendment (1996) that prohibit
police from owning or using a gun if they are convicted of a misdemeanor crime of domestic
violence.
Review on Profit-motivated Police Crime
The Mollen Commission (1994) found that greed is the primary motive behind police
crime that constitutes corruption. This is true if police corruption is conceptualized in the
traditional context of acts such as accepting bribes to refrain from law enforcement. It is,
however, “less clear whether officers who perform robberies or burglaries, shoplift, sell drugs, or
engage in welfare or insurance fraud during their off-duty time are engaging in a variety of
police corruption” (Fyfe & Kane, 2006, p. xv). Fyfe and Kane (2006) reconceptualized these
actions as “profit-motivated misconduct” and noted that “police corruption is not as easy to
define as we formerly may have believed (p. xv). Similarly, Carter (1990b, pp. 89–90)
conceptualized police corruption as being characterized by a “profit-driven cycle.” The
classification of some police crime as profit-motivated is also found in Ross’ (2001) taxonomy of
police crime, where the second of four dichotomous distinctions is between “economicallymotivated and noneconomically-motivated police behavior” (p. 184).
Using the same data set as Fyfe and Kane (2006), Kane and White (2013) explored the
nature of the career-ending police misconduct in the NYPD and found that the profit-motivated
cases (N = 387) included bribe-taking (18.6%), grand larceny (17.1%), insurance fraud (8.6%),
burglary (7.3%), petit larceny (7.3%), receiving stolen property (3.9%), government fraud
(3.1%), gratuities (2.6%), gambling (1.8%), illegal operation (1.8%), and other profit-motivated
misconduct (27.4%) such as extortion, robbery, abusing official resources (p. 73, Table 4.2).

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Stinson and colleagues found that profit-motivated police crime is a quantifiable variable
that helps explains the nature of police crime across the life course of officers’ law enforcement
careers in a variety of contexts at nonfederal law enforcement agencies across the United States.
Whereas less experienced police officers are more likely to commit violent crimes, those officers
who commit profit-motivated crimes are more likely to be experienced officers late in their
policing career and more likely to be supervisors and/or administrators (Stinson et al., 2010).
Likewise, crime by policewomen is most often profit-motivated (Stinson, Todak, et al., 2013).
Crime by school resource officers typically is not profit-motivated police crime (Stinson &
Watkins, 2014). Profit-motivated police crimes are more likely to be committed while on-duty
and less likely to be committed while an officer is off-duty (Stinson, Liederbach, et al., 2012).
Statement of Rationale for the Research
The purpose of the current research project is to promote police integrity by gaining a
better understanding of police crime and agency responses to officer arrests. The study is
providing a wealth of data on a phenomena that relates directly to police integrity—data that
police executives did not previously have access to because they did not exist in any useable
format.
The first goal of this research is to determine the nature and extent of police crime in the
United States. Objectives for this goal are to: (a) identify and describe the incidence of officers
arrested during each year in the study period; and (b) identify and describe the prevalence of
officers arrested over the course of the study period.
A second goal of this research is to determine what factors influence how a police
organization responds to arrests of officers. Objectives for this goal are to: (a) identify, describe,
and analyze the severity of crimes for which police officers are arrested and determine whether

61
severity of crime influences employment outcomes; (b) identify and analyze the level of
urbanization for each employing police organization by using a 9-level urban-to-rural continuum
scale to determine whether an agency’s degree of urbanization or rurality influences agency
response and employment outcomes; (c) identify and analyze the geographic location (region,
division, and state) for each employing police organization to determine whether geographic
disparity influences agency response and employment outcomes; (d) identify and analyze length
of service and age of arrested officers to further investigate whether age in years and/or years of
service influence agency response and employment outcomes; and (e) identify and analyze
criminal case outcomes for each officer arrested to determine whether this factor influences
agency response and employment outcomes.
A third and final goal of the research is to foster police integrity by exploring whether
police crime and officer arrests correlate with other forms of police misconduct. Objectives for
this goal are to: (a) identify and analyze whether the arrested officers in our database were also
named individually as a defendant in any federal court civil actions pursuant to 42 U.S.C. §1983
during the life course of their law enforcement careers; and (b) inform police practitioners and
policymakers of strategies derived from the project’s findings that could improve early
intervention systems and other internal controls to better identify problem officers and those at
risk for engaging in police misconduct – including engaging in police crime creating potential
agency exposure to civil liability – and its correlates.
The rationale for the current study evolved from long-standing obstacles associated with
the collection of data on police crime. The underlying reasons for the current study reflect the
ongoing lack of empirical data on the crimes committed by police—a situation that relates
directly to the limitations of previous methodologies used to collect data on the phenomenon.

62
Researchers have resorted to a variety of methodologies to learn more about the nature and
extent of police misconduct, corruption, and/or crime; but, empirical scholarship has usually
incorporated one or more of the following: (a) officer surveys, (b) agency records, and (c)
sociological field studies. Studies that utilize one or more of these methodologies have clearly
contributed to the knowledge base on police crime, but the methodology used in the present
study offers clear advantages in terms of both the scope and quality of potential data on police
crime. The first part of this section provides a brief overview of those limitations by method.
This overview is followed by a concise description of the advantages of our methodology. These
advantages provide the basis of and rationale for our research on police crime.
Limitations of Surveys, Agency Records, and Field Research
Surveys have been perhaps the most commonly utilized method to collect data on the
closely related problems of police corruption and police misconduct. Since the phenomena of
police crime, police corruption, and police misconduct are analogous and sometimes
overlapping, some survey research does provide limited insight into the measurement of police
crime. There are two primary limitations associated with the utilization of surveys in regard to
the study of police crime, corruption, and misconduct. The first problem relates to gaining
access to police organizations to collect data on these issues. Researchers have long reported
problems in gaining access and maintaining access to law enforcement agencies for research
purposes (see, e.g., Kutnjak Ivkovic, 2005; Lundman & Fox, 1978, 1979), so researchers have
been afforded comparatively few opportunities to openly ask police officers about their
perceptions of or tolerance for various forms of misconduct—let alone their perpetration of
actual criminal behavior (Barker, 1978; Kutnjak Ivkovic, 2003). In fact, none of the survey
research reported in the literature directly measures police crime. Rather, the studies measure

63
correlates of police crime and tend to indicate the level of tolerance for such misconduct within
some police agencies (see, e.g., Fishman, 1978). The second limitation of surveys in regard to
the study of police crime involves validity and problems associated with the propensity to
provide socially desirable responses to inquiries about misconduct, wrongdoing, and/or criminal
behavior. There are some indications that police administrators and other police personnel may
be less than truthful in responding to surveys by exaggerating (Maguire & Uchida, 2000). There
are obvious problems associated with asking police officers to complete questionnaires with
items on the nature and extent of police offending, because it is widely assumed that officers will
maintain a code of silence and fail to disclose the nature and extent of police crime through a
survey format (Fishman, 1978).
Researchers have also utilized agency and other official records to study issues associated
with police misconduct, corruption, and crime. This line of research includes numerous crosssectional studies based on small single agency samples of police officers. More recently, Fyfe
and Kane (2006) provide longitudinal data on the misconduct and crime of police based on
official agency records derived from the NYPD. The NYPD data compared the personal and
career histories of all 1,543 NYPD officers who were involuntarily separated from the NYPD for
cause from 1975 to 1996 with a random sample of their police academy classmates. The study
methodology offers unique opportunities to describe and measure police misconduct and crime
from single agencies over comparatively long time periods. Studies based on agency records
have two primary limitations. First, similar to officer surveys, these data require difficult to
obtain access to a police organization. Most police agencies are obviously reluctant to disclose
official records of officer misconduct and crime to researchers. As a result, studies based on
agency records almost invariably describe the misbehavior and crime of officers from a single

64
police agency, a situation that leads to issues in regard to the generalizability of studies based on
data from a single police agency. A second limitation of using agency records is that they result
from a filtering process within the organization that created the records. That is to say, agency
records are the version of events from the perspective of the organization.
Reiss' (1971) classic research on the behavior of police officers in Chicago, Boston, and
Washington DC stands as the only published study that utilized field observations to specifically
describe various forms of police misconduct and crime. Reiss found that roughly 20% of
observed police engaged in on-duty crime, not including assaults and syndicated crime. Most of
the observed police crime was limited to thefts. Large scale observational studies of police are
costly and difficult to conduct, so only a small number have been completed (Bayley &
Garofalo, 1989; see, .e.g., Black & Reiss, 1967; Frank, Novak, & Smith, 2001; Liederbach,
2005; Liederbach & Frank, 2003; Mastrofski et al., 1998). Small scale observation studies are
nonsystematic and often involve ride-along observations in one single agency. Field
observations are also limited by the danger of reactivity, wherein officers may alter their
behavior because of the presence of a civilian observer. Reactivity poses clear problems in the
collection of observational data on police misconduct, corruption, and/or crime.

65
METHODS
Data for the current study were collected as part of a project commenced in the year 2004
designed to locate cases in which sworn law enforcement officers had been arrested for any type
of criminal offense(s). Data were derived from published news articles using the Google News
search engine and its Google Alerts email update service. Google Alerts searches were
conducted using the same 48 search terms developed by Stinson (2009). The Google Alerts
email update service sent a message each time one of the automated daily searches identified a
news article in the Google News search engine that matched any of the designated search terms.
The automated alerts contained a link to the URL for the news articles. The articles were
located, examined for relevancy, printed, logged, and then scanned, indexed, and archived in a
digital imaging database for subsequent coding and content analyses. The present study focuses
on the identification and description of the cases in which police officers were arrested during
the years 2005-2011.
Coding and Content Analysis
Content analyses were conducted in order to code the cases in terms of (a) arrested
officer, (b) employing nonfederal law enforcement agency, (c) each of the charged criminal
offenses, (d) victim characteristics, (e) organizational adverse employment outcomes, and (f)
criminal case dispositions. Each of the charged criminal offenses was coded using the data
collection guidelines of the National Incident-Based Reporting System (NIBRS) as the coding
protocol for each criminal offense category (see U.S. Department of Justice, 2000). Fifty-seven
criminal offenses are included in the NIBRS, consisting of 46 incident-based criminal offenses in
one of 22 crime categories as well as 11 additional arrest-based minor criminal offense
categories. In each case every offense charged was recorded on the coding instrument as well as

66
the most serious offense charged in each police crime arrest case. The most serious offense
charged was determined using the Uniform Crime Report’s (UCR) crime seriousness hierarchy
(see U.S. Department of Justice, 2004). An additional eight offenses were added following an
earlier pilot study (see Stinson, 2009) because police officers who were arrested often were
charged with criminal offenses not included in the NIBRS (e.g., online solicitation of a child,
indecent exposure, official misconduct / official oppression / violation of oath, vehicular hit-andrun, perjury / false reports / false statements, criminal deprivation of civil rights).
The primary unit of analysis in this study is criminal arrest case. One of the primary
issues in coding was differentiating between arrest cases with multiple victims and officers who
were arrested on multiple occasions within the study years 2005-2011. The remainder of this
paragraph presents hypothetical situations to demonstrate the unit of analysis in this study.
Assume, for example, that an officer was arrested for assaulting his wife. That is coded as one
arrest case (arrest case #1). If the same officer was again arrested a week later for violating an
order of protection (arrest case #2) that was issued by a court judge following the officer’s first
arrest, the second arrest was treated as a separate case in this study. If that same officer was
arrested a few months later for drunk driving, that too was recorded as a new arrest case (arrest
case #3). The officer was suspended from his employment immediately following his arrest for
DUI. For the purposes of this hypothetical, assume that the same officer was acquitted at trial in
all three of those arrest cases (that is, arrest case #1, arrest case #2, and arrest case #3) and
returned to duty as a police officer. Two years later, let’s assume that the same officer was
arrested for sex crimes involving a 14 year-old victim (arrest case #4) and 15 year-old victim
(arrest case #5). Further assume that the officer was convicted in the case involving the 14 yearold victim (arrest case #4), and the charges were dismissed by the prosecutor in the criminal case

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involving the 15 year-old victim (arrest case #5). Following the officer’s conviction (in arrest
case #4), the officer was fired from the police department. By coding each arrest case separately,
the criminal case dispositions in each case as well as the adverse employment actions attached to
each arrest case can be documented for analysis.
Cases were also coded on Stinson’s (2009) typology of police crime, which posits that
most crime committed by police officers is alcohol-related, drug-related, sex-related, violencerelated, and/or profit-motivated.1 The types of police crime are not mutually-exclusive
categories. Rather, each type of police crime is coded as a dichotomous variable because crimes
committed by officers often involve more than one type of police crime. In a case where an
officer was arrested and charged with the forcible rape of a female motorist during a traffic stop,
for example, the case would be coded in this study as both sex-related and violence-related.
Additionally, cases were coded for the presence of police sexual violence and/or driving while
female encounters. Police sexual violence is operationalized as “those situations in which a
female citizen experiences a sexually degrading, humiliating, violating, damaging, or threatening
act committed by a police officer through the use of force or police authority” (Kraska &
Kappeler, 1995, p. 93). Driving while female is operationalized as instances where a police
officer stops a female driver under the pretext of an alleged traffic violation and then abuses the
power and authority of his position to take advantage of a vulnerable motorist (Walker & Irlbeck,
2002, 2003). Walker and Irlbeck (2002) conceptualized the problem of driving while female as a
parallel to the problem of racial profiling of African-American motorists often referred to as
driving-while-black (cf. D. A. Harris, 1997, 1999). In the context of driving while female
encounters, an officer typically asks for sexual favors in exchange for dropped charges or in lieu

A sixth type of police crime not yet explored is revenge-motivated police crime.

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of being taken to jail in a forced quid pro quo. In some cases, driving while female encounters
escalate into sexual harassment, sexual assault, and in rare instances, forcible rape.
Cases were coded on numerous variables relating to police DUI arrest cases (see Stinson
et al., 2010), off-duty police crimes (see Stinson, Liederbach, et al., 2012), drug-related police
crimes and drugs of abuse (see Stinson, Liederbach, et al., 2013), and officer-involved domestic
violence (see Stinson & Liederbach, 2013). Cases were also coded to assess each arrested
officer’s history of being named as a party-defendant in federal civil rights litigation pursuant to
42 U.S.C. §1981 (asserting a denial of equal rights under the law), 42 U.S.C. §1983 (asserting a
civil rights deprivation under the color of law), and 42 U.S.C. §1985 (asserting a conspiracy to
interfere with civil rights). The master name index in the federal court’s Public Access to Courts
Electronic Records (PACER) system to search and cross-reference the names of each arrested
officer in our database to measure official capacity civil rights litigation as a correlate of police
misconduct. Data from PACER were also recorded on civil actions removed from state trial
courts to a United States District Court pursuant to the provisions of 28 U.S.C. §1441 (removal
of civil actions).
Secondary data were employed from the Census of State and Local Law Enforcement
Agencies (CSLLEA) to ascertain demographic data including the number of full-time sworn
personnel and part-time sworn personnel employed by each agency where arrested officers
served. There are 52 agencies included in this study that were not listed in the 2008 wave of the
CSLLEA. County (and independent city) five-digit FIPS identifier numbers were used to verify
location of arrested officers’ employing law enforcement agencies, as well as for use as a key
variable to merge other data sources into the project’s master database and data set. The U.S.
Department of Agriculture’s (2003) county-level urban to rural nine-point continuum scale was

69
used to measure rurality. Population data from the U.S. Census Bureau’s decennial census in
years 2000 and 2010 were utilized for county, independent city, and state populations.
Reliability
Analytic procedures were undertaken to ensure reliability of the data. An additional
coder was employed to independently code a random sample of five percent of the total number
of cases in the study. Intercoder reliability was assessed by calculating the Krippendorf’s alpha
coefficient across 195 variables of interest in this study on a random sample (n = 290, 4.3%) of
the cases in the study (N = 6,724) (see Hayes & Krippendorff, 2007). Krippendorf’s alpha is
often recognized as the standard reliability statistic for content analysis research (Riffe, Lacy, &
Fico, 2005). The Krippendorf’s alpha coefficient (Krippendorf’s α = .9153) is strong across the
variables in this study (see Krippendorff, 2013). The overall level of simple percentage of
agreement between coders across all of the variables in this study (97.7%) also established a
degree of reliability well above what is generally considered acceptable in content analysis
research (see Riffe et al., 2005).
Statistical Analysis
Chi Square is used to measure the statistical significance of the association between two
variables measured at the nominal level. Cramer’s V measures the strength of that relationship
with values that range from zero to 1.0 and allows for an “assessment of the actual importance of
the relationship” (Riffe et al., 2005, p. 191). Stepwise binary logistic regression is used to
determine which of the predictor variables are statistically significant in multivariate models.
Stepwise logistic regression models are appropriate where the study is purely exploratory and
predictive (Menard, 2002). This is an exploratory study because little is known empirically
about police sexual misconduct arrests and the specific factors responsible for conviction and/or

70
job loss subsequent to the arrest of a police officer for sex-related crimes. Summary statistics are
also reported for evaluation of regression diagnostics and each logistic regression model.
Classification tree analysis—also known as decision trees—is utilized as a statistical
technique to uncover the causal pathways between independent predictors and one of Stinson’s
(2009) types of police crime versus other types of police crime, job loss, and conviction. This
approach moves beyond the simple one-way additive relationship of linear statistical models by
identifying the hierarchical interactions between the independent predictors and their
compounding impact. Classification trees examine the entire data set and produce a graphical
output that ranks the variables by statistical importance. The most influential variable is
represented at the top of the tree (known as the root node). This variable is used to split the data
in a recursive manner through the creation of subsets into the lower branches of the tree.
Variable selection and splitting criteria are driven by the algorithm of the tree program. Decision
tree techniques have received attention due to their ability to handle interaction effects in data
without being bound to statistical assumptions (Sonquist, 1970). Classification tree analysis has
been used to examine police practices including career-ending police misconduct (Kane &
White, 2013), police drug corruption arrests (Stinson, Liederbach, et al., 2013), fatal and nonfatal
incidents involving conductive energy devices (White & Ready, 2009), and police drunk driving
(Stinson, Liederbach, Brewer, & Todak, 2014). This study utilizes two decision tree predictive
analytic algorithms: Classification and Regression Trees (CART) and Chi-Square Additive
Interaction Detection (CHAID).
CART is a classification procedure that produces a binary decision tree and restricts
partitioning at each node to two nodes, thus producing binary splits for each child node
(Dension, Mallick, & Smith, 1998). Breiman, Friedman, Olshen, and Stone’s (1984) CART

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algorithm uses an extensive and exhaustive search of all possible univariate splits to determine
the splitting of the data for the classification tree. Partitioning will continue until the algorithm is
unable to produce mutually exclusive and homogenous groups (De’ath & Fabricius, 2000;
Dension et al., 1998). After creating an exhaustive tree, CART will prune nodes that do not
significantly contribute to overall prediction.
The CHAID algorithm differs from other classification tree algorithms through the
inclusion of multiple measurement levels for the independent variables. The algorithm can
compute nominal, ordinal, and interval levels for both independent and dependent variables.
Therefore, the independent variables can have different levels of measurement. If a ratio level
variable is included in the analysis, the program will convert the variable into a categorical
variable. Kass (1980) was concerned with the computation time when running decision trees and
therefore, created his algorithm with time in mind (Wilkinson, 1992). He created an algorithm
that partitioned the data in a timely manner without losing its ability to uncover interactions and
lose predictive power. Because of this, computation time is saved and CHAID can search
through large data sets to produce T without adding significant computation time.
The CART algorithm was primarily used in this study because it fit our problems and
produced optimal decision trees for most of the models by minimizing the generalization error
(see, e.g., Rokach & Maimon, 2005, p. 167). CHAID was selected for a few decision tree
models due to CART’s inability to partition the data. Since CART only produces binary splits
certain subsets of data are unable to be partitioned into an interpretable tree. The CHAID
algorithm conducts exhaustive searches of the data, which allows smaller data sets to be
partitioned into trees.

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The predictive power of logistic regression and classification tree models is assessed
through the area under the curve (AUC) component of the receiver operating characteristic
(ROC). The AUC assesses the predictive accuracy of a statistical model and serves as the
preferred method for assessing and comparing models (Bewick, Cheek, & Ball, 2004; Dolan &
Doyle, 2000). The ROC curve considers the sensitivity versus 1- specificity, a representation of
the true positive rate versus the false positive rate (TPR vs. FPR). The curve is displayed
graphically by plotting the true positive rate (TFP) on the y-axis and the false positive rate (FPR)
on the x-axis. ROC curves are interpreted through the AUC, a score that ranges from zero to
one. A straight line through a ROC curve is the equivalent of 0.5 and suggests that the model is
no better at prediction than flipping a coin. A score of one indicates that the model is able to
accurately predict all cases. The AUC is interpreted as a proportional reduction of error (PRE)
2
measure of explained variation by calculating RROC
 2( AUC  .5) (Menard, 2010).

Strengths and Limitations
The news search methodology utilizing the Google News search engine and the Google
Alerts email update service provided an unparalleled amount of information on police crime
arrests at law enforcement agencies across the United States. The Google News search engine
algorithm offers some clear advantages over other aggregated news databases and the
methodologies employed by previous studies that used news-based content analyses to document
cases of sex-related police misconduct. The Google Alerts email update service provides the
ability to run persistent automated queries of the Google News search engine and deliver realtime search results. The Google News search engine draws content from more than 50,000 news
sources (Bharat, 2012) and allows for access to a larger number of police misconduct cases than
would be available through other methods (Payne, 2013).

73
There are four primary limitations of the data. First, this research study includes every
case known to the research team of a nonfederal sworn law enforcement officer who was
arrested during the years 2005-2011. Thus, this study is a census of the universe of police crime
arrest cases identified through our search methodology. We do not purport to include every
single instance of a law enforcement officer being arrested. Second, our research is limited by
the content and quality of information provided for each case. The amount of information
available on each case varied, and data for several variables of interest were missing for some of
the cases. This is especially true for victim-related variables in this study as news organizations
generally do not report the names and other personally identifiable information (e.g., victim age,
relationship to the accused) of rape victims (Denno, 1993). Third, the data are limited to cases
that involved an official arrest based on probable cause for one or more crimes. We do not have
any data on police officers who engaged in criminal activity if their conduct did not result in an
arrest. Finally, we note that these data are the result of a filtering process that includes the
exercise of discretion by media sources in terms of both the types of stories covered and the
nature of the content devoted to particular stories (Carlson, 2007). Ready, White, and Fisher
(2008), however, found that news coverage of officer misconduct is consistent with official
police records of these events. Research also suggests that police agencies are not especially
effective at controlling media accounts of officer misconduct (Chermak, McGarrell, &
Gruenewald, 2006). Despite the noted limitations, the use of news articles as the primary data
source is a long established method of analyzing deviant/illegal police behavior (see, e.g.,
Lawrence, 2000; Lersch & Feagin, 1996; Rabe-Hemp & Braithwaite, 2013; Ross, 2000).
Efforts were made to minimize the limitations inherent in utilizing open source
information for data collection and quantitative analyses. Online news sources were limited to

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the news articles published by reputable news sources. We did not rely on blog entries and other
self-published information. Whenever possible data sources were triangulated for each instance
where an arrest case of a sworn law enforcement officer was reported. Triangulation included
reliance on multiple published news articles, videos from television news programs, and online
criminal court records. Efforts were made to verify case information by at least two separate
sources whenever possible.
As LaFree and Dugan (2007) noted, even with data triangulation there are two serious
drawbacks in use of open source data culled from online news sources. First, news reporting on
police crime is inherently biased toward publication of only the most newsworthy forms of crime
by sworn law enforcement officers. In some instances arrests of officers were not reported in the
news, because the arrestee’s employment as a law enforcement officer was unknown to news
reporters. Indeed, sometimes the only aspect making an arrest newsworthy is the fact that a law
enforcement officer was arrested. Second, our database lacks information on many aspects of
the crimes reported in online open source news publications. There is a lack of uniformity from
article to article and across the census of articles collected about each case and arrest. There was
journalistic consistency whereby news articles typically included some or all of the following
data: name of the officer arrested, officer’s employer, officer’s age and years of service as a
sworn law enforcement officer and types of criminal offense(s) charged. Often times the
information regarding crime victims in these cases were vague, typically to protect the identity of
victims of domestic violence or sexual assault, and when the victim was a child. News articles
as a primary source of arrest data is merely a substitute for better data sources that either do not
exist or are not readily available to researchers. There are no official data collected, tabulated,

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archived, or published that would provide the same level of information as analyzed in this
study.

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RESULTS
The results of the study are organized in seven parts. Part I presents the full police crime
data set models. Part II presents the sex-related police crime data set models. Part III presents
the alcohol-related police crime data set models. Part IV presents the drug-related police crime
data set models. Part V presents the violence-related police crime data set models. Part VI
presents the profit-motivated police crime data set models. Part VII presents the employing law
enforcement agencies in terms of rates of police crime per agency, per 1,000 officers, and per
100,000 population.
Part I: Full Police Crime Data Set Models
The Google News searches resulted in the identification of 6,724 cases in which sworn
law enforcement officers were arrested during the time period January 1, 2005, through
December 31, 2011. The cases involved the arrests of 5,545 individual sworn officers employed
by 2,529 nonfederal state, local, and special (e.g., school district police, college/university police,
park police, and constables) law enforcement agencies located in 1,205 counties and independent
cities in all 50 states and the District of Columbia.2 Of these, 674 (12.1%) of the officers have
more than one case (although X = 1.21, Mdn = 1.00, Mode = 1, SD = .870) because they have
had more than one victim (one criminal case per victim) and/or were arrested on more than one
occasion.
More than half of the criminal cases resulted in the arrested officer losing his or her job
(n = 3,628, 54.0%) as a result of being arrested. The known final adverse employment outcomes
include cases where no adverse action was taken against the arrested officer (n = 868, 12.9%),
cases where the officer was suspended from their job (n = 2,228, 33.1%) as a sworn officer for a

Cities in the Commonwealth of Virginia are not in counties. The cities of Baltimore, Maryland, St. Louis,
Missouri, and Carson City, Nevada, are also independent cities that are separate from counties.

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period of time, cases where the officer resigned (n = 1,709, 25.4%), and cases where the officer
was terminated (n = 1,919, 28.5%). Less than half of the police crime arrest cases resulted in
known criminal conviction (n = 2,846, 42.3%) on at least one offense charged against the
arrested officer.
Arrested Officers, Employing Law Enforcement Agencies, and Criminal Offenses Charged
Table 1 presents information on the arrest cases in terms of the arrested officers and their
employing nonfederal law enforcement agencies. Most of the cases involve male officers (n =
6,357, 94.5%). The modal category for known officer age at time of arrest is 36-39 years of age
(n = 1,081, 16.1%). The youngest officer(s) were age 19 at time of arrest, and the oldest
officer(s) were 79 years old ( X age = 37.34, Mdn age = 37, Mode age = 39, SD = 8.576 years).
The modal category for known years of service at time of arrest is three to five years (n = 954,
14.2%). Most of the cases involve police employed in a patrol or other street-level rank such as
nonsupervisory officers, deputies, troopers, and detectives (n = 5,464, 81.3%). Other arrest cases
involve line and field supervisors (n = 881, 13.1%) (i.e., corporals, sergeants, and lieutenants), as
well as police managers and executives (n = 379, 5.6%) (i.e., captains, majors, colonels, deputy
chiefs and chief deputies, and chiefs and sheriffs). More than half of the arrest cases involve
crimes that were committed while an officer was on-duty (n = 3,931, 58.5%). Two-thirds of the
cases (n = 4,447, 66.1%) involve arrests made of officers by a law enforcement agency other
than the arrested officer’s employing law enforcement agency.
Most of the arrest cases involve sworn officers employed by municipal police
departments (n = 4,915, 73.1%) or sheriff’s offices (n = 1,109, 16.5%). Arrested officers were
also employed by primary state police agencies (n = 269, 4.0%), county police departments (n =
226, 3.4%), special police departments (n = 174, 2.6%) (e.g., park police departments, school

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district police departments, college/university police departments, etc.), or other nonfederal law
enforcement agencies (n = 31, 0.4%) (e.g., constable agencies, tribal police departments, and
regional police departments). The modal category for agency size by number of officers
employed is 1,000 or more full-time sworn officers (n = 1,857, 27.6%) and zero part-time sworn
officers (n = 5,008, 74.5%). Most of the arrested officers were employed by a law enforcement
agency located in a nonrural metropolitan county (n = 5,711, 84.9%). The employing agencies
are located throughout the United States, including in Southern states (n = 2,906, 43.2%),
Northeastern states (n = 1,430, 21.3%), Midwestern states (n = 1,380, 20.5%), and Western
states (n = 1,008, 15.05%).
Table 2 presents the cases in terms of the most serious offense charged. There are 61
separate offense categories represented as the most serious offense charged in the arrest cases in
years 2005-2011. Most common as the most serious offense charged in a case are simple
(misdemeanor) assault (n = 877, 13.0%), driving under the influence (n = 841, 12.5%),
aggravated (felonious) assault (n = 572, 8.5%), forcible fondling (n = 352, 5.2%), forcible rape
(n = 322, 4.8%), and drug offenses (n = 308, 4.6%). Other noteworthy crimes that were the most
serious offense charged in cases include murder and nonnegligent manslaughter (n = 125, 1.9%),
burglary (n = 112, 1.7%), robbery (n = 109, 1.6%), thefts from buildings (n = 103, 1.5%),
statutory rape (n = 100, 1.5%), extortion and blackmail (n = 95, 1.4%), forcible sodomy (n = 94,
1.4%), obstruction of justice (n = 93, 1.4%), pornography and obscene material (n = 86, 1.3%),
and criminal deprivation of civil rights (n = 84, 1.2%).
Victims of Police Crime
Table 3 presents information on the characteristics of the victims in police crime arrest
cases in years 2005-2011. Over half of the known victims of police crime are female (n = 2,246,

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33.4%, valid 61.2%). The modal category for victim age is 25-32 years old (although X age =
24.16, Mdn age = 20, Mode age = 15, SD = 14.134 years). Victim age is missing data in a large
number of cases (n = 4,876, 72.5%). In many cases (n = 2,142, 31.9%) we were able to
determine if the victim was an adult (n = 3,051, 45.3%, valid 76.5%) or a child (n = 939, 14.0%,
valid 23.5%) but were unable to determine the exact age of a victim. The youngest victims were
infants under the age of one year old (n = 10, 0.1%, valid 0.5%) and the oldest victims were age
92 (n = 4, 0.1%, valid 0.2%).
The relationship of the victim and the arrested officer could not be determined in a large
number of arrest cases (n = 2,790, 41.5%). Many of the known victims are adult strangers or
nonstranger acquaintances (n = 2,237, 33.3%, valid 56.9%) or a child unrelated (n = 673, 10.0%,
valid 17.1%) to the arrested officer. Other victims are a current spouse (n = 346, 5.1%, valid
8.8%), former spouse (n = 68, 1.0%, valid 1.7%), a current girlfriend or boyfriend (n = 202,
3.0%, valid 5.1%), a former girlfriend or boyfriend (n = 136, 2.0%, valid 3.5%), or a child or
stepchild of the arrested officer (n = 177, 2.7%, valid 4.5%). The victims in some of the arrest
cases are also police officers (n = 229, 3.4%, valid 5.8%). In those cases where the victim was
also a police officer, most of the crimes occurred while the officer who was arrested was off-duty
(n = 179, 78.2%), where χ2 (1, N = 3,967) = 30.746, p < .001, V = .088.
Predicting Conviction
Bivariate analyses were conducted as part of the regression diagnostics and to assess the
associations between various independent variables and the dependent predictor variables of
interest. Chi-Square associations are statistically significant at the p < .05 level for 90
independent variables, individually, and the dependent variable, criminal conviction on any
offense charged. See Table 4. The strongest bivariate predictors of an officer being convicted

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on any offense charged are (a) most serious offense charged, where χ2 (67, N = 3,934) = 394.757,
p < .001, V = .317; (b) age of the victim, where χ2(1, N = 3,934) = 110.509, p = .005, V = .305;
and (c) victim’s relationship to the arrested officer, where χ2(1, N = 3,934) = 114.234, p < .001, V
= .219.
Multivariate analyses were conducted to further investigate the relationship between the
outcome variable, criminal conviction on any offense charged, and various predictor variables.
Table 5 presents a backward stepwise binary logistic regression model predicting criminal
conviction on any offense charged. Bivariate correlations computed for each of the independent
variables revealed that none of the variables were highly correlated with each other.
Multicollinearity is not a problem in the model as indicated by no tolerance statistics below .575
and no variance inflation factors exceeding 1.386. A Durbin-Watson score of 1.754 indicates
that there is no autocorrelation in the model. Regression results indicate that the overall model
of 16 predictors is statistically reliable in distinguishing between officers who were convicted on
any offense charged and officers who were not convicted on any charge. The model correctly
2
classified 71.2% of the cases (AUC = 718, 95% CI [.697, .740], RROC
= .436). Wald statistics

indicate that all of the independent variables in the model significantly predict conviction versus
nonconviction.
Odds ratio interpretations provide context for prediction of criminal case outcomes.
Twelve independent variables in this model predict when an officer is more likely to be
convicted of a crime. For example, the simple odds of an officer being convicted on any offense
charged are 13 times greater if the arrest case involved a drug shakedown, controlling for all
other variables in the model. The simple odds of an officer being convicted are 6.9 times greater
if the arrest case was officer-involved domestic violence and the victim died of the injuries,

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controlling for all other variables in the model. Also, the simple odds of an officer being
convicted are about 1.9 times greater if the officer was arrested for driving under the influence
while driving a personally-owned vehicle, controlling for all other variables. Four independent
variables in the model predict when an officer is less likely to be convicted of any offense
charged in their arrest case. Among these, for every one step increase in an arrested officer’s
rank at time of their arrest, the simple odds of conviction go down by 9.2%, controlling for all
other variables in the model. The simple odds of conviction go down by 29% if an officer was
suspended from his or her law enforcement job following their being arrested. Also, the simple
odds that an arrested officer will be convicted go down by 38.8% if the officer was arrested for a
crime related to officer-involved domestic violence and the victim suffered nonfatal injuries.
Decision trees were used to derive the casual pathways between independent predictors
and the case dispositions using the CART algorithm. The CART analysis includes a total of
3,934 cases. Figure 1 presents the results of this analysis. The tree had an overall classification
2
= .388) and selected the variable most
score of 74.6% (AUC = .694, 95% CI [.676, .713], RROC

serious offense charged as the splitting criterion. Officers who were charged with offenses
scores over 18 were convicted of a crime in 77.8% of the cases (node 2). In contrast, officers
who were charged with offenses scores equal to or less than 18 were convicted in 53.9% of the
cases (node 1). The second tier of the decision tree includes four additional splits in the data.
Node 2 is further separated by the variable most serious offense charged; these scores are all
greater than 18. Officers who received an offense score between 19 and 68 were convicted of a
crime in 80.4% of the cases. Officers who had a score above 68 were only convicted in 63.7% of
the cases.3
3

The numerical scoring for this variable (V183, most serious offense charged) is misleading to the extent that the
categorical variable was created by taking the 65 dichotomous offense variables (V15 through V80) and making a

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Node 1 was further separated by the variable victim’s relationship to the offender. In the
cases where victims were a stranger or an unrelated child the officers were convicted in 60.3% of
the cases. In cases that involved victims who were a current spouse to the offender, relative,
child or stepchild, current and or former boyfriend/girlfriend, the officers were convicted in
43.4% of the cases. The tree also included the following variables in tiers three through five:
gender of the victim, geographic division, victim’s relationship to the offender, state, profitmotivated versus other crime type, and age categorical.
Predicting Job Loss
The next set of models predict job loss after an officer has been arrested for committing
one or more crimes. At the bivariate level, Chi-Square associations are statistically significant at
the p < .05 level for 120 independent variables, individually, and the dependent variable job loss.
See Table 6. The job loss variable is a binary variable that was created by recoding the variables
for involuntary termination (V100)—that is, an officer was fired—and resignation (V101)—that
is, an officer quit his or her job as a sworn officer—after being arrested. The recoded
dichotomous variable for job loss is coded as 0 = kept job, and 1 = lost job. Cases where we
were unable to determine the final adverse employment outcome were coded as 0 = kept job. As
such, for this dichotomous recoded variable the absence of information as to an officer having
lost their job was treated as if the officer kept their job after being arrested for some criminal
offense(s).4 The strongest bivariate predictors of an officer losing their job as a sworn law

new 65-category variable for the most serious offense charged in each case. Since the research assistant coders were
already familiar with the specific offenses (that is, V15-V80), we continued to use the labels of 15-80 for the 65
offense categories in V183.
4
The research interest with this variable is in sworn law enforcement officers who lost their jobs (either through
involuntary termination or voluntary resignation). A coding protocol decision was made to focus on what our data
would support, which is job loss. We operationalize kept job as those cases where the arrested officer is not known
by us to have lost their job. Data collection efforts on this variable often resulted in missing data from the open
source information because in many jurisdictions final adverse employment outcomes are treated as confidential
personnel records.

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enforcement officer following a criminal arrest are (a) most serious offense charged, where χ2 (1,
N = 6724) = 752.849, p < .001, V = .335; (b) age of victim, where χ2 (1, N = 6,724) = 111.533, p
= .008, V = .247; and (c) victim’s relationship to the arrested officer, where χ2 (1, N = 6,724) =
173.685, p < .001, V = .210.
Table 7 presents a backward stepwise binary logistic regression model predicting job
loss. Bivariate correlations computed for each of the independent variables revealed that none of
the variables in the model were highly correlated with each other. Multicollinearity is not a
problem as indicated by no tolerance statistics below .592 and no variance inflation factors
higher than 1.689. There is no autocorrelation in the model as measured by the Durbin-Watson
score of 1.646. Logistic regression results indicate that the multivariate model of 11 predictors is
statistically reliable in distinguishing between officers who kept their jobs after being arrested
and officers who lost their jobs after being arrested, although the model only correctly classified
2
= .276). Wald statistics indicate that
68.7% of the cases (AUC = .638, 95% CI [.625, .652], RROC

all of the independent variables in the model significantly predict whether an officer lost their or
kept their job subsequent to being arrested.
Predictors of the final adverse employment outcomes are aided by interpretation of the
odds ratios. Seven of the independent variables in the model predict when an officer is more
likely to lose their job after being arrested. The simple odds that an officer will lose their job
(through either involuntary termination or voluntary resignation) are approximately 2.5 times
greater if the arrested officer has been sued in federal court pursuant to 42 U.S.C. §1983
(deprivation of rights under color of law) at some point during his or her law enforcement career.
The federal civil rights civil action(s) may or may not be related to the underlying incident(s)
leading to an officer’s criminal arrest, and the federal lawsuit(s) may have been filed before

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and/or after an officer’s date of arrest. The type of crime(s) for which an officer was arrested
impacts on job loss. For example, the simple odds that an officer will lose their job are 1.3 times
greater if the offense was a sex-related crime. Similarly, the simple odds that an officer will lose
their job increase by 73% if the crime involved police sexual violence. Likewise, the simple
odds that an officer will lose their job increase by about 54% if the arrest case is alcohol-related.
Four of the independent variables in the logistic regression model predict when an officer
is more likely to keep their job in the aftermath of being arrested. Adverse employment
outcomes short of termination predict the final adverse employment outcome. If an officer was
suspended from their job after being arrested, then the simple odds of losing their job go down
by 61.5%. Likewise, the simple odds of an officer losing their job after being arrested decrease
by 65.4% if the officer was reassigned to another position within the employing law enforcement
agency after being arrested. Two variables related to the nature of the incident resulting in an
officer’s arrest also predict that an officer will not ultimately lose their job as a sworn law
enforcement officer as a result of being arrested. The simple odds that an officer will lose their
job decrease by 48.8% if the crime involves family violence. As to drug offenses, the simple
odds that an officer will lose their job decrease by 95.6% if the criminal case involves marijuana.
Figure 2 presents the results of predicting job loss and included a total of 6,724 arrest
cases. The tree had an overall classification score of 71.0% (AUC = .766, 95% CI [.754, .777],
2
RROC
= .532) and selected the variable sex-related police crime versus other police crime as the

splitting criterion. Officers who were involved in sex-related cases (node 1) lost their job in
72.1% of the cases. In contrast, officers who were involved in a criminal case that was not sexrelated (node 2) lost their job in 48.9% of the cases.

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The sex-related cases in node 1 were partitioned by the variable officer was suspended for
a period of time. Officers who had been previously suspended lost their job in 66.3% of the
cases and officers who were not previously suspended lost their job in 82.1% of the cases. The
cases that were not sex-related in node 2 were partitioned by the variable profit-motivated police
crime versus other crime. Officers who were involved in a profit-motivated case lost their job in
67.7% of the cases and officers who were not involved in a profit-motivated case lost their job in
40.9% of cases. The tree also included the following variables in tiers three through nine:
internal versus organization, geographic division, state, official capacity versus individual
capacity, age categorical, victim’s relationship to the offender, driving under the influence,
urban/rural continuum, years of service categorical, part-time sworn personnel, drug-related
police crime versus other crime, officer was reassigned to another position in the agency, age of
the victim, full-time sworn personnel, and gender of the victim.
Predicting Sex-related Police Crime
In this section, models predict sex-related arrest cases versus other types of arrest cases.
Preliminarily, at the bivariate level, Chi-Square associations are statistically significant at the p <
.05 level for 117 independent variables and the dependent variable, sex-related arrest cases
versus other types of crimes. See Table 8. The strongest bivariate predictor of sex-related police
crime is the most serious offense charged, where χ2 (63, N = 6,724) = 5322.861, p < .001, V =
.890, indicating that there is a very high correlation between outcome variable (sex-related police
crime arrest cases, which is a true dichotomy) and the predictor variable (most serious offense
charged, which is a categorical variable with up to 65 levels of offenses). The most serious
offense charged variable was coded based on the hierarchy of offenses in the Uniform Crime
Reports. The very high bivariate correlation here is because the second most serious offense on

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the UCR hierarchy is rape, and rape is the most serious offense charged (n = 322, 21.8%) within
the sex-related arrest cases (N = 1475). Other strong bivariate associations of sex-related police
crime arrest cases include (a) age of the victim, where χ2 (79, N = 1,848) = 592.004, p < .001, V
= .566; (b) relationship of the victim to the arrested officer, where χ2 (7, N = 3,934) = 909.238, p
< .001, V = .481; (c) years of difference in age between the arrested officer and their victim,
where χ2 (103, N = 6,724) = 1,406.902, p < .001, V = .457; (d) gender of the victim, where χ2 (1,
N = 3,668) = 651.711, p < .001, V = .422; and (e) child victims, where χ2 (1, N = 3,990) =
684.979, p < .001, V = .414.
Moderate bivariate associations of sex-related arrest cases include (a) driving while
female encounters, where χ2 (1, N = 6,724) = 587.698, p < .001, V = .296; (b) citizen complaint
as the method of crime detection, where χ2 (1, N = 6,724) = 520.439, p < .001, V = .278; (c)
profit-motivated police crime, where χ2 (1, N = 6,724) = 492.036, p < .001, V = .271; (d)
violence-related police crime, where χ2 (1, N = 6,724) = 357.899, p < .001, V = .231; and (e)
alcohol-related police crime, where χ2 (1, N = 6,724) = 260.078, p < .001, V = .197.
A backward stepwise binary logistic regression model predicting sex-related police crime
arrest cases versus other types of police crime arrest cases is presented in Table 9. Bivariate
correlations computed for each of the independent variables in the model revealed that none of
the variables were highly correlated with each other. Multicollinearity is not a problem as
indicated by no tolerance statistics below .312 and no variance inflation factors higher than
3.207. The Durbin-Watson score of 1.643 indicates that there is no autocorrelation in the model.
Regression results indicate that the overall model of 14 predictors is statistically reliable in
distinguishing between sex-related police crime arrest cases and police crime arrest cases
involving other types of crime. The model correctly classified 88.7% of the cases (AUC = .939,

87
2

95% CI [.931, .947], RROC = .878). Wald statistics indicate that all of the independent variables
in the regression model significantly predict sex-related police crime arrest cases.
Interpretation of odds ratios provides context for prediction of sex-related police crime
arrest cases. Five of the independent variables in the model predict sex-related police crime
arrest cases versus other types of police crime arrest cases, four of which are interpreted in the
sentences that follow. Duty status predicts sex-related police crime. The simple odds that an
arrested officer’s case involves a sex-related offense increase by 57% if the officer was on-duty
at the time of commission of the crime(s) charged. The method of crime detection also predicts
sex-related arrest cases. The simple odds that an officer’s arrest is sex-related are approximately
6.2 times greater is the method of crime detection is a citizen complaint, controlling for all other
variables in the model. Since sex crimes are typically committed out of public view it makes
sense that the primary method of crime detection for sex crimes is by way of a citizen complaint
as opposed to other methods of crime detection.
Two predictors involve characteristics of the victim of an officer’s sex-related arrest case.
The simple odds that the victim is a child are 5.6 times greater if the arrest case is sex-related
versus some other type of crime. The relationship of the victim to the arrested officer also
predicts sex-related police crime arrest cases versus other types of police crime arrest cases.
Victim relationship is an eight category variable, and the precise interpretation is difficult in a
logistic regression model. Literally, the simple odds that an officer’s arrest case is sex-related go
up by 40.7% for every one unit increase in victim relationship (but this is a nominal-level
measure and the practical interpretation is lacking). In other words, the likelihood of a police
crime arrest case being sex-related increases as you move from the victim being a spouse,
through other levels of relation, toward victims who are strangers or nonstranger acquaintances.

88
Ten of the independent variables in the logistic regression model predict that a police
crime arrest case will be some type of crime other than a sex-related one. For example, the
simple odds that an arrest case is sex-related decrease by 96.9% if the victim is male. Family
violence cases are typically not also sex-related crimes. The simple odds that a crime is sexrelated decrease by 73.3% if the crime involves family violence. Also, the simple odds that a
crime is sex-related decrease by 77% if the arrest case is officer-involved domestic violence
where the officer is accused of using his hands/fists as a weapon. Alcohol-related or profitmotivated police crime arrest cases are not typically also sex-related crimes. The simple odds
that an arrest case is sex-related decrease by 75.4% if the case is alcohol-related, whereas the
simple odds that an arrest case is sex-related decrease by 88.6% if the case is profit-motivated.
Finally, the simple odds of an arrest case being sex-related decrease by 51.5% if the arrested
officer has been sued in federal court pursuant to 42 U.S.C. §1983 at some point during his or
her law enforcement career.
Figure 3 presents the results of predicting sex-related cases versus other crime types and
included a total of 6,724 arrest cases. The tree had an overall classification score of 89.6%
2

(AUC = .895, 95% CI [.884, .907], RROC = .790) and selected the variable victim age
(categorical) as the splitting criterion. Cases that involved the victims from the age of birth to 19
(node 1) were sex-related 68.4% of the time and cases that involved victims 20 years or older
(node 2) were sex-related in only 14.7 % of the cases.
The victim age categories in node 1 were partitioned by the variable gender of the victim.
Cases that involved a female victim were sex-related 84.2% of the time and cases that involved
male victims were sex-related in only 34.1% of the cases. The victim age categories in node 2
were partitioned by the variable driving while female encounter. Cases that stemmed from

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driving while a female encounter were sex-related 95.7% of the time and cases that did not
involve a female encounter were only sex-related in 12.3% of the cases. The tree also included
the following variables in tiers three through five: victim’s relationship to the offender and
alcohol-related versus not alcohol-related.
Predicting Alcohol-related Police Crime
The next set of models predict alcohol-related police crime arrest cases versus other types
of police crime arrest cases. Table 10 presents statistically significant Chi-Square bivariate
associations at the p < .05 level for 116 independent variables and the dependent variable,
alcohol-related arrest cases versus other types of arrest cases. The strongest bivariate predictor
of alcohol-related police crime arrests is the most serious offense charged, where χ2 (63, N =
6,724) = 3906.421, p < .001, V = .762. This very strong bivariate association is likely due to
alcohol-related incidents typically falling within relatively few offense categories (e.g., driving
under the influence, public drunkenness, disorderly conduct, etc.) for a large number of the
alcohol-related arrest cases. Other moderate bivariate associations of alcohol-related police
crime arrest cases include (a) duty status, where χ2 (1, N = 6724) = 630.074, p < .001, V = .306;
(b) victim age, where χ2 (79, N = 1,848) = 172.884, p < .001, V = .306; and (c) individual versus
official capacity, where χ2 (1, N = 6,724) = 619.269, p < .001, V = .303.
Table 11 presents a backward stepwise binary logistic regression model predicting
alcohol-related police crime arrest cases. Bivariate correlations computed for each of the
independent variables in the model revealed that none of the variables were highly correlated
with each other. Multicollinearity is not a problem as indicated by no tolerance statistics below
.392 and no variance inflation factors higher than 2.552. The Durbin-Watson score of 1.612
indicates that there is no autocorrelation in the model. Logistic regression results indicate that

90
the overall model of 17 predictors is statistically reliable in distinguishing between alcoholrelated police crime arrest cases and police crime arrest cases involving other types of police
crime. The model correctly classified 88.1% of the cases (AUC = .802, 95% CI [.781, .823],
2
= .604).
RROC

Odds ratio interpretations provide context for prediction of alcohol-related police crime
arrest cases. Seven of the independent variables in the model predict when an arrest case is more
likely to be alcohol-related. Two of these predictors are independent variables relating to the
underlying nature of the criminal behavior. The first relates to drug usage. The simple odds that
an officer’s arrest case will be alcohol-related are 5 times greater if the arrest incident involves
marijuana. This can be explained to the extent that imbibing in alcoholic beverages and smoking
marijuana often co-occur in social settings. The second relates to sexual violence. The simple
odds that an officer’s arrest case will be alcohol-related are 1.6 times greater if the incident also
involves police sexual violence. This specific finding is counterintuitive for two reasons. First,
police sexual violence invokes some aspect of an officer’s official status as a sworn law
enforcement officer and most of the police sexual violence arrest cases involved on-duty (n =
516, 82.8%) criminal behavior, where χ2 (1, N = 6,724) = 481.979, p < .001, V = .268. Second,
most of the alcohol-related arrests in this study occur off-duty (n = 1,235, 87.9%). Events
occurring in the aftermath of an officer’s commission of criminal offense(s) also predict alcoholrelated police crime. The simple odds that an officer’s arrest case will be alcohol-related are
approximately 1.2 times greater if there an ensuing scandal or cover up of the incident at the law
enforcement agency employing the arrested officer.
Ten of the independent variables in the logistic regression model predict when it is
unlikely that an officer’s arrest case will be alcohol-related. Alcohol-related crimes by police

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officers tend to occur off-duty. The simple odds that an officer’s arrest case will be alcoholrelated decrease by 91.9% if the officer was on-duty when the crime occurred. Interestingly,
although acts of police sexual violence predict alcohol-related crime arrests, that is not the case
for other types of sex-related crime or violence-related crime. The simple odds that an officer’s
arrest case will be alcohol-related decrease by 74.3% if the crime is sex-related. Similarly, the
simple odds that an officer’s arrest case will be alcohol-related decrease by 29.7% if the crime is
violence-related. Finally, crimes by police officers involving child victims are typically not
alcohol-related offenses. The simple odds of an officer’s arrest case being alcohol-related go
down by 73.4% if the victim of the crime is a child.
Figure 4 presents the results of predicting alcohol-related cases versus other crime types
and included a total of 6,724 cases. The tree had an overall classification score of 89.3% (AUC
2
= .802) and selected the variable internal crime versus
= .901, 95% CI [.893, .910], RROC

organizational crime as the splitting criterion. Cases that involved a crime by the organization
against the officer (node 1) were alcohol-related 98.6% of the time and cases that internal crimes
against the organization (node 2) were alcohol-related in only 18.3 % of the cases.
A crime by the organization against the officer (node1) was a terminal node and was
unable to be partitioned further. Crimes by the officer against the organization in node 2 were
partitioned by the variable duty status. Cases that involved off-duty officers at the time of the
offense were alcohol-related 26.7% of the time and cases that involved on-duty officers were
alcohol-related in only 6% of the cases. The tree also included the following variables in tiers
three through ten: age of victim, profit-motivated versus not profit-motivated, violence-related
versus other crime type, official misconduct / official oppression / violation of oath, sex-related

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versus other crime type, internal versus organizational, drug-related versus other crime type,
false report / false statement, and method of crime detection: citizen complaint.
Predicting Drug-related Police Crime
The models in this section predict drug-related police crime arrests versus other types of
police crime arrest cases. Table 12 presents statistically significant Chi-Square bivariate
associations at the p < .05 level for 88 independent variables and the dependent variable, drugrelated arrest cases versus other types of arrest cases. Here again, the strongest bivariate
predictor of drug-related police crime arrests is the variable most serious offense charged, where
χ2 (63, N = 6,724) = 3127.330, p < .001, V = .682. Many, but not all, of the drug-related cases
include specific drug offenses (coded as V23 in this data set), which explains this very strong
bivariate correlation. Other moderate bivariate associations of drug-related police crime arrest
cases include (a) profit-motivated police crime, where χ2 (1, N = 6,724) = 580.101, p < .001, V =
294; (b) violence-related police crime, where χ2 (1, N = 6,724) = 331.322, p < .001, V = .222; (c)
age of the victim, where χ2 (79, N = 1,848) = 140.050, p < .001, V = .275; and (d) officerinvolved domestic violence resulting in fatal injuries to a victim, where χ2 (1, N = 3,840) =
3.840, p = .050, V = .240.
Table 13 presents a backward stepwise binary logistic regression model predicting drugrelated police crime arrest cases. Bivariate correlations computed for each of the independent
variables in the model revealed that none of the variables were highly correlated with each other.
Multicollinearity is not a problem in this model as indicated by no tolerance statistics below .662
and no variance inflation factors higher than 1.512. The Durbin-Watson score of 1.654 indicates
that there is no autocorrelation in the model. Logistic regression results indicate that the overall
model of ten predictors is statistically reliable in distinguishing between drug-related police

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crime arrest cases and police crime arrest cases involving other types of police crime. The model
2
correctly classified 97.9% of the cases (AUC = .645, 95% CI [.622, .668], RROC
= .290).

Odd ratio interpretations provide context for prediction of drug-related police crime arrest
cases. Nine of the independent variables in the logistic regression model predict when an arrest
case is more likely to be drug-related. Three other types of police crime also predict drug-related
police crime arrests. The simple odds that an arrest case will be drug-related are 36.7 times
greater if the crime is also profit-motivated. This finding suggests that much of the drug-related
police crime involves the drug trade. Some drug-related police crime, however, is not related to
the drug trade. For example, the simple odds that an arrest case will be drug-related are 3.6
times greater if the crime is also alcohol-related. Likewise, the simple odds that an officer’s
arrest case will be drug-related is about 3 times greater if the crime is also sex-related. Several
of the predictors of drug-related police crime are specific criminal offenses. The simple odds
that an arrest case will be drug-related are 15.7 times greater if the officer is charged with
perjury, false reports, or false statements. Similarly, the simple odds that an officer’s arrest case
will be drug-related are 12.3 times greater if the officer is charged with obstruction of justice.
Two specific sex-related criminal offenses also predict drug-related police crime. The simple
odds of an officer’s arrest case being drug-related are 9.4 times greater if the officer is charged
with pornography or obscene materials. Also, the simple odds that an arrest case will be drugrelated is 2 times greater if the officer is charged with forcible rape. One other predictor is
noteworthy in that the simple odds of an officer’s arrest case being drug-related are 3.7 times
greater if an arrested officer’s arrest case involves a DUI-related traffic accident.
Figure 5 presents the results of predicting drug-related cases versus other crime types and
included a total of 6,724 cases. The tree had an overall classification score of 89.6% (AUC =

94
2
.862, 95% CI [.849, .876], RROC
= .724) and selected the variable profit-motivated versus other

types of police crime as the splitting criterion. Cases that were profit-motivated (node 1) were
drug-related 27.5% of the time and cases that were not profit-motivated (node 2) were drugrelated in only 5.9 % of the cases.
The not profit-motivated cases (node 1) were partitioned by the variable violence-related
police crime versus other crime. Cases that were not violence-related were drug-related 12.8%
of the time and cases that were violence-related were drug-related in only 1.5% of the cases. The
profit-motivated cases (node 2) were partitioned by the variable weapons law violation. Cases
that involved officers who had a weapons law violation were drug-related 55.5% of the time and
cases that did not involve a weapons law violation were drug-related in only 24.9% of the cases.
The tree also included the following variables in tiers three through six: age of victim, sexrelated versus other crime type, method of crime detection: citizen complaint, state, and gender
of victim.
Predicting Violence-related Police Crime
The models in this section predict violence-related police crime arrest cases versus other
types of police crime arrest cases. Bivariate Chi-Square associations are statistically significant
at the p < .05 level for 131 independent variables and the dependent variable, violence-related
arrest cases versus other types of arrest cases. See Table 14. As in previous prediction models,
the strongest bivariate predictor of violence-related police crime is the most serious offense
charged, where χ2 (63, N = 6,724) = 4883.929, p < .001, V = .852. This indicates a very high
correlation between the outcome variable, violence-related arrest cases, and the predictor
variable, most serious offense charged. A large number of the violence-related arrest cases
involve a relatively small number of offense categories for the most serious offense charged

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variable (e.g., aggravated assault, simple assault, murder and nonnegligent manslaughter,
forcible rape, etc.). Strong bivariate associations of violence-related police crime arrest cases
include citizen complaint as the method of crime detection, where χ2 (1, N = 6,724) = 1327.054,
p < .001, V = .444. Moderate associations of violence-related arrest cases include (a) years
difference in victim age, where χ2 (103, N = 6,724) = 879.176, p < .001, V = .362; (b) age of the
victim, where χ2 (79, N = 1,848) = 166.314, p < .001, V = .300; and (c) victim relationship to the
arrested officer, where χ2 (7, N = 3,934) = 206.633, p < .001, V = .229.
A backward stepwise binary logistic regression model predicting violence-related police
crime arrest cases versus other types of police crime arrest cases is presented in Table 15.
Bivariate correlations computed for each of the independent variables in the model revealed that
none of the variables were highly correlated with each other. Multicollinearity is not a problem
as indicated by no tolerance statistics below .471 and no variance inflation factors higher than
2.123. Autocorrelation is also not a problem in the model as indicated by the Durbin-Watson
score of 1.608. Logistic regression results indicate that the overall model of 11 predictors is
statistically reliable in distinguishing between violence-related police crime arrest cases and
other types of police crime arrest cases. The model correctly classified 83.4% if the cases (AUC
2
= .688). Wald statistics indicate that all of the independent
= .844, 95% CI [.826, .862], RROC

variables in the logistic regression model significantly predict violence-related police crime
arrest cases.
Odds ratio interpretations provide context for prediction of violence-related police crime
arrest cases. Five of the independent variables in the logistic regression model predict when a
police crime arrest case is more likely to be violence-related. Six of the independent variables
predict when a police crime arrest case is less likely to be violence-related. Some interesting

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patterns emerge. For example, the simple odds that an officer’s arrest case will be violencerelated increase by 5.8% for every one year increase in the officer’s age at time of arrest. On the
other hand, the simple odds that an officer’s arrest case will be violence-related decrease by
3.9% for every one year increase in years of service as a sworn officer at time of arrest.
Although at first glance this seems counterintuitive, it is easily at least partially explained by
noting that not all police officers start their law enforcement careers at the same age. Many
violence-related instances of police crime come to the attention of law enforcement authorities
through citizens. The simple odds of an officer’s arrest case being violence-related are almost
1.3 times greater if the method of crime detection is a citizen complaint. Most violence-related
police crime is not also profit-motived, as the simple odds that an officer’s arrest case will be
violence-related go down by 98.2% if the crime is also profit-motivated.
Figure 6 presents the results of predicting violence-related cases versus other crime types
and included a total of 6,724 cases. The tree had an overall classification score of 84.6% (AUC
2
= .764) and selected the variable citizen complaint as the
= .882, 95% CI [.874, .891], RROC

method of crime detection as the splitting criterion. Cases that involved a citizen complaint
(node 1) were violence-related 68.1% of the time and cases that did not involve a citizen
complaint were only violence-related in 22.9% of the cases.
Cases that involved citizen complaints in node 1 were partitioned by the variable profitmotivated police crime versus other types of crime. Cases that were profit-motivated were
violence-related 16.9% of the time and cases that were not profit-motivated were violencerelated in 76.8% of the cases. Cases that did not involve a citizen complaint in node 2 were
partitioned by the variable victim age (categorical). Cases that involved crimes from 12-15
years of age were violence-related in 15.8 of the cases and for age birth to 11 and 16 or older

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were violence-related in 74% of the cases. The tree also included the following variables in tiers
three through five: driving under the influence, drug-related crime versus other crime type,
intimidation, and victim’s relationship to the offender.
Predicting Profit-motivated Police Crime
In this section, models predict profit-motived police crime versus other types of police
crime arrest cases. Table 16 presents statistically significant Chi-Square bivariate associations at
the p < .05 level for 127 independent variables and the dependent variable, profit-motivated
police crime arrest cases versus other types of arrest cases. As in the other models using the full
data set in this study, there is a very strong correlation between the variable most serious offense
charged and the dependent variable. At the bivariate level for the association between the most
serious offense charged and profit-motivated arrest cases, χ2 (1, N = 6,724) = 4449.343, p < .001,
V = .813. Other strong to moderate bivariate associations of profit-motivated police crime
include (a) duty status, where χ2 (1, N = 6,724) = 633.147, p < .001, V = .307; (b) age of the
victim, where χ2 (79, N = 1,848) = 211.386, p < .001, V = .338; (c) violence-related police crime,
where χ2 (1, N = 6,724) = 1280.268, p < .001, V = .436; (d) drug-related police crime, where χ2
(1, N = 6,724) = 580.101, p < .001, V = .294; (e) alcohol-related police crime, where χ2 (1, N =
6,724) = 498.804, p < .001, V = .272; (f) official capacity, where χ2 (1, N = 6,724) = 540.962, p <
.001, V = .284; and (g) sex-related police crime, where χ2 (1, N = 6,724) = 492.371, p < .001, V =
.271.
Table 17 presents a backward stepwise binary logistic regression model predicting profitmotivated police crime arrest cases. Bivariate correlations computed for each of the independent
variables in the model revealed that none of the variables were highly correlated with each other.
Multicollinearity is not a problem as indicated by no tolerance statistics below .362 and no

98
variance inflation factors higher than 2.790. The Durbin-Watson score of 1.776 indicates that
there is no autocorrelation in the model. Logistic regression results indicate that the overall
model of ten predictors is statistically reliable in distinguishing between profit-motivated police
crime arrest cases and police crime arrest cases involving other types of crime. The model
2
correctly classified 97.6% of the cases (AUC = .870, 95% CI [.801, .939], RROC
= .740).

Prediction of profit-motivated police crime arrest cases are explained through
interpretation of odds ratios. Five of the independent variables in the model predict when an
officer’s arrest is more likely to be profit-motivated police crime versus other types of police
arrest cases. The simple odds of a case being profit-motivated are 4.9 times greater if the crime
came to the attention of law enforcement by a citizen complaint. These are not the types of
crimes found on patrol by the police. More often than not profit-motivated police crime is crime
that occurs while on-duty. The simple odds that an arrest case will be profit-motivated are 1.5
times greater if the crime occurred while on-duty. Profit-motivated police crime predicts when
an arrested officer is at risk of being sued in his or her official capacity for civil rights violations.
Specifically, the simple odds that an arrest case will be profit-motivated are 4.2 times greater if at
some point during an officer’s law enforcement career he or she was named as a party-defendant
in a federal court civil action pursuant to 42 U.S.C. §1985 for a conspiracy to interfere with civil
rights. Children are rarely the victim of profit-motivated police crime as the simple odds go up
by 7.9% for every one year increase in the age of the victim at time of an officer’s crime
commission. Two independent variables in the model predict when a police crime arrest case is
less likely to be profit-motivated. Profit-motivated police crime is rarely sex-related or violencerelated. The simple odds of a profit-motivated police crime arrest case decrease by 90.4% if an
officer’s arrest case is sex-related and decrease by 94% if it is violence-related.

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Figure 7 presents the results of predicting profit-motivated crime and included a total of
6,724 police crime arrest cases. The tree had an overall classification score of 88.6% (AUC =
2
.908, 95% CI [.900, .917], RROC
= .816) and selected the variable violence-related police crime

versus other crime as the splitting criterion. Cases that were violence-related (node 1) were
profit-motivated 4.9% of the time and cases that were not violence-related (node 2) were profitmotivated in 42% of the cases. The cases that were violence-related in node 1 were partitioned
by the variable drug-related police crime versus other types of crime. Cases that did not involve
a drug crime were only profit-motivated 2.5% of the time and cases that involved a drug crime
were profit-motivated in 63.6% of the cases. Cases that were not violence-related in node 2 were
further partitioned by the variable victim age. Cases that involved victims from birth to the age
of 20 were only profit-motivated 2.9% of the time and cases that involved victims older than 20
years old were profit-motivated in 47% of the cases. The tree also included the following
variables in tiers three through four: gender of victim, alcohol-related crime versus other crime
type, and sex-related crime versus other crime type.
Predicting Federal Civil Rights Litigation pursuant to 42 U.S.C. §1983
The final area of prediction using the full data set for model building examines arrested
officers who have also at some point in their law enforcement career been sued in federal court at
least once pursuant to 42 U.S.C. §1983. Table 18 presents statistically significant Chi-Square
bivariate associations at the p < .05 level for 96 independent variables and the dependent
variable, arrested officer named as a party defendant in a federal civil action pursuant to 42
U.S.C. §1983 at some point during career. The strongest bivariate predictors of Section 1983
litigation are several other caused of action, including (a) 42 U.S.C. §1997 (civil plaintiff is an
institutionalized person) civil defendant, where χ2 (1, N = 6,724) = 1247.262, p < .001, V = .431;

100
(b) 42 U.S.C. §1985 (conspiracy to interfere with civil rights) civil defendant, where χ2 (1, N =
6,724) = 632.692, p < .001, V = .307; and (c) 42 U.S.C. §1981 (equal rights under the law) civil
defendant, where χ2 (1, N = 6,724) = 435.624, p < .001, V = .255. Civil rights litigation
originally filed by a plaintiff in a state trial court and later removed by defense counsel to a
federal district court pursuant to 28 U.S.C. §1441 is also a strong bivariate predictor, where χ2 (1,
N = 6,724) = 1192.487, p < .001, V = .421.
Other moderate bivariate associations of Section 1983 federal court civil actions are (a)
age of victim, where χ2 (79, N = 1,848) = 159.139, p < .001, V = .293; (b) officer’s years of
service at time of arrest, where χ2 (43, N = 4,780) = 250.387, p < .001, V = .229; (c) the state
where the officer’s employing law enforcement agency is located, where χ2 (50, N = 6,724) =
346.944, p < .001, V = .227; and (d) the most serious offense charged, where χ2 (63, N = 6,724) =
320.050, p < .001, V = .218.
A backward stepwise binary logistic regression model predicting arrested officers being
named as a party defendant in federal civil rights litigation pursuant to 42 U.S.C. §1983 at some
point during their policing career is presented in Table 19. Bivariate correlations computed for
each of the independent variables in the model revealed that none of the variables were highly
correlated with each other. None of the tolerance scores in the regression model are below .334
and no variance inflation factors are above 2.995, indicate that multicollinearity is not a problem.
The Durbin-Watson score of 1.580 indicates that there is no autocorrelation. Binary logistic
regression results indicate that the overall model of 16 predictors is statistically reliable in
distinguishing between arrested officers who were sued in federal court pursuant to Section 1983
and those arrested officers who never have been named as a party defendant in Section 1983

101
federal civil rights litigation. The model correctly classified 76.4% of the cases (AUC = .743,
2
95% CI [.723, .764], RROC
= .486).

Fourteen of 16 independent variables in the binary logistic regression model predict when
an arrested officer is likely to be named as a party defendant in a 42 U.S.C. §1983 federal court
civil action at some point during his or her career as a sworn nonfederal law enforcement officer.
Many of the federal court civil actions analyzed in this research study had nothing to do with the
incident for which an officer was arrested. Some of the civil litigation occurred years or even
decades prior to an officer being arrested for some crime(s). In other instances federal civil
rights litigation directly arose out of the same operative facts as in an officer’s criminal case.
Federal civil rights civil actions often include averment of more than one cause of action
in the complaint filed in a federal district court. In many instances, the officers in this study were
sued more than once for a variety of civil rights causes of action. Here, the simple odds that an
arrested officer will be named as a party defendant in Section 1983 civil litigation are 51.5 times
greater if the same officer has been named as a defendant in a civil action pursuant to 42 U.S.C.
§1981 for denial of equal rights under the law.
The simple odds that an officer will be sued in their official capacity pursuant to Section
1983 are about 2 times greater if the officer was on-duty at time of committing the crime(s) for
which they were arrested. The longer an officer has been employed by a law enforcement
agency, the more likely they are at risk of being sued in their official capacity in a federal court
civil rights civil action. The simple odds of being named in a Section 1983 civil action increase
by 6.9% for every one year increase in years of service at time of arrest. There is often collateral
damage to others in an officer’s employing law enforcement agency as indicated by the odds

102
being 9.7 times greater that an officer will be sued under Section 1983 if the officer’s supervisor
was disciplined and/or reassigned as fallout from the arrest of the subordinate officer.
The nature of the criminal offenses in an officer’s arrest case provide context for
predicting when an officer is likely to be sued in a Section 1983 civil action. The simple odds of
being named as a party defendant in a Section 1983 civil action at some point during an officer’s
law enforcement career are 1.1 times greater if an officer is arrested for kidnapping or abduction,
one time greater if charged with a crime involving family violence, 6.4 times greater if arrested
for crime involving cocaine, 1.54 times greater if arrested for a crime stemming from a “driving
while female” encounter, and go up by 99.8% if arrested for murder or nonnegligent
manslaughter.
Figure 8 presents the results of predicting a 42 U.S.C. §1983 civil defendant included a
total of 6,724 arrest cases. The tree had an overall classification score of 78.2% (AUC = .722,
2
= .444) and selected the variable official capacity versus individual
95% CI [.708, .737], RROC

capacity as the splitting criterion. Arrested officers who were acting in their individual capacity
during commission of the crime for which they were arrested (node 1) were named as party
defendants in federal court Section 1983 civil actions at some point during their law enforcement
careers 16.7% of the time. Arrested officers who were acting in their official capacity during
commission of the crime for which they were arrested (node 2) were named as party defendants
in federal court Section 1983 civil actions at some point during their law enforcement careers
32.4% of the time.
The criminal cases where an officer was acting in an individual capacity (in node 1) were
partitioned by the variable years of service. Officers who has served as a sworn officer seven
and one-half years or less at time of arrest were named as Section 1983 civil defendants at some

103
point during their career in 11.3% of the criminal cases, whereas officers who had served more
than seven and one-half years at time of arrest were named as Section 1983 civil defendants at
some point during their law enforcement career in 20.6% of the criminal cases. Criminal cases
that involved officers operating in their official capacity (in node 2) were further partitioned by
the variable violence-related police crime versus other types of crime. As to the violence-related
criminal cases, 43.8% of the cases involved arrested officers who – at some point during their
law enforcement career – were sued in federal court pursuant to Section 1983, but only 22.9% of
the cases that were not violence-related involved an officer who was at some point named as a
federal court Section 1983 civil defendant. The tree also included the following variables in tiers
three through ten: geographic division, rank, most serious offense charged, state, discussion of an
agency scandal/ cover up, years of service, years of service, age of victim, type of agency, and
urban/rural continuum.

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Part II: Sex-related Police Crime Data Set Models
There are 1,475 cases in the data set where sworn law enforcement officers were arrested
for sex-related crimes during the years 2005-2011. The sex-related police crime arrest cases
involve 1,070 individual officers employed by 795 nonfederal state, local, special, constable, and
tribal law enforcement agencies located in 550 counties and independent cities in 48 states (all
except Maine and Wyoming) and the District of Columbia. Of these, 191 of the arrested officers
have more than one case ( X = 1.38, Mdn = 1.00, Mode = 1, SD = 1.288) because they have
more than one victim (one criminal case per victim) and/or were arrested for a sex-related crime
on more than one occasion during the study period years.
The majority of sex-related police crime arrest cases involve an officer who is known to
have ultimately lost their job (n = 1,063, 72.1%) as a sworn officer after being charged with one
or more crimes. The known final employment outcomes in the sex-related arrest cases include
those in which there was no known adverse employment action taken against the officer (n = 96,
6.5%), cases resulting in the officer only being suspended for a period of time (n = 316, 21.4%),
cases in which the officer was separated through voluntary resignation (n = 505, 34.2%), and
those cases in which the officer was separated through involuntary termination (n = 558, 37.8%).
More than half the sex-related police crime arrest cases resulted in criminal conviction (n = 791,
53.6%, valid 80.2%) on one or more offenses charged, however the final criminal case
disposition is unknown (n = 489, 33.2%) in one-third of the sex-related arrest cases.
Officers Arrested for Sex-related Police Crime, Offenses Charged & Employing Agencies
Table 20 presents information on the sex-related arrest cases in terms of the arrested
officers and their employing nonfederal law enforcement agencies. Almost all of the sex-related
arrest cases involve male officers (n = 1,467, 99.5%) who were arrested. The modal category for

105
known officer age at time of arrest for sex-related cases is 36-39 years of age (n = 261, 17.6%).
The youngest officer arrested for a sex-related crime was age 19 at time of arrest, and the oldest
arrested officer was 74 years old ( X age = 37.88, Mdn age = 38.00, Mode age = 39, SD = 8.636
years). The modal category for known years of service at time of arrest for a sex-related crime is
3-5 years ( X years of service = 9.81 years, Mdn = 8.00 years, Mode = 2 years, SD = 7.650
years). Most of the sex-related arrest cases involve sworn officers employed in patrol or streetlevel nonsupervisory positions, including officers, deputies, troopers, and detectives (n = 1,221,
82.8%). Almost half of the sex-related arrest cases involves sex crimes that occurred while onduty (n = 682, 46.2%). The majority of the cases (n = 919, 62.3%) involve arrests made by some
law enforcement agency other than the department where the arrested officer was employed.
Most of the sex-related arrest cases involve sworn officers employed by municipal police
departments (n = 1,040, 70.5%) or sheriff’s offices (n = 280, 19.0%). Officers arrested for sexrelated crimes were also employed by primary state police agencies (n = 61, 4.1%), county police
departments (n = 30, 2.0%), or other types of nonfederal law enforcement agencies (n = 64,
4.4%). The modal category for agency size by number of full-time sworn personnel is 1,000 or
more sworn officers (n = 349, 23.6%). Most of the employing law enforcement agencies were
located in a nonrural metropolitan county or independent city (n = 1,229, 83.3%). The
employing agencies are located throughout the United States, including in Southern states (n =
658, 44.6%), Western states (n = 316, 21.4%), Northeastern states (n = 259, 17.6%), and
Midwestern states (n = 242, 16.4%).
Table 21 presents the sex-related arrest cases in terms of the most serious offense
charged. There are 40 separate offense categories represented as the most serious offense
charged in sex-related police crime arrest cases during the years 2005-2011. Most common in

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the sex-related cases as the most serious offense charged are forcible fondling (n = 352, 23.9%),
forcible rape (n = 322, 21.8%), statutory rape (n = 100, 6.8%), unclassified sex crimes (n = 98,
6.6%), forcible sodomy (n = 94, 6.4%), pornography and obscene material (n = 86, 5.8%),
intimidation and harassment (n = 52, 3.5%), online solicitation of a child (n = 44, 3.0%), and
prostitution (n = 42, 2.8%).
Victims of Sex-related Police Crime
Victim characteristics in sex-related police crime arrest cases in years 2005-2011 are
presented in Table 22. Victim information is particularly difficult to ascertain from news reports
and even official court records (when available) in criminal cases involving sex offenses. For
example, exact victim age is unknown (n = 661, 44.8%) due to missing data. We were, however,
able to determine whether the victim of a sex-related case was a child (birth to 17 years of age)
or an adult (ages 18 and older). Almost half of the known victims are children (n = 721, valid
52.4%). Most of the victims of sex-related police crime arrest cases are either an unrelated child
(n = 527, valid 39.3%) or an adult who is a stranger or nonstranger acquaintance (n = 658, valid
49.1%).
Predicting Conviction in Sex-related Police Crime Arrest Cases
The regression models in this section predict criminal conviction on any offense charged
in sex-related police crime arrest cases. Conviction data are available on two-thirds (n = 986,
66.8%) of the sex-related arrest cases. Of those sex-related cases with known criminal case
outcomes, most of the officers arrested for sex-related crimes were convicted (n = 791, 80.2%)
on at least one criminal offense charged against the arrested officer. Bivariate associations are
presented in Table 23. Chi-Square associations are statistically significant at p < .05 for 23
independent variables and the dependent variable, conviction on any offense charged. There are

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four bivariate associations of moderate strength as indicated by Cramer’s V scores for the
statistically significant Chi-Square associations. They are age of the victim, where χ2 (50, N =
541) = 86.980, p = .001, V = .401; the State where the arrested officer’s employing law
enforcement agency is located, where χ2 (48, N = 986) = 80.347, p = .002, V = .285; years of
service at time of arrest, where χ2 (34, N = 820) = 52.020, p = .025, V = .252; and victim’s
relationship to the arrested officer, where χ2 (7, N = 905) = 36,740, p < .001, V = .201.
Table 24 presents a backward stepwise binary logistic regression model predicting
conviction in sex-related police crime arrest cases. Bivariate correlations computed for each of
the independent variables in the model revealed that none of the variables were highly correlated
with each other. Multicollinearity is not a problem as indicated by no tolerance scores below
.905 and no variance inflation factors above 1.105. The Durbin-Watson score is 1.587 and
indicates that autocorrelation is not a problem. Logistic regression results indicate that the
overall model of four predictors is statistically reliable in distinguishing between conviction and
nonconviction in sex-related police crime arrest cases. Wald statistics indicate that all of the
independent variables in the binary logistic regression model significantly predict conviction in
sex-related police crime arrest cases. The regression model correctly classified 83.7% of the
2
= .346).
cases (AUC = .673, 95% CI [.621, .725], RROC

Context for prediction of conviction in sex-related police crime arrest cases is provided
by interpretation of odds ratios. The greatest predictor conviction is being charged with
pornography and obscene material offenses. The simple odds of conviction in sex-related police
crime arrest cases are 4.6 times greater if the arrested officer is charged with crimes involving
pornography and obscene material. Job loss also predicts conviction in sex-related cases as the
simple odds of conviction are 4.2 times greater if the final adverse employment action against the

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officer is job loss through either involuntary termination or voluntary resignation. Officers
arrested for sex-related crimes involving a child are more likely to be convicted than officers
whose victims are adults. The simple odds of conviction are 1.1 times greater in sex-related
police crime arrest cases where the victim is a child. Years of service at time of arrest is also a
slight predictor of conviction. The simple odds of conviction in sex-related police crime arrest
cases goes up by 5.8% for every one year increase in years of service at time of arrest.
Figure 9 presents the results of predicting conviction and included a total of 986 arrest
cases. The tree had an overall classification score of 81.9% (AUC = .712, 95% CI [.699, .754],
2
= .424) and selected the variable job lost as the splitting criterion. Officers who had lost
RROC

their job (node 1) were convicted in 83.8% of the cases. In contrast, officers who kept their job
(node 2) were convicted in 63.6% of the cases. The officers who lost their job in node 1 were
partitioned by the variable victim’s relationship to the offender. Cases that involved a victim
who was an unrelated child, a child or stepchild of the offender or former boy/girlfriend of the
offender had a conviction rate of 91.3%. In contrast, cases that involved a victim who was a
current spouse, other relative of the offender, stranger to the offender, current boy/girlfriend of
the offender, or ex-spouse of the offender had a conviction rate of 63.6%. The officers who kept
their job subsequent to being arrested in node 2 were partitioned by the variable age of victim.
Officers were convicted 56% of the time when the victim was 19 years of age or older and were
convicted 72% of the time when the victim was younger than 19 years of age. The tree also
included the following variables in tiers three: state, years of service, and rank.
Predicting Job Loss in Sex-related Police Crime Arrest Cases
This section presents the regression models predicting job loss as the final adverse
employment action taken against an officer following his or her arrest for a sex-related police

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crime. Chi-Square associations are presented in Table 25. Bivariate associations are statistically
significant at the p < .05 level for each of 35 independent variables and the dependent variable,
job lost. There are, however, only four bivariate associations of moderate strength as indicated
by the Cramer’s V scores for the statistically significant Chi-Square statistics. They are the year
when the officer was arrested, where χ2 (6, N = 1,475) = 135.487, p < .001, V = .303; the State
where the arrested officer’s employing law enforcement agency is located, where χ2 (48, N =
1,475) = 111.725, p < .001, V = .275; years of service at time of arrest, where χ2 (36, N = 1,195)
= 62.775, p = .004, V = .229; and officer age at time of his or her arrest, where χ2 (46, N = 1,397)
= 68.062, p = .019, V = .221.
Table 26 presents a backward stepwise binary logistic regression model predicting job
loss in sex-related police crime arrest cases. Bivariate correlations computed for each of the
independent variables in the regression model indicate that none of the variables are highly
correlated with each other. Multicollinearity is not a problem in the model as indicated by no
tolerance scores below .649 and no variance inflation factors above 1.541. The Durbin-Watson
score of 1.634 indicates that autocorrelation is not a problem. Logistic regression results suggest
that the overall model of nine predictors is statistically reliable for sex-related police crime arrest
cases in distinguishing between officers who kept their jobs after being arrested and officers who
lost their job. Wald statistics indicate that all of the independent variables in the model
significantly predict job loss in sex-related police crime arrest cases. The regression model
2
= .544).
correctly classified 82.6% of the cases (AUC = .772, 95% CI [.733, .810], RROC

Interpretation of odds ratios provides context for prediction of job loss in sex-related
police crime arrest cases. Six of the independent variables in the logistic regression model
predict when an officer is more likely to lose his or her job as a sworn law enforcement officer

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after being arrested for a sex-related police crime. The single largest predictor of job loss in sexrelated criminal cases is conviction on at least one of the charged offenses. The simple odds of
job loss are 2.8 times greater when the officer is convicted of a sex-related crime. Characteristics
of the sex-related crimes also predict job loss. Sex-related crimes are committed by officers both
on- and off-duty. The simple odds of job loss are 1.4 times greater if the sex-related crime was
committed by an officer in his or her official capacity as a sworn law enforcement officer.
Similarly, the simple odds of job loss are 1.7 times greater if the sex-related crime(s) for which
an officer was arrested involved a driving while female encounter. Officers arrested for sexrelated police crimes are prone to commit other types of police misconduct; the simple odds of
job loss are 1.2 times greater if at some point during the officer’s career he or she was named as
a party defendant in federal civil rights litigation pursuant to 42 U.S.C. §1983. Some of the
predictors in the model show when an officer is less likely to lose his job as a sworn law
enforcement officer subsequent to an arrest for committing a sex-related crime. Older officers
are less likely to lose their jobs; the simple odds of job loss go down by 3.5% for every one year
increase in the officer’s age at time of arrest. Oddly, the simple odds of job loss decrease by
85.5% if the sex-related crime for which the officer was arrested also involves marijuana.
Figure 10 presents the results of predicting job loss and included a total of 1,475 arrest
cases. The tree had an overall classification score of 78.8% (AUC = .786, 95% CI [.758, .813],
2
= .572) and selected the variable year of arrest as the splitting criterion. Officers who were
RROC

arrested before 2007 (node 1) lost their job in 55.5% of the cases. In contrast, officers who were
arrested after 2007 (node 2) lost their job in 82.7% of the cases. The officers arrested prior to
2007 in node 1 were partitioned by the variable officer was suspended. Officers who had been
previously suspended lost their job in 43.2% of the cases and officers who had not been

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suspended lost their jobs in 73% of the cases. The officers arrested after the year 2007 in node 2
were partitioned by the variable criminal conviction. Officers who were convicted lost their jobs
in 84.9% of the cases and officers who were not convicted lost their job in 71.3% of cases. The
tree also included the following variables in tiers three through five: being named as a 42 U.S.C.
§1983 civil defendant (at some point during an officer’s law enforcement career), urban/rural
continuum, geographic division, state, method of crime detection, citizen complaint as method of
crime detection, the number of part-time sworn personnel employed by the arrested officer’s
employing law enforcement agency, and the number of full-time sworn personnel employed by
the arrested officer’s employing law enforcement agency.
Predicting Child Victims in Sex-related Police Crime Arrest Cases
The next set of regression models predict child victims in sex-related police crime arrest
cases. Bivariate associations are presented in Table 27. Chi-Square calculations are statistically
significant at p < .05 for 54 independent variables and the dependent variable, child victim
(where adult victim = 0 and child victim = 1). Three of the bivariate associations are strong as
indicated by Cramer’s V scores where V > .40. They are crimes committed in the arrested
officer’s official capacity, where χ2 (1, N = 1,377) = 330.080, p < .001, V = .490; duty status at
time of commission of the crime, where χ2 (1, N = 1,377) = 323.355, p < .001, V = .485; and
crimes that are acts of police sexual violence, where χ2 (1, N = 1,377) = 290.300, p < .001, V =
.459. The bivariate associations of adult versus child victim are of moderate strength in 14
instances, including among others, statutory rape, where χ2 (1, N = 1,377) = 179.498, p < .001, V
= .361; the criminal offense of official misconduct, official oppression, or violation of oath,
where χ2 (1, N = 1,377) = 161.527, p < .001, V = .342, and driving while female encounters,
where χ2 (1, N = 1,377) = 142.850, p < .05, V = .322.

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A backward stepwise logistic regression model predicting child victims in sex-related
police crime arrest cases is presented in Table 28. Bivariate correlations computed for each of
the independent variables in the logistic regression model revealed that none of the variables are
highly correlated with each other. Multicollinearity is not a problem as indicated by no tolerance
scores below .449 and no variance inflation factors above 2.229. The Durbin-Watson score of
1.664 indicates that autocorrelation is not a problem. Logistic regression results indicate that the
overall model of 20 predictors is statistically reliable in distinguishing between sex-related police
crime arrest cases with adult victims and sex-related police crime arrest cases with child victims.
2
=
The model correctly classified 85.9% of the cases (AUC = .920, 95% CI [.902, .938], RROC

.844). Wald statistics indicate that all of the independent variables in the logistic regression
model significantly predict child victims in sex-related police crime arrest cases.
Odds ratio interpretations provide context for prediction of child victims in sex-related
police crime arrest cases. Eight of the 20 predictors in the logistic regression model indicate
when the victim of a sex-related police crime arrest case is more likely to be a child than an
adult. Certain types of sex crimes tend to involve child victims. The simple odds of the victim
being a child are 2.9 times greater if the arrested officer is charged with crimes involving
pornography and obscene material. Similarly, the simple odds of the victim being a child are 2.2
times greater if the arrested officer was charged with an unclassified sex crime (that is, a sexrelated criminal offense not specifically included in the coding protocol for this study) such as
promoting the sexual performance of a child, child enticement, corruption of a minor, or sexual
coercion. Crimes involving fondling often involve a child victim; the simple odds of the victim
being a child are 2.4 times greater if the arrested officer is charged with offenses relating to
forcible fondling. The gender of the victim also predicts child victims. The simple odds that the

113
victim is a child are more than five times greater if the victim is male. Sex-related police crime
arrest cases are more likely to result in a criminal conviction if the victim is a child; the simple
odds of the victim being a child were 1.2 times greater if the arrested officer is convicted on at
least one offense charged. Finally, the simple of odds of the victim being a child increased
17.2% if the crime for which an officer was arrested involved family violence.
Some of the independent variables in the logistic regression model predict when the
victim is less likely to be a child. The victims of sex-related police crime arrest cases are less
likely to be a child in cases where the arrested officer is employed by a large police department
in a metropolitan (that is, urban or suburban) county or independent city. The simple odds of the
victim being a child decrease by 17.7% for every one unit increase in the size of the arrested
officer’s employing law enforcement agency (on a ten-point scale) based on the number of sworn
personnel. Likewise, the simple odds of a child victim decrease by 17.6% for every one unit
increase in rurality (on a nine-point scale) based on the population of the county where the
arrested officer’s employing agency is located. Duty status at time of commission of the crime
for which an officer was arrested also predicts whether the victim of a sex-related police crime
arrest case is an adult or child. The simple odds of the victim being a child decrease by 83.4% if
the arrested officer was on-duty when the crime for which he was arrested was allegedly
committed. The on-duty sex-related crimes of the arrested officers are more likely to involve an
adult victim if the offenses are also violence-related crimes. For example, the simple odds of the
victim being a child decrease by 62.7% if the crime is an act of police sexual violence. The odds
ratios are similar if the arrested officer is charged with aggravated assault, kidnapping,
abduction, or false imprisonment, or intimidation / harassment in sex-related police crime arrest
cases.

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Figure 11 presents the results of predicting child victims for the sex-related cases and
included a total of 1,377 police crime arrest cases. The tree had an overall classification score of
2
79.8% (AUC = .835, 95% CI [.814, .857], RROC
= .670) and selected the variable official capacity

versus individual capacity as the splitting criterion. Officers operating in their individual
capacity (node 1) had child victims in 72.8% of the cases. In contrast, officers operating in an
official capacity (node 2) had child victims in 23.9% of the cases. The officers who were
operating in an individual capacity (node 1) were partitioned by the variable
kidnapping/abduction. Cases involving a kidnapping or abduction had child victims in 14.3% of
the cases. Cases that did not involve a kidnapping or abduction had child victims in 76% of the
cases. The officers who were operating in an official capacity (node 2) were partitioned by the
variable gender of victim. Cases that involved a female victim were also child victims in 19.4%
of the cases and cases with a male victim were also a child victim in 66.2% of the cases. The
tree also included the following variables in tier three through five: indecent exposure, violencerelated crime versus other crime type, driving while female encounter, state, and urban/rural
continuum.
Predicting Conviction in Police Sexual Violence Arrest Cases
This section presents regression models that predict criminal conviction on any offense
charged in sex-related police crime arrest cases involving police sexual violence. Conviction
data are available in more than two-thirds (n = 431, 69.3%) of the police sexual violence cases.
In the police sexual violence cases with known criminal case dispositions, the majority of the
arrest cases resulted in criminal conviction (n = 345, valid 80%) on at least one offense charged
against the arrested officer. Bivariate associations are presented in Table 29. Chi-Square
associations are statistically significant at p < .05 for 13 independent variables and the dependent

115
variable, conviction on any offense charged. Only three of the bivariate associations are of
moderate strength as indicated by Cramer’s V scores. They are job lost, where χ2 (1, N = 431) =
22.139, p < .001, V = .227; victim’s relationship, where χ2 (6, N = 427) = 20.988, p = .002, V =
.222; and year of arrest, where χ2 (6, N = 431) = 18.185, p = .006, V = .205.
Table 30 presents a backward stepwise binary logistic regression model predicting
conviction in sex-related police crime arrest cases involving acts of police sexual violence.
Bivariate correlations computed for each of the independent variables in the model revealed that
none of the variables were highly correlated with each other. Multicollinearity is not a problem
as indicated by no tolerance scores below .983 and no variance inflation factors above 1.018.
Autocorrelation is not a problem as indicated by the Durbin-Watson score of 1.562. Logistic
regression results indicate that the overall model of four predictors is statistically reliable in
distinguishing between conviction and nonconviction in police sexual violence arrest cases.
Wald statistics indicate that all of the independent variables in the model significantly predict
conviction in police sexual violence arrest cases. The binary logistic regression model correctly
2
= .422).
classified 80.8% of the cases (AUC = .711, 95% CI [.647, .774], RROC

Interpretation of the odds ratios provide context for prediction of criminal conviction in
police sexual violence arrest cases. Three of the four independent variables in the regression
model predict when conviction is more likely. Here again, sex-related arrest cases involving a
child victim are more likely to result in the arrested officer’s criminal conviction. The simple
odds of conviction are 2.7 times greater if the victim of a sex-related arrest case involving police
sexual violence is a child. The type of sex offense predicts whether an officer arrested for acts of
police sexual violence will be convicted of a crime. The simple odds of conviction are 2.1 times

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greater if the officer was arrested for forcible sodomy. Conversely, the simple odds of
conviction decrease by 54.1% if the officer was arrested for forcible fondling.
Figure 12 presents the results of predicting conviction in police sexual violent arrest cases
and included a total of 431 police crime arrest cases. The CHAID tree had an overall
2
classification score of 80.0% (AUC = .680, 95% CI [.615, .746], RROC
= .360) and selected the

variable job lost as the splitting criterion. Officers who had kept their job (node 1) were
convicted in 56.4% of the cases. In contrast, officers who lost their job (node 2) were convicted
in 83.5% of the cases. The officers who kept their job in node 1 were not partitioned by another
variable. The officers who lost this job in node 2 were partitioned by the variable forcible
fondling. Cases that did not involve forcible fondling resulted in conviction in 88.3% of the
cases and cases that involved forcible fondling resulted in conviction in 77.2% of the cases. The
tree also included the variable adult victim versus child victim in tier 3.
Predicting Job Loss in Police Sexual Violence Arrest Cases
Regression models predicting job loss as the final adverse employment outcome taken
against an officer following his or her arrest for sex-related crimes involving acts of police
sexual violence are presented in this section. Chi-Square associations are presented in Table 31.
Bivariate associations are statistically significant at p < .05 for each of 28 independent variables
and the dependent variable, job loss. Six of the bivariate associations are of moderate strength as
indicated by the Cramer’s V scores for the statistically significant Chi-Square statistics. They are
the State where the arrested officer’s employing law enforcement agency is located, where χ2
(45, N = 622) = 78.683, p = .001, V = .356; years of service at time of arrest, where χ2 (29, N =
495) = 61.583, p < .001, V = .353; year of arrest, where χ2 (6, N = 622) = 60.900, p < .001, V =
.313; internal crime against the organization, where χ2 (1, N = 622) = 60.900, p < .001, V = .290,

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conviction, where χ2 (1, N = 431) = 22.139, p < .001, V = .227; and years of service (categorical
variable), where χ2 (10, N = 622) = 25.372, p = .005, V = .202.
A backward stepwise binary logistic regression model predicting job loss in cases
involving acts of police sexual violence is presented in Table 32. Bivariate correlations for each
of the independent variables in the regression model indicate that none of the variables are highly
correlated with each other. Multicollinearity is not a problem in the model as indicated by no
tolerance scores below .966 and no variance inflation factors above 1.035. The Durbin-Watson
score of 1.562 indicates that autocorrelation is not a problem in the model. Logistic regression
results indicate that the overall model of six predictors is statistically reliable for cases of police
sexual violence in distinguishing between officers who kept their job after being arrested and
officers who lost their job. Wald statistics indicate that all of the independent variables in the
binary logistic regression model significantly predict job loss in sex-related police crime arrest
cases involving acts of police sexual violence. The regression model correctly classified 89.5%
2
= .470).
of the cases (AUC = .735, 95% CI [.651, .819], RROC

Context is provided for prediction of job loss in sex-related arrest cases involving police
sexual violence through interpretation of odds ratios. Four of the independent variable in the
logistic regression model predict when an officer is more likely to lose his or her job as a sworn
officer after being arrested for a sex-related crime involving an act of police sexual violence.
Conviction predicts job loss in police sexual violence arrest cases. The simple odds of job loss
are 4.7 times greater if the officer is convicted of at least one criminal offense charged in the case
involving police sexual violence. Predatory behavior in police sexual violence arrest cases also
predicts job loss. The simple odds of job loss are 2.2 times more likely if the police sexual
violence arrest case also involves a driving while female encounter. Police sexual violence cases

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are crimes against persons and rarely, if ever, constitute an internal crime against the
organization or a crime by the organization against the officer. As such, the simple odds of job
loss increase by 29.7% if the crime involving police sexual violence is neither an internal or
organizational crime. The simple odds of job loss in police sexual violence arrest cases increase
by 89.4% for every one unit categorical increase in the number of part-time sworn officers
employed by the same law enforcement agency as the arrested officer. Two of the independent
variables in the model predict when an officer is more likely to keep his or her job after being
arrested for a crime involving police sexual violence. The longer an officer has been employed
the less likely they are to lose their job. The simple odds of job loss in police sexual violence
arrest cases go down by 23% for every one unit (three year) categorical increase in years of
service at time of arrest. The years of service categorical variable groups years of service in
three-year increments. Also, the simple odds of job loss in police sexual violence arrest cases
decrease by 91.8% if the arrested officer is also charged with a weapons law offense.
Figure 13 presents the results of predicting job loss and included a total of 622 cases.
2
=
The tree had an overall classification score of 84.1%, (AUC = .794, 95% CI [.748, .840], RROC

.496) and selected the variable year of arrest as the splitting criterion. Officers who were
arrested before the year 2007 (node 1) lost their job in 60.2% of the cases. In contrast, officers
who were arrested after the year 2007 (node 2) lost their job in 86.4% of the cases. The officers
arrested prior to the year 2007 in node 1 were partitioned by the variable suspended for a period
of time. Officers who had been previously suspended lost their job in 48.4% of the cases and
officers who had not been suspended lost their jobs in 76.8% of the cases. The officers arrested
after the year 2007 in node 2 were partitioned by the variable criminal conviction versus
nonconviction. Officers who were convicted lost their jobs in 89.4% of the cases and officers

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who were not convicted lost their job in 72.1% of cases. The tree also included the following
variables in tiers three through four: geographic division and state.
Predicting Conviction in Driving While Female Police Crime Arrest Cases
Driving while female encounters are those incidents where a police officer initiates a
bogus traffic stop to harass, intimidate, and/or sexually assault female motorists. The regression
models in this section predict conviction in sex-related police crime arrest cases involving
driving while female encounters (N = 174). Criminal case final disposition data are available in
a majority of the driving while female cases (n = 126, 72.4%). Of those cases with known
criminal case outcomes, most of the officers were convicted (n = 103, valid 81.7%) on at least
one criminal offense charged against the arrested officer. Bivariate associations are presented in
Table 33. Chi-Square associations are statistically significant at p < .05 for 18 independent
variables and the dependent variable, conviction on any offense charged. The bivariate
association for conviction and the number of full-time sworn officers employed (categorical) is
strong, where χ2 (9, N = 126) = 22.232, p = .008, V = .420. There are eight bivariate associations
of moderate strength: age (categorical), where χ2 (8, N = 126) = 18.588, p = .017, V = .384; year
of officer’s arrest, where χ2 (6, N = 126) = 14.407, p = .025, V = .338; alcohol-related police
crime arrest cases, where χ2 (1, N = 126) = 9.895, p = .002, V = .280; burglary, where χ2 (1, N =
126) = 9.101, p = .003, V = .269; internal versus organizational crime, where χ2 (1, N = 126) =
7.204, p = .007, V = .239; sexual assault with an object, where χ2 (1, N = 126) = 6.081, p = .014,
V = .220; rank by function, where χ2 (1, N = 126) = 5.769, p = .016, V = .214; and, forcible
fondling, where χ2 (1, N = 126) = 5.277, p = .022, V = .205.
Table 34 presents a backward stepwise logistic regression model predicting conviction in
sex-related police crime arrest cases involving driving while female encounters. Bivariate

120
correlations computed for each of the independent variables in the model revealed that none of
the variables were highly correlated with each other. Multicollinearity is not a problem as
indicated by no tolerance scores below .962 and no variance inflation factors above 1.040. The
Durbin-Watson score is 1.700 indicating that autocorrelation is not a problem in the model.
Logistic regression results indicate that the overall model of five predictors is statistically
reliable in distinguishing between conviction and nonconviction in sex-related police crime
arrest cases involving a driving while female encounter. Wald statistics indicate that all of the
independent variables in the binary logistic regression model significantly predict conviction in
driving while female encounter cases. The regression model correctly classified 85.7% of the
2
= .662).
cases (AUC = .831, 95% CI [.741, .921], RROC

Context for prediction of conviction in sex-related police crime arrest cases involving
driving while female encounters is provided by interpretation of odds ratios. Two of the
independent variables in the model predict when the odds of conviction increase. The simple
odds of conviction in driving while female encounter arrest cases is 3.2 times greater if the
arrested police officer has been sued in federal court pursuant to 42 U.S.C. §1983 (civil action
for deprivation of rights) at some point during his or her law enforcement career. Driving while
female encounter arrest cases involve crimes against a person and do not involve crimes against
the organization or crimes by the organization against the officer. This is demonstrated where
the simple odds of conviction increase by 23% if the driving while female encounter arrest case
involves a crime against a person and is neither an internal or organizational crime. The other
three independent variables in the logistic regression model predict when the odds of conviction
decrease in sex-related police crime arrest cases involving driving while female encounters. The
simple odds of conviction in driving while female cases decrease by 87.1% if the officer is

121
charged with the criminal offense of sexual assault with an object. Similarly, the simple odds of
conviction decrease by 73.4% in driving while female cases if the arrested officer is charged with
the criminal offense of forcible fondling. Additionally, the simple odds of conviction in driving
while female encounter arrest cases decrease by 93.3% if the crime is also alcohol-related.
Figure 14 presents the results of predicting conviction and included a total of 126 cases.
2
The tree had an overall classification score of 81.7% (AUC = .736, 95% CI [.637, .835], RROC
=

.472) and selected the variable year of arrest as the splitting criterion. Officers who were
arrested in the year 2005 (node 1) were convicted in 66.7% of the cases. Officers who were
arrested during years 2006-2011 were convicted in 90.1% of the cases. The officers who kept
their job, in node 1, were not partitioned by another variable. The officers who lost their jobs, in
node 2, were partitioned by the variable 42 U.S.C. §1983 civil defendant at some point during an
officer’s law enforcement career. Cases that did not involve an officer who had been named as a
party-defendant pursuant to 42 U.S.C. §1983 in a federal court civil rights lawsuit (at some point
during his or her law enforcement career) resulted in conviction in 81.6% of the cases. In
contrast, cases that involved an officer who had at some point been sued pursuant to 42 U.S.C.
§1983 resulted in conviction in 97.7% of the cases.
Predicting Job Loss in Driving While Female Police Crime Arrest Cases
This section presents the regression models for predicting job loss as the final adverse
employment actions taken against an officer following his arrest for a sex-related police crime
arrest case involving a driving while female encounter. Chi-Square associations of job loss are
presented in Table 35. Four of the bivariate associations of job loss are strong as indicated by
the Cramer’s V score > .400. They are: the State where the officer’s employing law enforcement
agency is located, where χ2 (33, N = 174) = 53.896, p = .012, V = .557; victim age difference,

122
where χ2 (28, N = 174) = 51.795, p = .004, V = .546; officer’s years of service, where χ2 (22, N =
144) = 42.173, p = .006, V = .541; and age, where χ2 (33, N = 168) = 47.043, p = .054, V = .529.
There are also five bivariate associations of job loss that are of moderate strength: victim age
(categorical), where χ2 (7, N = 174) = 22.217, p = .002, V = .357; year of arrest, where χ2 (6, N =
174) = 15.536, p = .016, V = .299; rank, where χ2 (4, N = 174) = 14.145, p = .007, V = .285;
internal versus organizational crime, where χ2 (1, N = 174) = 13.514, p < .001, V = .279; and,
indecent exposure, where χ2 (1, N = 174) = 12.402, p < .001, V = .267.
Table 36 presents a backward stepwise binary logistic regression model predicting job
loss in sex-related police crime arrest cases involving driving while female encounters. There
are only two independent variables in the model predicting job loss. Neither of the independent
variables are highly correlated with each other. Multicollinearity is not a problem, but the
tolerance scores and variance inflation factors = 1 for both independent variables in the model.
Autocorrelation is not a problem as indicated by a Durbin-Watson score of 1.587. Logistic
regression results indicate that the overall model of two predictors is statistically reliable in
distinguishing between cases where officers who lost their jobs and officers who kept their jobs
after being arrested for sex-related police crimes involving a driving while female encounter.
Wald statistics indicated that the independent variables in the model significantly predict job
loss. The regression model correctly classified 93.3% of the cases (AUC = .766, 95% CI [.671,
2
= .532).
.860], RROC

Context for prediction of job loss in sex-related police crime arrest cases involving
driving while female encounters is provided by interpretation of odds ratios. The cases involving
driving while female encounters are crimes against persons. The simple odds of job loss go up
by 42.9% in driving while female encounter arrest cases when the underlying nature of the crime

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is not an internal crime against the organization nor a crime by the organization against the
officer. The difference in age between the age of the arrested officer and the age of the victim
also predicts job loss in these cases. The simple odds of job loss in sex-related police crime
arrest cases involving driving while female encounters go down by 6.9% for every one year
increase in the difference in age between the age of the arrested officer and the age of the victim.
Figure 15 presents the results of predicting job loss and included a total of 174 arrest
cases. The tree had an overall classification score of 84.5% (AUC = .776, 95% CI [.681, .870],
2
= .552) and selected the variable internal crime versus organizational crime as the splitting
RROC

criterion. Officers who committed an internal crime or crime against the organization (node 1)
lost their job in 73.4% of the cases. In contrast, officers committed a crime against a person
(node 2) lost their job in 93.7% of the cases. The officers who committed either an internal
crime or crime against the organization in node 1 were partitioned by the variable victim age
difference. Cases that involved a victim age difference that was unknown resulted in job loss in
84.6% of the cases. In contrast, cases that involved a known victim age difference (that is, the
difference in years between the age of the arrested officer and the age of the victim) resulted in
job loss in 51.9% of the cases. The officers who committed crimes against a person in node 2
were partitioned by the variable official misconduct. Officers who were not charged with the
crime of official misconduct lost their jobs in 97% of the cases and officers who were charged
with the crime of official misconduct lost their jobs in 86.2% of the cases.

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Part III: Alcohol-related Police Crime Data Set Models
There are 1,405 cases in the data set where sworn officers were arrested for alcoholrelated crimes during the years 2005-2011. The alcohol-related police crime arrest cases involve
1,283 individual officers employed by 814 nonfederal state, local, special, constable, and tribal
law enforcement agencies located in 564 counties and independent cities in all 50 states and the
District of Columbia. Of these, 81 of the arrested officers had more than one case ( X = 1.10,
Mdn = 1.00, Mode = 1, SD = .452), because they had more than one victim (one criminal case
per victim) and/or were arrested for an alcohol-related crime on more than one occasion during
the study period years.
More than one-third of the alcohol-related arrest cases involve an officer who is known to
have lost his or her job (n = 533, 37.9%) subsequent to being arrested. The known final
employment outcomes in the alcohol-related arrest cases include those in which there was no
known adverse employment action taken against the arrested officer (n = 260, 18.5%), cases
resulting in the arrested officer being suspended for a period of time (n = 612, 43.6%), cases in
which the arrested officer was separated through voluntary resignation (n = 263, 18.7%), and
cases in which the arrested officer was separated through involuntary termination (n = 270,
19.2%). Relatively few of the alcohol-related DUI arrest cases are known to have resulted in
criminal conviction (n = 492, 35.0%, valid 75.2%) on at least one criminal offense charged
against the arrested officer.
Included in the alcohol-related police crime arrest cases are 960 driving under the
influence (DUI) arrests during the years 2005-2011. The alcohol-related police DUI arrest cases
involve 924 individual officers employed by 621 nonfederal state, local, special, constable, and
tribal law enforcement agencies located in 464 counties and independent cities in all 50 states

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and the District of Columbia. Of these, 27 of the arrested officers had more than one alcoholrelated DUI case ( X = 1.04, Mdn = 1.00, Mode = 1, SD = .256), because they had more than
one victim (one criminal case per victim) and/or were arrested for an alcohol-related DUI on
more than one occasion during the study years while employed as a sworn law enforcement
officer.
Less than one-third of the officers arrested for DUI are known to have lost their job (n =
303, 31.6%) after being arrested. The known final employment outcomes in the alcohol-related
DUI arrest cases include those cases in which there was no adverse employment action taken
against the arrested officer (n = 202, 21.0%), cases resulting in the arrested officer being
suspended for a period of time (n = 455, 47.4%), cases in which the arrested officer was
separated through voluntary resignation (n = 167, 17.4%), and those cases in which the arrested
officer was separated through involuntary termination (n = 136, 14.2%). Less than one-third of
the alcohol-related DUI arrest cases resulted in criminal conviction (n = 303, 31.6%, valid
80.2%). The final disposition in the criminal cases is unknown (n = 582, 60.6%), however, in
many of the DUI cases.
Officers Arrested for Alcohol-related Police Crime, Offenses Charged & Employers
Table 37 presents information on the alcohol-related arrest cases in terms of the arrested
officers and their employing nonfederal law enforcement agencies. The majority of the alcoholrelated cases involve male officers (n = 1,314, 93.5%) who were arrested. The youngest officer
arrested for an alcohol-related crime was age 20 at time of arrest, and the oldest arrested officer
was 66 years old ( X age = 36.70, Mdn age = 37.00, Mode age = 38, SD = 8.379 years). Most of
the alcohol-related arrest cases involve sworn officers employed in patrol or street-level
nonsupervisory positions, including officers, deputies, troopers, and detectives (n = 1,138,

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81.0%). Most of the alcohol-related cases involved crimes committed while the officer was offduty (n = 1,235, 87.9%). The majority of the alcohol-related cases involve arrests made by some
other law enforcement agency (n = 1,009, 71.8%) and were not arrests made by the agency
where the arrested officer was employed.
Most of the alcohol-related arrest cases involve officers employed by municipal police
departments (n = 1,028, 73.2%) or sheriff’s offices (n = 214, 15.2%). Officers arrested for
alcohol-related crimes were also employed by primary state police agencies (n = 74, 5.3%),
county police departments (n = 54, 3.8%), special police departments (n = 24, 1.7%), tribal
police departments (n = 7, 0.5%), or other types of nonfederal law enforcement agencies. The
modal category for agency size by number of full-time sworn personnel is 1,000 or more sworn
officers (n = 399, 28.4%). Most of the employing law enforcement agencies were located in a
nonrural metropolitan county or independent city (n = 1,212, 86.3%). The employing law
enforcement agencies are located throughout the United States, including in Southern states (n =
548, 39.0%), Midwestern states (n = 358, 25.5%), Northeastern states (n = 309, 22.0%), and
Western states (n = 190, 13.5%).
Table 38 presents the alcohol-related arrest cases in terms of the most serious offense
charged. There are 38 separate offense categories representing the most serious offense charged
in alcohol-related police crime arrest cases during the years 2005-2011. Most common are DUI
(n = 817, 58.1%), simple assault (n = 149, 10.6%), aggravated assault (n = 103, 7.3%), weapons
law violations (n = 47, 3.3%), forcible fondling (n = 27, 1.9%), disorderly conduct (n = 27,
1.9%), murder and nonnegligent manslaughter (n = 26, 1.9%), forcible rape (n = 26, 1.9%), and
destruction of property / vandalism (n = 23, 1.6%).

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Victims of Alcohol-related Police Crime
Table 39 presents victim characteristics in alcohol-related police crime arrest cases
during years 2005-2011. Many of the arrest cases—including most of the DUI cases not
involving traffic accidents and injuries—do not involve crimes with victims. Where victim
information is relevant and available in alcohol-related arrest cases, the victims are most often
female (n = 263, valid 52.1%) and adults age 18 or older (n = 495, valid 87.8%). Most of the
known victims are either strangers to the arrested officer or nonstranger acquaintances (n = 382,
valid 69.1%). Sometimes alcohol-related police crime arrest cases involve victims who are also
police officers (n = 58, 4.1%, valid 10.4%).
Incident Events in Police DUI Arrest Cases
Table 40 presents the DUI arrest cases in terms of DUI event incidents and
characteristics. Some of the arrest cases involved officers who were DUI while actually on-duty
in a police vehicle (n = 42, 4.4%), in a police vehicle while out of the officer’s jurisdiction (n =
28, 2.9%), or off-duty in a take-home police vehicle (n = 78, 8.1%). Some of the arrest cases
involved officers who refused to perform field sobriety tests (n = 81, 8.4%) when stopped for
suspicion of DUI and/or refused to consent to a blood-alcohol content (BAC) test (n = 195,
20.3%). Many of the police DUI arrest cases involved traffic accidents (n = 492, 51.2%), often
resulting in victim injuries (n = 231, 24.1%) and fatalities (n = 39, 4.1%). Some of the DUIrelated traffic accidents happened when an intoxicated officer flipped his or her car (n = 33,
3.4%) or crashed into another vehicle causing the other vehicle to flip (n = 4, 0.4%). Some
inebriated officers fled the scene (n = 103, 10.7%) after being involved in a DUI-related traffic
accident, and many of those officers were subsequently criminally charged with vehicular hitand-run (n = 76, 7.9%). A few of the officers were involved in a DUI-related traffic accident

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while attempting to flee and evade the police (n = 10, 1.0%). Other officers were arrested for
DUI after causing a traffic accident while driving in the wrong direction on a roadway (n = 17,
1.8%) or denied being the driver after being involved in a DUI-related traffic accident (n = 12,
1.3%). Several of the traffic accidents involved an officer who was drunk while driving a
motorcycle (n = 15, 1.6%). A few of the DUI-related traffic accidents occurred while the drunk
officer was also engaged in family violence (n = 10, 1.0%)
Drugs in Police DUI Arrest Cases
Although there is a separate part reporting drug-related arrest cases, police DUI arrest
cases that are drug-related (n = 49, 5.1% of the DUI cases) are reported in this section. The
specific drugs involved in the drug-related DUI arrest cases are presented in Table 41. The most
common drugs include “other” depressants (that is, depressants other than GHB and
benzodiazepines, such as Ambien) (n = 15, 30.6%), oxycodone (n = 8, 16.3%), cocaine (n = 5,
10.2%), hydrocodone (n = 4, 8.2%), “other” narcotics (e.g., fentanyl, Demoral, methadone,
Darvon, etc.) (n = 4, 8.2%), benzodiazepines (n = 4, 8.2%), amphetamine / methamphetamine (n
= 3, 6.1%), and marijuana (n = 3, 6.1%). The drug-related DUI arrest cases include officers
arrested for DUI while on-duty (n = 16, 32.7%), although the majority of the drug-related DUI
cases involved an arresting agency that was not the officer’s employer (n = 34, 69.4%).
Predicting Conviction in Alcohol-related Police Crime Arrest Cases
The regression models in this section predict criminal conviction on one or more offenses
charged against an arrested officer in alcohol-related police crime arrest cases versus
nonconviction on any offense charged. Bivariate Chi-Square associations are statistically
significant at p < .05 for 25 independent variables and the dependent variable, conviction on any
offense charged. See Table 42. There are two bivariate associations of moderate strength as

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indicated by Cramer’s V scores. They are the state where the arrested officer’s employing law
enforcement agency is located, where χ2 (48, N = 654) = 95.625, p < .001, V = .382; and victim’s
relationship to the arrested officer, where χ2 (7, N = 331) = 23.297, p = .002, V = .265.
Table 43 presents a backward stepwise binary logistic regression model predicting
conviction in alcohol-related police crime arrest cases. Bivariate correlations computed for each
of the independent variables in the logistic regression model revealed that none of the variables
were highly correlated with each other. Multicollinearity is not a problem as indicated by no
tolerance scores below .849 and no variance inflation factors above 1.177. Autocorrelation is
also not a problem as indicated by a Durbin-Watson score of 2.027. Logistic regression results
indicate that the overall model of six predictors is statistically reliable in distinguishing between
conviction in alcohol-related police crime arrest cases and nonconviction in alcohol-related
police crime arrest cases. The model correctly classified 77.0% of the cases (AUC = .688, 95%
2
= .376). Wald statistics indicate that all of the independent variables in the
CI [.641, .734], RROC

binary logistic regression model significantly predict conviction in alcohol-related police crime
arrest cases.
Context for prediction of conviction in alcohol-related police crime arrest cases is
provided by interpretation of odds ratios. Four of the independent variables in the model predict
when conviction in alcohol-related cases is more likely than not. The simple odds of conviction
in an alcohol-related arrest cases are 2.6 times greater if the arrested officer was arrested for DUI
while driving a personally-owned vehicle (that is, not a police vehicle). The simple odds of
conviction in alcohol-related arrest cases increase by 78.1% if the officer was arrested by a law
enforcement agency that is not his or her employing agency. The simple odds of conviction in
alcohol-related police crime arrest cases are two times more likely if the crime(s) for which the

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officer was arrested is also sex-related. Final adverse employment outcomes also predict
conviction in alcohol-related arrest cases. The simple odds of conviction in alcohol-related arrest
cases are 1.5 times greater if the arrested officer ultimately loses his or her job as a sworn law
enforcement officer after being arrested. Two of the independent variables in the model predict
when conviction in alcohol-related police crime arrest cases is less likely to occur. The simple
odds of conviction in an alcohol-related case go down by 93% if the crime(s) for which the
officer was arrested involved a driving while female encounter. The simple odds of conviction
in alcohol-related arrest cases goes down by 63.8% if the crime(s) for which the officer was
arrested constituted officer-involved domestic violence where the officer used his or her hands or
fist as a weapon.
Figure 16 presents the results of predicting conviction and included a total of 654 police
crime arrest cases. The tree had an overall classification score of 76.8% (AUC = .721, 95% CI
2
= .442) and selected the variable victim’s relationship to the offender as the
[.673, .768], RROC

splitting criterion. Cases that involved victims who were a stranger, unrelated child, ex-spouse,
former boy/girlfriend, or a child/stepchild (node 1) received convictions in 77.5% of the cases.
In contrast, cases that involved victims who were a current spouse, current boy/girlfriend, or
relative of the offender (node 2) received convictions in 54.7% of the cases. Cases in node 1
were further partitioned by the variable job lost. Officers who had not lost their job were
convicted in 71.3% of the cases and officers who had lost their job were convicted in 83% of the
cases. Cases in node 2 were not further partitioned. The tree also included the following
variables in tiers three through four: state and geographic division.

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Predicting Job Loss in Alcohol-related Police Crime Arrest Cases
The next set of models predict job loss in alcohol-related police crime arrest cases. ChiSquare bivariate associations presented in Table 44 are statistically significant at p < .05 for 53
independent variables and the dependent variable, job loss. The strength of the statistically
significant bivariate association is weak in 50 of the 53 predictors of job loss in alcohol-related
arrest cases. There is a moderate bivariate association in the alcohol-related cases between job
loss and each of the following three independent variables: the difference in years between the
age of the arrested officer and the victim, where χ2 (67, N = 1,405) = 125.538, p < .001, V = .299;
the state where the arrested officer’s employing law enforcement agency is located, where χ2 (50,
N = 1,405) = 106.935, p < .001, V = .276; and, victim age (categorical), where χ2 (9, N = 1,405)
= 59.634, p < .001, V = .206.
Table 45 presents a backward stepwise binary logistic regression model predicting job
loss in the aftermath of an officer’s arrest for an alcohol-related crime. Bivariate correlations
computed for each of the independent variables in the logistic regression model revealed that
none of the variables in the model were highly correlated with each other. None of the tolerance
statistics were below .806 and none of the tolerance statistics were above 1.240. The DurbinWatson score of 1.782 indicates that there is no autocorrelation in the model. Logistic regression
results indicate that the multivariate model of seven predictors is statistically reliable in
distinguishing between officers who kept their job as a sworn law enforcement officer after being
arrested for an alcohol-related crime and those officers who lost their job as a sworn officer
subsequent to being arrested through either voluntary resignation or involuntary termination as a
final adverse employment outcome. The logistic regression model correctly classified 71.6% of
2
= .322). Wald statistics indicate that all of the
the cases (AUC = .661, 95% CI [.620, .702], RROC

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independent variables in the binary logistic regression model significantly predict whether an
officer will keep or lose his or her job as a sworn law enforcement officer after being arrested for
an alcohol-related crime.
Context for prediction of job loss in alcohol-related police crime arrest cases is provided
by interpretation of the odds ratios in the binary logistic regression model. Five of the seven
independent variables in the model predict when an officer is more likely to lose his or her job as
a sworn officer after being arrested for an alcohol-related crime. The simple odds of job loss in
alcohol-related arrest cases are 5.5 times greater if at some point during the officer’s law
enforcement career, the officer was named as a party-defendant in a civil rights lawsuit that was
originally filed in a state trial court and removed by the civil defendant to a United States District
Court pursuant to 28 U.S.C. § 1441. If the alcohol-related crime for which an officer was
arrested is also violence-related, then the simple odds of job loss are 1.4 times greater than if the
officer’s alcohol-related crime was not also violence-related. Similarly, the simple odds of job
loss are 2.5 times greater if an officer was arrested for an alcohol-related crime that was also sexrelated. Duty status also predicts job loss in alcohol-related police crime arrest cases, as the
simple odds of job loss are 1.9 times greater if the officer was arrested for an alcohol-related
crime that occurred while the officer was on-duty. As with many of the other prediction models,
conviction predicts job loss. The simple odds of job loss in alcohol-related arrest case are 1.6
times greater if an arrested officer is ultimately convicted of at least one of the criminal offenses
for which they were prosecuted in a criminal court. Two of the independent variables in the
logistic regression model predict a decrease in the likelihood of job loss following an arrest for
an alcohol-related police crime. If an officer was reassigned from one position to another
position in the officer’s employing law enforcement agency subsequent to being arrested for an

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alcohol-related crime, then the simple odds of job loss decrease by 84.9%. Also, the method of
crime detection predicts job loss in alcohol-related arrest cases, as the simple odds of job loss
decrease by 65.1%.
Figure 17 presents the results of predicting job loss and included a total of 1,405 cases.
2
The tree had an overall classification score of 68.3% (AUC = .710, 95% CI [.683, .738], RROC
=

.420) and selected the variable sex-related crime versus other crime type as the splitting criterion.
Officers who were not involved in a sex-related case (node 1) lost their job in 35.5% of the cases.
In contrast, officers involved in a sex-related case (node 2) lost their job in 75.6% of the cases.
The officers who were convicted in node 1 were partitioned by the variable number of full-time
sworn personnel. Officers in police departments from 1-99 sworn officers lost their job in 44.7%
of the cases and officers in departments from 100-1000+ sworn officers lost their job in 29.8% of
the cases. The cases in node 2 were not partitioned further. The tree also included the following
variables in tiers three through five: organization versus against citizenry, victim age categorical,
years of service categorical, geographic division, case disposition, officer was convicted of a
crime, and state.
Predicting Conviction in Police DUI Arrest Cases
This section includes regression models predicting criminal conviction in an arrest case
on at least one offense charged against an officer arrested for driving under the influence (DUI).
Bivariate Chi-Square associations presented in Table 46 are statistically significant at p < .05 for
22 independent variables and the dependent variable, conviction on any offense charged. The
statistically significant bivariate association is strong for the State where the arrested officer’s
employing law enforcement agency is located, where χ2 (46, N = 378) = 85.253, p < .001, V =
.475. The statistically significant bivariate association is moderate for Geographic Division,

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where χ2 (8, N = 378) = 25.595, p = .001, V = .260, as well as Geographic Region, where χ2 (3, N
= 378) = 18.376, p < .001, V = .220. The statistically significant bivariate associations are weak
for each of the other 19 independent variables and the dependent variable.
Table 47 presents a backward stepwise binary logistic regression model predicting
conviction in police DUI arrest cases. Bivariate correlation computations for each of the
independent variables in the model revealed that none of the variables were highly correlated
with each other. None of the tolerance scores were below .936 and none of the variance inflation
factors were above 1.068, indicating that multicollinearity is not a problem in the model.
Autocorrelation is not a problem as indicated by the Durbin-Watson score of 2.155. Binary
logistic regression results indicate that the overall model of six predictors is statistically reliable
in distinguishing between conviction and nonconviction in police DUI arrest cases. The model
2
= .410). Wald
correctly classified 81.7% of the cases (AUC = .705, 95% CI [.639, .771], RROC

statistics indicate that all of the independent variables in the logistic regression model
significantly predict conviction in police DUI arrest cases.
Interpretation of the odds ratios provide context for prediction of conviction in police
DUI arrest cases. Two of the independent variables in the logistic regression model predict when
conviction is more likely than not in police DUI cases. The simple odds of conviction are 1.3
times greater if the arrested officer was involved in a DUI-related traffic accident resulting in his
or her arrest. Once again, job loss predicts conviction, as the simple odds of conviction are 1.4
times greater if the arrested officer ultimately loses his or her job as a final adverse employment
action in the aftermath of being arrested for DUI. The four other independent variables in the
model predict when criminal conviction in a police DUI arrest case is less likely to occur. If an
officer arrested for DUI was also charged with a liquor law violation, then the simple odds of

135
conviction on at least one criminal offense charged decreased by 94.5%. The simple odds of
conviction decreased by 61.3% if the officer was acting in his or her official capacity as a sworn
law enforcement officer when arrested for DUI. In cases where an officer denied driving after
being involved in a DUI-related traffic accident, the simple odds of conviction decreased by
94.3%. In some instances, officers who were arrested for DUI refused to submit to a bloodalcohol content (BAC) test. The simple odds of conviction decreased by 53.9% when an officer
arrested for DUI refused to submit to a BAC test.
Figure 18 presents the results of predicting conviction and included a total of 390 cases.
2
=
The tree had an overall classification score of 80.3% (AUC = .747, 95% CI [.684 .809], RROC

.494) and selected the variable geographic division as the splitting criterion. Cases from East
North Central, West North Central, and East South Central geographic divisions (node 1) had
convictions in 92.6% of the cases. In contrast, cases from New England, South Atlantic, Pacific,
Middle Atlantic, Mountain, and West South Central divisions (node 2) had convictions in 72.6%
of the cases. The cases in node 1 were not partitioned further. The cases in node 2 were
partitioned by the variable state. Cases in Massachusetts, Florida, South Carolina, North
Carolina, Pennsylvania, Rhode Island, Washington, Texas, New Hampshire, Oregon, New
Mexico, Connecticut, and Montana had convictions in 55.7% of the cases. Cases in California,
New York, New Jersey, Virginia, West Virginia, Colorado, Arkansas, Nevada, Maryland,
Georgia, Maine, Arizona, Idaho, Utah, Oklahoma, Hawaii, Delaware, Vermont, and the District
of Columbia had convictions in 88.1% of the cases. The tree did not have any additional
variables.

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Predicting Job Loss in Police DUI Arrest Cases
The next set of models predict job loss in police DUI arrest cases. Chi-Square bivariate
associations presented in Table 48 are statistically significant at the p < .05 level for 29
independent variables and the dependent variable, job loss. There is a moderate bivariate
association between job loss and each of the following three independent variables: the State in
which the arrested officer’s employing law enforcement agency is located, where χ2 (50, N =
960) = 107.344, p < .001, V = .334; the difference in years between the age of the arrested officer
and the age of the victim, where χ2 (54, N = 960) = 80.423, p = .011, V = .289; and, Geographic
division, where χ2 (8, N = 960) = 38.273, p < .001, V = .200.
A backward stepwise binary logistic regression model predicting job loss in police DUI
arrest cases is presented in Table 49. Bivariate correlations computed for each of the
independent variables in the logistic regression model revealed that none of the variables in the
model were highly correlated with each other. None of the tolerance scores were below .841 and
none of the tolerance scores were above 1.190, indicating that multicollinearity is not a problem.
The Durbin-Watson score of 1.975 indicates that there is no autocorrelation in the model. Binary
logistic regression results indicate that the multivariate model of eight predictors is statistically
reliable in distinguishing between officers who kept their jobs and officers who lost their jobs
after being arrested for DUI. The logistic regression model only correctly classified 66.9% of
2
= .436). Wald statistics indicate that all of the
the cases (AUC = .718, 95% CI [.666, .770], RROC

independent variables in the logistic regression model significantly predict whether an officer
will keep or lose his or her job after being arrested for DUI.
Interpretation of odds ratios provides context for prediction of job loss as a final adverse
employment action against an officer after being arrested for DUI. Six of the independent

137
variables in the logistic regression model predict when an officer is more likely than not to lose
his or her job after being arrested for DUI. The simple odds of job loss are 7.5 times greater if an
officer was involved in a DUI-related traffic accident after driving in the wrong direction on a
roadway. If an officer was charged with murder or nonnegligent manslaughter in a DUI arrest
case, the simple odds of job loss are 2.5 times greater. The simple odds of job loss are 1.9 times
greater if the officer was acting in his or her official capacity when arrested for DUI. If the DUI
arrest case incident was also violence-related, then the simple odds of job loss are 1.4 times
greater. Here again, conviction predicts job loss, as the simple odds of job loss are 1.3 times
greater if an officer arrested for DUI was ultimately convicted of one or more criminal offenses
arising out of the incident. Experienced police officers are more likely to lose their jobs after
being arrested for DUI than are inexperienced officers, as the simple odds of job loss increased
by 4.3% for every one unit categorical (3 year) increase in years of service at time of arrest. Two
of the independent variables in the logistic regression model predict when job loss is less likely
to occur subsequent to an officer’s arrest for DUI. The simple odds of job loss decreased by
13.8% for every one unit categorical increase in the number of full-time sworn personal
employed by the arrested officer’s employing law enforcement agency. Some law enforcement
agencies require their officers to be available at all times, 24/7. The simple odds of job loss
subsequent to an officer’s off-duty DUI arrest decreased by 89.5% if the officer’s employing
agency requires 24/7 availability.
Figure 19 presents the results of predicting job loss and included a total of 991 arrest
cases. The tree had an overall classification score of 70.7% (AUC = .679, 95% CI [.642, .715],
2
= .358) and selected the variable geographic division as the splitting criterion. Officers
RROC

from New England, West North Central, South Atlantic, and Mountain division states (node 1)

138
lost their job in 41.8% of the cases. In contrast, officers from East North Central, Pacific, Middle
Atlantic, West South Central, and East South Central divisions (node 2) lost their job in 25.7% of
the cases. Cases in node 1 were partitioned by the variable state. Officers from Minnesota,
Connecticut, Rhode Island, West Virginia, Montana, Nevada, North Dakota, Maryland,
Wyoming, Maine, Iowa, Kansas, Delaware, Idaho, and Nebraska lost their job in 19.8% of the
cases. Officers from Massachusetts, Florida, South Carolina, Colorado, Georgia, New Mexico,
North Carolina, Virginia, Arizona, South Dakota, New Hampshire, Missouri, District of
Columbia, Utah, and Vermont lost their job in 48.5% of the cases. Cases in node 2 were
partitioned by the variable state. Officers from; Illinois, Texas, Michigan, Tennessee,
Pennsylvania, Oklahoma, and Alabama lost their job in 37.4% of the cases. Officers from Ohio,
Indiana, California, New York, Louisiana, Wisconsin, New Jersey, Kentucky, Washington,
Arkansas, Oregon, Mississippi, Alaska, and Hawaii lost their job in 18.4% of the cases. The tree
also included the following variables in tiers three though five: age (categorical) and state.

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Part IV: Drug-related Police Crime Data Set Models
The data set includes 739 cases in which sworn law enforcement officers were arrested
for drug-related crimes during the time period January 1, 2005, through December 31, 2011.
The drug-related arrest cases involve 665 individual sworn officers employed by 432 nonfederal
state, local, special, constable, and tribal law enforcement agencies located in 323 counties and
independent cities in 45 states and the District of Columbia (all except Alaska, Maine, Montana,
North Dakota, and Vermont). Of these, 44 (6.6%) of the arrested officers have more than one
case ( X = 1.11, Mdn = 1.00, Mode = 1, SD = .724) because they have more than one victim
(one criminal case per crime victim) and/or were arrested for a drug-related crime on more than
one occasion.
The majority of drug-related arrest cases involved an officer who is known to have
ultimately lost their job (n = 520, 70.4%) as a sworn law enforcement officer as a result of being
arrested. The known final employment outcomes in the drug-related cases include those in
which there was no known adverse employment action (n = 64, 8.7%), where the officer was
suspended (n = 155, 21.0%) for a period of time, where the officer was separated through
voluntary resignation (n = 220, 29.8%), and those cases where the officer was separated through
involuntary termination (n = 300, 40.6%). Approximately two-thirds of the drug-related police
crime arrest cases resulted in a criminal conviction against the arrested officer (n = 485, 65.6%)
on at least one offense.
Officers Arrested for Drug-related Police Crime, Offenses Charged & Employing Agencies
Table 50 presents information on the drug-related arrest cases in terms of the arrested
officers and their employing nonfederal law enforcement agencies. Most of the drug-related
cases involve male officers (n = 701, 94.9%). The modal category for known officer age at time

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of arrest is 36-39 years of age (n = 136, 18.4%). The youngest officer was age 20 at time of
arrest, and the oldest officer was 76 years old ( X age = 37.18, Mdn age = 36, Mode age = 28,
SD = 8.182 years). The modal category for known years of service at time of arrest is three to
five years (n = 116, 15.7%). Most of the drug-related arrest cases involve sworn officers
employed in patrol or street-level rank including nonsupervisory officers, deputies, troopers, and
detectives (n = 629, 85.1%). Other drug-related cases involve arrested line and field supervisors
(n = 74, 10.0%) (i.e., corporals, sergeants, and lieutenants), as well as police executives and
managers (n = 36, 4.9%) (i.e., captains, majors, deputy chiefs and chief deputies, and chiefs and
sheriffs). Well over half of the drug-related arrest cases involve crimes that were committed
while on-duty (n = 444, 60.1%) working in official capacity as a sworn officer. Many of the
drug-related cases (n = 527, 71.3%) involve an officer who was arrested by a law enforcement
agency other than the arrested officer’s employing nonfederal law enforcement agency.
Most of the drug-related arrest cases involve sworn officers employed by municipal
police departments (n = 527, 71.3%) or sheriff’s offices (n = 139, 18.8%). Officers arrested for
drug-related crimes were also employed by primary state police agencies (n = 23, 3.1%), county
police departments (n = 25, 3.4%), special law enforcement agencies (n = 20, 2.7%), constable
agencies (n = 4, 0.5%), and a tribal police department (n = 1, 0.1%). The modal category for
agency size by number of sworn officers employed is 1,000 or more full-time sworn officers (n =
236, 31.9%) and zero part-time sworn officers (n = 551, 74.6%). Most of the officers arrested in
drug-related criminal cases were employed by a law enforcement agency located in a nonrural
metropolitan county or independent city (n = 611, 82.7%). The employing law enforcement
agencies are located throughout the United States, including in the Southern states (n = 346,

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46.8%), Northeastern states (n = 154, 20.8%), Midwestern states (n = 145, 19.6%), and Western
states (n = 94, 12.7%).
Table 51 presents the drug-related cases in terms of the most serious offense charged.
There are 37 separate offense categories represented as the most serious offense charged in drugrelated police crime arrest cases during the years 2005-2011. Most common in the drug-related
cases as the most serious offense charged are drug offenses (n = 308, 41.7%), robbery (n = 60,
8.1%), driving under the influence (n = 38, 5.1%), unclassified theft/larceny offenses (n = 33,
4.5%), theft from a building (n = 28, 3.8%), and burglary (n = 26, 3.5%).
Specific Drugs and Classes of Drugs in the Drug-related Police Crime Arrest Cases
Table 52 presents information on the specific drugs as well as classes of drugs in the
drug-related police crime arrest cases from years 2005-2011. In some cases it was not possible
to determine specific drugs, or even classes of drugs, involved in the criminal behavior resulting
in an officer’s arrest for a drug-related crime. All classes of commonly abused drugs are found,
except inhalants, in the drug-related police crime arrest cases, including stimulants (n = 308,
41.7%), narcotics, (n = 197, 26.7%), cannabis (n = 177, 24.0%), depressants (n = 45, 6.1%),
anabolic steroids (n = 44, 5.9%), and hallucinogens (n = 23, 3.1%). Some of the drug-related
arrest cases involve two classes of drugs (n = 105, 14.2%), three classes of drugs (n = 22, 3.0%),
or four classes of drugs (n = 13, 1.8%).
Twenty specific drugs were mentioned in the case article narratives and/or court records
of drug-related arrest cases analyzed in the current study. The most common drugs in these
cases are cocaine (n = 233, 31.5%), marijuana (n = 177, 24.0%), oxycodone (n = 89, 12.0%),
hydrocodone (n = 69, 9.3%), crack (n = 58, 7.8%), amphetamine and methamphetamine (n = 53,
7.2%), heroin (n = 50, 6.8%), unclassified narcotics (n = 29, 3.9%), anabolic steroids not

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including testosterone (n = 29, 3.9%), unclassified depressants (n = 26, 3.5%), benzodiazepines
(n = 19, 2.6%), and testosterone (n = 17, 2.3%). Some of the drug-related arrest cases involve
four or more specific drugs (n = 22, 3.0%), three specific drugs (n = 35, 4.7%), two specific
drugs (n = 136, 18.4%), or one specific drug (n = 415, 56.2%). In some drug-related cases it was
not possible to determine any specific drug (n = 131, 17.7%).
Victims of Drug-related Police Crime
Victim characteristics in drug-related police crime arrest cases in years 2005-2011 are
presented in Table 53. Victim information was not ascertainable in many of the drug-related
police crime arrest cases. As such, missing data limits the findings as to victims of the drugrelated cases. Over half of the known victims of drug-related police crime are male (n = 57,
52.3%). The modal category for known victim age is 20-24 (n = 8). Most of the known victims
were adults (n = 129, 90.2%) and almost none of the victims of drug-related police crime were
also known to be police officers (n = 4, 2.9%). The relationship of the victim and the arrested
officer could not be determined in most of the drug-related arrest cases (n = 601, 81.3%). Of
known victims, most are strangers or nonstranger acquaintances (n = 112, 81.2%) to the arrested
officer.
Predicting Patterns of Drug-related Corruption in Police Crime Arrest Cases
Table 54 presents patterns of drug-related corruption in police crime arrest cases during
the years 2005-2011. The arrested officers were charged with drug-related crimes that involved
a variety of recognized patterns of drug corruption, including drug trafficking (n = 299, 40.5%),
personal use of drugs (n = 235, 31.8%), crimes that facilitate the drug trade (n = 172, 23.3%),
drug-related shakedowns and thefts (n = 171, 23.1%), drug thefts from police evidence rooms (n
= 60, 8.1%), drug-related falsification (n = 58, 7.8%), forged prescriptions (n = 33, 4.5%),

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planting evidence (n = 33, 4.5%), and crimes that involve sexually-motivated drug corruption (n
= 25, 3.4%). Drug-related shakedowns included thefts from street-level drug dealers (n = 75),
thefts during warrantless searches (n = 69), thefts during traffic stops of cars and from drug
couriers (n = 58), off-duty robberies (n = 35), illegitimate raids and searches (n = 33), and
shakedowns and thefts during dispatched calls for service (n = 16). Some of the drug-related
police crime arrest cases did not involve any patterns of drug corruption (n = 59, 8.0%). The
largest number of the drug-related cases included one pattern (n = 316, 42.8%) or two patterns (n
= 192, 26.0%) of drug corruption. A smaller percentage of the cases involved three patterns (n =
72, 9.7%), four patterns (n = 56, 7.6%), or five or more patterns (n = 44, 5.9%) of drug
corruption criminal activities.
Some of the drug-related police crime arrest cases also involved other types of police
crime, including those that are also profit-motivated (n = 438, 59.3%), violence-related (n = 132,
17.9%), alcohol-related (n = 38, 5.1%), and/or sex-related (n = 37, 5.0%). Many of the drugrelated police crime arrest cases also involve at least one other type of police crime (n = 513,
69.4%).
CART procedures were utilized to identify the causal pathways between the statistically
significant variables to create classification estimates for six of the most prevalent forms of
misconduct including drug trafficking, the three most prevalent types of theft/shakedown, drug
use, and facilitation of the drug trade. Cocaine was the strongest predictor for three of the six
decision trees where specific drugs were the independent variables. The CART analysis also
identified numerous other drugs that all significantly contributed to classification estimates
beyond the splitting criterion. The following drugs were represented in nodes below the splitting
criterion: hydrocodone, heroin, marijuana, crack, anabolic steroids (other than testosterone),

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phencyclidine and analogs, oxycodone, and cocaine. The CART models presented in Table 55
had varying levels of predictive power AUC of .685 to .792. The models that examined drug
trafficking and facilitation of the drug trade had poor predictive power. In contrast, the models
that examined various forms of theft/shakedown had the highest levels of predictive power. The
tree predicting theft/shakedowns of street level dealers had the highest predictive power (AUC =
2
.792, 95% CI [.740, .844], RROC
= .584). Cocaine was identified as the strongest predictor and,

therefore, was selected as the splitting criterion for the data. The remaining branches of the tree
were based on the responses to the splitting criterion. Marijuana and heroin were statistically
significant, but only in cases where cocaine was present.
A second set of decision tree analyses predicted the various patterns of corruption using
drug classes instead of specific drugs is also presented in Table 55. Stimulants were selected as
the splitting criterion, and were the strongest predictor for facilitation of the drug trade, drug
trafficking, and shakedowns and thefts from car stops and drug couriers. Narcotics were the
strongest predictor for drug use. Cannabis were the strong predictor for shakedowns and thefts
from warrantless searches/seizures and street level dealers. CART trees that utilized drug classes
as predictors had a large range of predicted power. The tree predicting facilitation of the drug
trade had a low AUC score of .654, suggesting that the tree is barely able to predict 50% of the
cases correctly. The trees predicting the specific types of shakedowns each have AUC scores
that exceed 0.75. The strongest tree predicted thefts/shakedowns from street-level dealers based
on cannabis as the strongest predictor and splitting variable (AUC = .755, 95% CI [.697, .814],
2
= .510).
RROC

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Predicting Conviction in Drug-related Police Crime Arrest Cases
The regression models in this section predict criminal conviction on one or more offenses
charged in drug-related police crime arrest cases versus nonconviction on any offense.
Conviction data are available on approximately three-fourths of the drug-related cases (n = 562,
76.0%). Of those, most of the officers arrested in a drug-related case were convicted (n = 485,
valid 86.3%) on at least one criminal offense charged in the case. Bivariate Chi-Square
associations are statistically significant at the p < .05 level for 28 independent variables and the
dependent variable, conviction on any offense charged. See Table 56. There are four bivariate
moderate associations as indicated by the Cramer’s V scores for the statistically significant ChiSquare statistics. They are victim relationship to the arrested officer, where χ2 (6, N = 129) =
15.398, p = .017, V = .345; the state where the arrested officer’s employing agency is located,
where χ2 (43, N = 562) = 65.220, p = .016, V = .341; the number of years difference in age
between the arrested officer’s age at time of arrest and the age of their victim, where χ2 (25, N =
562) = 63.847, p < .001, V = .337; and, the age of the victim (in a categorical age recoded
variable), where χ2 (9, N = 562) = 47.832, p < .001, V = .292.
Table 57 presents a backward stepwise binary logistic regression model predicting
conviction in drug-related police crime arrest cases. Bivariate correlations computed for each of
the independent variables in the logistic regression model revealed that none of the variables
were highly correlated with each other. Multicollinearity is not a problem as indicated by no
tolerance scores below .928 and no variance inflation factors above 1.077. Autocorrelation is
also not a problem as indicated by a Durbin-Watson score of 1.799. Logistic regression results
indicate that the overall model of five predictors is statistically reliable in distinguishing between
conviction in drug-related police crime arrest cases and nonconviction in drug-related police

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crime arrest cases. The model correctly classified 85.3% of the cases (AUC = .849, 95% CI
2
[.761, .937], RROC
= .698). Wald statistics indicate that all of the independent variables in the

binary logistic regression model significantly predict conviction in drug-related police crime
arrest cases.
Odds ratio interpretations provide context for prediction of conviction on one or more
criminal offenses charged in drug-related police crime arrest cases. The simple odds of
conviction are 3.5 times greater if the officer ultimately loses their job as a sworn law
enforcement officer subsequent to an arrest for a drug-related crime. Conversely, the simple
odds of conviction decrease by 87.3% if the officer was suspended from their job as a sworn law
enforcement officer after being arrested in a drug-related criminal case. If an officer is charged
with obstruction of justice in a drug-related criminal case, the simple odds of conviction decrease
by 81.8%. It is possible in drug-related cases where an officer is charged with obstruction of
justice that criminal courts are skeptical of the officer’s criminality.
The relationship of the victim in a drug-related police crime arrest case also impacts on
whether an arrested officer will be convicted of at least one criminal offense charged in the case.
The simple odds of conviction increase by 60.1% for every one step increase in our categorical
variable of victim relationship. In that eight-category variable, 1 = victim is current spouse of the
arrested officer, and 8 = stranger or nonstranger acquaintance to the arrested officer. Thus, an
officer is more likely to be convicted in a drug-related police crime arrest case the more distant
the relationship is between the arrested officer and their victim in a drug-related crime. The
simple odds of conviction decrease by 9.1% for every one step increase in the categorical age of
the victim in a drug-related police crime arrest case. That is to say, generally, as victim age goes

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up, the likelihood of conviction of one or more criminal offenses charged in a drug-related police
crime case goes down.
Figure 20 presents the results of predicting conviction and included a total of 562 arrest
cases. The tree had an overall classification score of 86.3% (AUC = .760, 95% CI [.703, .816],
2
= .520) and selected the variable officer was suspended as the splitting criterion. Officers
RROC

who had been previously suspended (node 1) were convicted in 80.9% of the cases. In contrast,
officers who were not previously suspended (node 2) were convicted in 48.9% of the cases. The
officers who had been previously suspended in node 1 were partitioned by the variable job loss.
Officers who lost their job were convicted in 85.7% of the cases and officers who did not lose
their job were suspended in only in 67.9% of the cases. The officers who had not been
previously suspended in node 2 were partitioned by the variable profit-motivated police crime
versus other crime. Officers who were involved in a profit-motivated case were convicted in
96.4% of the cases and officers who were not involved in a profit-motivated case were convicted
in 86.1% of cases. The tree also included the following variables in tiers two through four:
stimulants, arresting agency, drug/narcotic violation, and drugs: selling, dealing, or trafficking.
Predicting Job Loss in Drug-related Police Crime Arrest Cases
In this section, the models predict job loss in the drug-related police crime arrest cases.
More than two-thirds of drug-related arrest cases are known to have resulted in the arrested
officer losing his or her job (n = 520, 70.4%) as a sworn law enforcement officer subsequent to
being arrested. Chi-Square bivariate associations are statistically significant at the p < .05 level
for 41 independent variables and the dependent variable, job loss. See Table 58. The strength of
the statistically significant bivariate association is, however, weak in 38 of the 41 predictors.
There is a moderate association that is statistically significant between job loss and officer’s age

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at time of arrest, where χ2 (45, N = 739) = 62.818, p = .041, V = .303; year of officer’s arrest
(during the study period of officers who were arrested during the years 2005 through 2011),
where χ2 (6, N = 739) = 56.885, p < .001, V = .277; and, difference in years between the age of
the arrested officer at time of arrest and the age of the victim in a drug-related police crime arrest
case, where χ2 (26, N = 739) = 39.386, p = .045, V = .231.
Table 59 presents a backward stepwise binary logistic regression model predicting job
loss subsequent to an officer’s arrest for a drug-related crime. Bivariate correlations computed
for each of the independent variables in the logistic regression model revealed that none of the
variables in the model were highly correlated with each other. None of the tolerance statistics
are below .892, and none of the variance inflation factors are higher than 1.121, indicating that
multicollinearity is not a problem. The Durbin-Watson score of 1.791 indicates that there is no
autocorrelation in the model. Logistic regression results indicate that the multivariate model of
nine predictors is statistically reliable in distinguishing between officers who kept their job after
being arrested for a drug-related crime and officers who lost their jobs after being arrested
through either involuntary termination or voluntary resignation. The model correctly classified
2
78.7% of the cases (AUC = .817, 95% CI [.776, .859], RROC
= .634). Wald statistics indicate that

all of the independent variables in the model significantly predict whether an officer lost or kept
their job as a sworn law enforcement officer after being arrested for a drug-related police crime.
Predictors of the final adverse employment outcomes in the drug-related police crime
arrest cases are aided by interpretation of the odds ratios. Five of the independent variables
predict when an officer is more likely to lose his or her job as a sworn law enforcement officer
after being arrested for a drug-related crime. The simple odds of job loss are 5.6 times greater if
the arrested officer has been sued (at some point during their law enforcement career) in a state

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court for civil rights violations and the civil action was removed from state court to a federal
district court pursuant to 28 U.S.C. §1441 because the lawsuit raised a federal question. These
are cases where a cause of action arose under 42 U.S.C. §1983 (civil action for deprivation of
civil rights under the color of law) that were originally filed in a state trial court and later
removed by defense counsel to a United States District Court because the claim involves a
federal statute or constitutional tort. Almost one-third of the drug-related police crime arrest
cases (n = 232, 31.4%) involve an officer who has been sued in federal court pursuant to 42
U.S.C. §1983 at some point during their police career. The class of drug involved in the arrest
case also impacts on the final adverse employment outcome (job loss). The simple odds of job
loss are almost 3.3 times greater if the drug-related police crime arrest case involved a narcotic.
Also, the level of rurality in the location of the arrested officer’s employer predicts job loss. The
simple odds of job loss after being arrested for a drug-related police crime are approximately 1.6
times greater if the employing law enforcement agency is located in a nonmetropolitan (that is,
rural) county or independent city.
Four of the independent variables in the logistic regression model predict when an officer
is more likely to keep his or her job subsequent to being arrested for a drug-related police crime.
The simple odds of job loss decrease by 73.8% if the drug-related crime is also alcohol-related.
Certain crimes predict a decrease in likelihood of job loss following an officer’s arrest for a drugrelated crime. The simple odds of job loss decrease by 82.1% if the officer is charged with
counterfeiting and/or forgery offenses. Similarly, the simple odds of job loss decrease by 69.4%
if the drug-related police crime case includes an allegation of falsification by the arrested officer.
If the arrested officer’s chief is under scrutiny as a result of the drug-related police crime case,
then the simple odds of job loss for the arrested officer go down by 62.8%.

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Figure 21 presents the results of predicting job loss and included a total of 739 arrest
cases. The tree had an overall classification score of 71.3% (AUC = .733, 95% CI [.695, .770],
2
= .466) and selected the variable year of arrest as the splitting criterion. Officers who were
RROC

arrested before the year 2007 (node 1) lost their job in 54.0% of the cases. In contrast, officers
who were arrested after 2007 (node 2) lost their job in 78.7% of the cases. The officers arrested
prior to the year 2007 in node 1 were not partitioned by any variable. The officers arrested after
the year 2007 in node 2 were partitioned by the variable criminal conviction. Officers who were
convicted lost their jobs in 81.1% of the cases and officers who were not convicted lost their job
in 57.4% of cases. The tree also included the following variables in tiers three through six:
profit-motivated crime versus other crime type, officer was suspended for a period of time, year
of arrest, and age.

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Part V: Violence-related Police Crime Data Set Models
There are 3,328 cases in the data set where nonfederal sworn law enforcement officers
were arrested for violence-related crimes during the years 2005-2011. The violence-related
arrest cases involve 2,586 individual officers employed by 1,445 nonfederal state, local, special,
constable, tribal, and regional law enforcement agencies located in 805 counties and independent
cities in 49 states (all except Wyoming) and the District of Columbia. Of those, 407 of the
arrested officers had more than one case ( X = 1.29, Mdn = 1.00, Mode = 1, SD = 1.044),
because they had more than one victim (one criminal case per victim) and/or were arrested for a
violence-related crime on more than one occasion during the study period years of 2005-2011.
More than half of the violence-related arrest cases involve an officer who is known to
have lost his or her job (n = 1,748, 52.5%) subsequent to being arrested. The known final
employment outcomes in the violence-related arrest cases include those in which there was no
known adverse employment action taken against the arrested officer (n = 419, 12.6%), cases
resulting in the arrested officer being suspended from his or her job for a period of time (n =
1,161, 34.9%), cases in which the arrested officer was separated through voluntary resignation (n
= 767, 23.0%), and cases in which the arrested officer was separated through involuntary
termination of employment (n = 981, 29.5%). Less than half of the violence-related arrest cases
are known to have resulted in criminal conviction of the arrested officer (n = 1,316, 39.5%) on at
least one criminal offense, although the conviction rate is higher (66.3%) in the cases where the
criminal case disposition is known.
Included in the violence-related police crime cases are a subset of 961 officer-involved
domestic violence arrest cases. The officer-involved domestic violence cases involve 849
individual officers employed by 604 nonfederal state, local, special, tribal, and regional law

152
enforcement agencies located in 436 counties and independent cities in 49 states (all except
Wyoming) and the District of Columbia. Of these, 89 of the arrested officer had more than one
officer-involved domestic violence arrest case ( X = 1.13, Mdn = 1.00, Mode = 1, SD = .449),
because they had more than one victim and/or were arrested for a crime involving officerinvolved domestic violence (while still employed as a sworn law enforcement officer) on more
than one occasion during the years 2005-2011.
Just over one-third of the officer-involved domestic violence arrest cases involve an
officer known to have lost his or her job (n = 360, 37.5%) as a sworn law enforcement officer
after being arrested. The known final employment outcomes in the officer-involved domestic
violence arrest cases include those in which there was no known adverse employment action
taken against the arrested officer (n = 148, 15.4%), cases that resulted in the arrested officer
being suspended from his or her job for a period of time (n = 453, 47.1%), cases in which the
arrested officer was separated from his or her employing law enforcement agency through
voluntary resignation (n = 150, 15.6%), and cases in which the arrested officer was separated
through involuntary termination of employment (n = 210, 21.9%).
Officers Arrested for Violence-related Police Crime, Offenses Charged & Employers
Table 60 presents information on the violence-related police crime arrest cases in terms
of the arrested officers and their employing law enforcement agencies. Most of the violencerelated cases involve male officers (n = 3,194, 96.0%) who were arrested. The youngest officer
arrested for a violence-related crime was age 19 at time of arrest, and the oldest officer was 74
years old ( X age = 36.00, Mdn age = 36.00, Mode age = 35, SD = 8.232 years). Most of the
violence-related arrest cases involve sworn officers employed in patrol or street-level
nonsupervisory positions (n = 2,797, 84.0%). Less than two-thirds of the violence-related police

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crime arrest cases involved crimes committed while the officer was off-duty (n = 2,155, 64.8%).
Similarly, less than two-thirds of the violence-related arrest cases involve arrests effectuated by
some other law enforcement agency (n = 2,100, 63.1%) and were not arrested by representatives
of the agency where the arrested officer was employed.
Most of the violence-related police crime arrest cases involved officers employed by
municipal police departments (n = 2,504, 75.2%) or sheriff’s offices (n = 523, 15.7%). Officers
arrested for violence-related crimes were also employed by primary state police agencies (n =
118, 3.5%), county police departments (n = 90, 2.7%), special police departments (n = 77, 2.3%),
constable agencies (n = 6, 0.2%), tribal police departments (n = 9, 0.3%), or a regional police
department (n = 1, 0.0%). The modal category for agency size by number of full-time sworn
personnel is 1,000 or more sworn officers (n = 987, 29.7). Most of the employing law
enforcement agencies were located in a nonrural metropolitan county or independent city (n =
2,880, 86.5%). The agencies employing officers arrested for violence-related police crime are
located throughout the United States, including in Southern states (n = 1,386, 41.6%),
Northeastern states (n = 715, 21.5%), Midwestern states (n = 664, 20.0%), and Western states (n
= 563, 16.9%).
Table 61 presents the violence-related police crime arrest cases in terms of the most
serious offense charged. There are 40 separate criminal offense categories representing the most
serious offense charged in each of the violence-related cases during the years 2005-2011. Most
common offenses are simple assault (n = 870, 26.4%), aggravated assault (n = 570, 17.1%),
forcible fondling (n = 352, 10.6%), forcible rape (n = 322, 9.7%), intimidation and harassment (n
= 200, 6.0%), murder and nonnegligent manslaughter (n = 104, 3.1%), unclassified offenses (n =

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99, 3.0%), forcible sodomy (n = 94, 2.8%), robbery (n = 92, 2.8%), and criminal deprivation of
civil rights (n = 61, 1.8%).
Victims of Violence-related Police Crime
Table 62 presents victim characteristics in violence-related police crime arrest cases
during the years 2005-2011. More than half of the cases involve a crime victim who is female (n
= 1,841, 55.3%, known 62.1%). Most of the victims are adults (n = 2,416, 72.6%, known
78.1%) and are not law enforcement officers (n = 2,912, 87.5%, known 93.8%). Slightly more
than half of the victims are strangers or nonstranger acquaintances (n = 1,668, 50.1%, valid
54.3%) to the officer who was arrested. Some of the victims of violence-related police crime
arrest cases were family members of the arrested officer, including current spouse (n = 336,
10.1%), former spouse (n = 59, 1.8%), child or stepchild (n = 159, 4.8%), or some other relative
(n = 84, 2.5%). Other victims include a child unrelated to the arrested officer (n = 451, 13.6%),
current girlfriend or boyfriend (n = 195, 5.9%) and former girlfriend or boyfriend (n = 118,
3.5%).
Predicting Conviction in Violence-related Police Crime Arrest Cases
In this section the regression models predict conviction in violence-related police crime
arrest cases. Bivariate Chi-Square associations presented in Table 63 are statistically significant
at p < .05 for 75 independent variables and the dependent variable, conviction on any offense
charged. Five of the bivariate associations are of moderate strength as indicated by the Cramer’s
V score, including job loss, where χ2 (1, N = 1,984) = 254.985, p < .001, V = .358; victim age,
where χ2 (73, N = 962) = 96.014, p = .037, V = .316; the State in which the arrested officer’s
employing law enforcement agency is located, where χ2 (49, N = 1,984) = 98.550, p < .001, V =

155
.223; sex-related, where χ2 (1, N = 1,984) = 85.756, p < .001, V = .208; and, the relationship of
the victim to the arrested officer, where χ2 (7, N = 1,855) = 77.089, p < .001, V = .204.
Table 64 presents a backward stepwise binary logistic regression model predicting
conviction in violence-related police crime arrest cases. Computation of bivariate correlations
for each of the independent variables in the model revealed that none of the variables were
highly correlated with each other. Multicollinearity is not a problem as indicated by no tolerance
scores below .779 and no variance inflation factors above 1.285. The Durbin-Watson score of
1.748 indicates that autocorrelation is not a problem. Logistic regression results indicate that the
overall model of ten predictors is statistically reliable in distinguishing between conviction and
nonconviction in violence-related police crime arrest cases. The binary logistic regression model
2
= .482). Wald
correctly classified 77.1% of the cases (AUC = .741, 95% CI [.718, .764], RROC

statistics indicate that all of the independent variables in the model significantly predict criminal
conviction in violence-related police crime arrest cases.
Interpretation of the odds ratios in the logistic regression model provide context for
prediction of conviction in violence-related police crime arrest cases. Nine of the independent
variables in the regression model predict when conviction is more likely to occur when an officer
was charged with one or more violence-related criminal offenses. The serious nature of the
crimes for which officers were arrested impacts on criminal conviction. The simple odds of
conviction are more than 11 times greater if the arrested officer was charged with either burglary
or driving under the influence while driving a personally-owned vehicle. Similarly, the simple
odds of conviction are eight times greater if the officer was arrested for pornography or obscene
material. The simple odds of conviction in violence-related police crime arrest cases are 2.9
times greater if the officer was arrested for criminal deprivation of civil rights, and 1.7 times

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greater if the arrested officer was charged with forcible sodomy. If the violence-related crime for
which an officer was arrested is also sex-related, then the simple odds of conviction are 87%
more likely than if the crime was not sex-related. Also, the level of an officer’s experience as a
sworn law enforcement officer predicts conviction because it is a factor considered by the courts
in criminal case adjudication. The simple odds of conviction in violence-related cases go up by
4.9% for every one unit categorical (three year) increase in years of service as a sworn officer at
time of the officer’s arrest. The simple odds of conviction in violence-related police crime arrest
cases are 6.3 times greater if the underlying incident was officer-involved domestic violence
resulting in fatal injuries to the victim. As with most other prediction models in this study, job
loss predicts criminal conviction, as the simple odds of conviction in violence-related police
crime arrest cases is 4.5 times greater if the officer ultimately lost his or her job as a sworn law
enforcement officer after being arrested. There is one independent variable in the logistic
regression model that predicts when the odds of criminal conviction decrease; the simple odds of
conviction in violence-related cases decrease by 8.3% for every one categorical unit increase in
the number of full-time sworn personnel employed by the arrested officer’s employing law
enforcement agency. The odds of conviction in violence-related police crime arrest cases go
down as the size of the arrested officer’s employing agency goes up.
Figure 22 presents the results of predicting conviction and included a total of 654 police
officers. The tree had an overall classification score of 76.8% (AUC = .721, 95% CI [.673,
2
= .442) and selected the variable victim’s relationship to the offender as the splitting
.768], RROC

criterion. Cases that involved victims who were a stranger or nonstranger acquaintance,
unrelated child, ex-spouse, former boyfriend or girlfriend, or a child/stepchild (node 1) to the
offender resulted in a conviction in 77.5% of the cases. In contrast, cases that involved victims

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who were a current spouse, current boyfriend or girlfriend, or some other relative of the offender
(node 2) resulted in a conviction in 54.7% of the cases. Cases in node 1 were further partitioned
by the variable job loss. Officers who lost their job were convicted in 83% of the cases and
officers who did not lose their job were convicted in only in 71.3% of the cases. Cases in node 2
were not partitioned further. The tree also included the following variables in tiers two through
four: geographic region and state.
Predicting Job Loss in Violence-related Police Crime Arrest Cases
The next set of regression models predict job loss in violence-related police crime arrest
cases. Chi-Square bivariate associations presented in Table 65 are statistically significant at p <
.05 for 90 independent variables and the dependent variable, job loss. The strength of the
statistically significant bivariate associations is moderate for each of 8 independent variables and
job loss: conviction, where χ2 (1, N = 1,984) = 254.985, p < .001, V = .358; sex-related, where χ2
(1, N = 3,328) = 284.771, p < .001, V = .293; victim age, where χ2 (76, N = 1,463) = 100.641, p =
.031, V = .224; victim’s relationship to the arrested officer, where χ2 (7, N = 3,070) = 153.358, p
< .001, V = 224; police sexual violence, where χ2 (1, N = 3.328) = 151.504, p < .001, V = .213;
Year of arrest, where χ2 (49, N = 3,328) = 151.504, p < .001, V = .210; the difference in years
from the age of the arrested officer to the age of the victim, where χ2 (99, N = 3,328) = 140.586,
p = .004, V = 206; and, forcible fondling, where χ2 (1, N = 3,328) = 137.226, p < .001, V = .203.
Table 66 presents a backward stepwise binary logistic regression model predicting job
loss as a final adverse employment action against an officer after being arrested for a violencerelated crime. Bivariate correlations for each of the independent variables in the model indicate
that none of the variables are highly correlated with each other. Multicollinearity is not a
problem as indicated by no tolerance scores below .572 and no variance inflation factors above

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1.747. Autocorrelation is not a problem as indicated by a Durbin-Watson score of 1.715.
Logistic regression results indicate that the overall model of eleven predictors is statistically
reliable in distinguishing between officers who kept their job after being arrested and officers
who lost their job after being arrested in a violence-related arrest case. Wald statistics indicate
that all of the independent variables in the model significantly predict job loss in violence-related
cases. The binary logistic regression model correctly classified 79.5% of the cases (AUC = .768,
2
= .536).
95% CI [.744, .791], RROC

Context for prediction of job loss in violence-related police crime arrest cases is provided
through interpretation of the odds ratios. Five of the independent variables in the logistic
regression model predict when an officer is more likely to lose his or her job as a sworn law
enforcement officer after being arrested for a violence-related criminal offense. The year of the
arrest (within the time period years, 2005-2011) predicts job loss, but this is likely an artifact of
the data and potentially resulting from one of several threats to internal validity. The simple
odds of job loss are 2.8 times greater if the violence-related crime for which the officer was
arrested involved police sexual violence. The relationship of the arrested officer to his or her
victim in violence-related police crime arrest cases predicts job loss, as the simple odds of job
loss increase by 24.4% for every one unit categorical increase on the eight-point scale of victim
relationship (but it is a nominal level variable, so the practical interpretation is imprecise). On
that variable (V85 on the coding instrument) the categories are 1 = victim is current spouse, 2 =
victim is former spouse, 3 = victim is current girlfriend or boyfriend, 4 = victim is former
girlfriend or boyfriend, 5 = victim is child or stepchild, 6 = victim is other relative, 7 = victim is
an unrelated child, and 8 = victim is a stranger or nonstranger acquaintance. As for most of the
other prediction models in this study, conviction predicts job loss and job loss predicts

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conviction. Here, the simple odds of job loss are 6 times greater in violence-related cases if the
arrested officer is ultimately convicted in criminal court of at least one of the offenses charged
against the officer. Some of the officers in the current study who were arrested were also named
as a party-defendant in one or more federal court civil actions alleging civil rights violations at
some point during an officer’s career as a sworn law enforcement officer. In this model, the
simple odds of job loss in violence-related arrest cases are 2.4 times greater if the arrested officer
was sued (at some point during the officer’s law enforcement career) in a state court civil action
that was removed to a United States District Court pursuant to 28 U.S.C. §1441.
Six of the independent variables in the logistic regression model predict when job loss is
less likely to occur in violence-related police crime arrest cases. As age goes up, the likelihood
of job loss goes down: the simple odds of job loss go down by 10% for every one categorical
unit (3 year) increase in the age of the officer at time of his or her arrest for a violence-related
crime. The officer’s status at time of committing the crime for which he or she was arrested also
predicts job loss. The simple odds of job loss decrease by 48.9% if the officer was acting in his
or her official capacity as a sworn law enforcement officer when he or she committed the
violence-related crime for which the officer was arrested. The employing agency’s response also
predicts, in terms of adverse employment actions short of job loss after an officer’s arrest, when
job loss is unlikely. The simple odds of job loss decrease by 70.6% if an officer was reassigned
to another position within the agency after being arrested for a violence-related crime. Similarly,
the simple odds of job loss decrease by 76.8% if an officer was suspended from his or her job for
a period of time after being arrested for a violence-related crime. Interestingly, the simple odds
of job loss go down by 94.2% if the violence-related arrest case also involved the drug cannabis.
The sex of the victim in a violence-related police crime arrest case also predicts job loss, as the

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simple odds of job loss in a violence-related case goes down by 43.6% if the victim is male (and
not a female). Conversely stated, job loss subsequent to an arrest for a violence-related police
crime is more likely if the crime victim is a woman or girl than a man or boy.
Figure 23 presents the results of predicting job loss and included a total of 3,328 cases.
2
The tree had an overall classification score of 68.5% (AUC = .756, 95% CI [.740, .772], RROC
=

.512) and selected the variable criminal conviction as the splitting criterion. Officers who were
convicted (node 1) lost their job in 55.6% of the cases. In contrast, officers who were not
convicted (node 2) lost their job in 41.7% of the cases. The officers who were convicted in node
1 were partitioned by the variable sex-related police crime versus other crime type. Officers
arrested in a sex-related case lost their job in 75.1% of the cases and officers arrested for crimes
that were not sex-related lost their job in 45.4% of the cases. The officers who were not
convicted in node 2 were partitioned by the variable sex-related versus other crime type. Here,
officers arrested for a sex-related case lost their job in 68.1% of the cases and officers arrested
for crimes that were not sex-related lost their job in 34.1% of the cases. The tree also included
the following variables in tiers three through five: year, officer was suspended for a brief period
of time, 42 U.S.C. §1983 civil defendant, duty status, simple assault, victim age categorical, age
(of the officer), and years of service categorical.
Predicting Conviction in Officer-involved Domestic Violence Arrest Cases
The regression models in this section predict conviction in officer-involved domestic
violence arrest cases. Bivariate Chi-Square associations presented in Table 67 are statistically
significant at p < .05 for 26 independent variables and the dependent variable, conviction on any
offense charged. There was a strong bivariate association between conviction and the State in
which the arrested officer’s employing law enforcement agency is located, where χ2 (46, N =

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497) = 80.273, p = .001, V = .402. There was a moderate bivariate association between
conviction and each of the following independent variables: job loss, where χ2 (1, N = 497) =
62.928, p < .001, V = .356; sex-related, where χ2 (1, N = 497) = 34.222, p < .001, V = .356;
simple assault, where χ2 (1, N = 497) = 29.509, p < .001, V = .244; victim age (categorical),
where χ2 (9, N = 497) = 28.296, p = .001, V = .239; victim’s relationship to the arrested officer,
where χ2 (7, N = 480) = 27.127, p < .001, V = .238; geographic division, where χ2 (8, N = 497) =
24.938, p = .002, V = .224; and, forcible fondling, where χ2 (1, N = 497) = 24.159, p < .001, V =
.220.
Table 68 presents a backward stepwise binary logistic regression model predicting
conviction in officer-involved domestic violence arrest cases. Bivariate correlations computed
for each of the independent variables in the model revealed that none of the variables were
highly correlated with each other. Multicollinearity is not a problem as indicated by no tolerance
scores below .775 and no variance inflation factors above 1.290. Autocorrelation is not a
problem as indicated by a Durbin-Watson score of 1.956. Logistic regression results indicate
that the overall model of ten predictors is statistically reliable in distinguishing between criminal
conviction and nonconviction in officer-involved domestic violence arrest cases. Wald statistics
indicate that all of the independent variables in the model significantly predict conviction in
officer-involved domestic violence arrest cases. The logistic regression model correctly
2
= .640).
classified 76.9% of the cases (AUC = .820, 95% CI [.782, .858], RROC

Interpretation of the odds ratios provides context for prediction of conviction on at least
one criminal offense charged against an officer in officer-involved domestic violence arrest
cases. Eight of the independent variables in the model predict when an officer is more likely to
be convicted versus nonconviction. The outcomes in officer-involved domestic violence arrest

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cases demonstrate that the criminal courts are not willing to tolerate certain things from officers
who are arrested. The simple odds of conviction in an officer-involved domestic violence arrest
case are 18.1 times greater if the officer was charged with the offense of obstruction of justice.
Similarly, the simple odds of conviction are 7.8 times greater in officer-involved domestic
violence arrest cases if the officer was charged with vandalism (e.g., destruction of property,
criminal mischief). The simple odds of conviction are 3.6 times greater in cases involving
officer-involved domestic violence if the incident is also sex-related. The victim’s relationship
to the arrested officer in officer-involved domestic violence arrest cases also predicts conviction.
The simple odds of conviction increase by 14.2% for every one unit categorical increase on the
eight-point scale of victim relationship. The interpretation of victim relationship can be better
explained in the regression tree analysis directly below.
There are several variables specific to cases involving officer-involved domestic violence
that predict the odds of criminal conviction. First, the simple odds of conviction are 1.2 times
greater if the officer uses or threatens to use a personally-owned gun. Second, the simple odds of
conviction are 3.7 times greater if the arrested officer violated a domestic order of protection.
Third, the simple odds of conviction are 6.7 times greater if the victim of the officer-involved
domestic violence is fatally injured as a result of the crime for which the officer was arrested.
Conversely, the simple odds of conviction decrease by 44.4% if the victim of an officer-involved
domestic violence arrest case is injured (nonfatal injuries). Once again, in this model job loss
predicts conviction. The simple odds of conviction are three times greater if the arrested officer
ultimately loses his or her job in the aftermath of being arrested in a case of officer-involved
domestic violence. Lastly, the location geographically predicts the criminal case disposition in
officer-involved domestic violence arrest cases. Logistic regression provides no meaningful

163
interpretation of the odds ratio for geographic region within the United States, and the regression
tree analysis in the next paragraph provides context for interpretation of geographic location.
Figure 24 presents the results of predicting conviction and included a total of 499 arrest
cases. The tree had an overall classification score of 73.9% (AUC = .781, 95% CI [.741, .822],
2
= .562) and selected the variable job lost as the splitting criterion. Officers who had not lost
RROC

their job (node 1) were convicted in 36.2% of the cases. In contrast, officers who lost their job
(node 2) were convicted in 71.5% of the cases. Cases in node 1 were further partitioned by the
variable year of arrest. Officers who were arrested during years 2005-2006 were convicted in
63.5% of the cases and officers who were arrested in years 2007-2011 were convicted in 26% of
the cases. Cases in node 2 were further separated by the variable geographic division. Cases
from the East North Central, West North Central, and Middle Atlantic divisions of the United
States had convictions in 85% of the cases. In contrast, cases from the Pacific, East South
Central, South Atlantic, West South Central, New England, and Mountain divisions of the
country had convictions in 62.5% of the cases. The tree also included the following variables in
tier three: state and victim’s relationship to the offender.
Predicting Job Loss in Officer-involved Domestic Violence Arrest Cases
The set of regression models in this section predict job loss in officer-involved domestic
violence arrest cases. Chi-Square bivariate associations presented in Table 69 are statistically
significant at p < .05 for 40 independent variables and the dependent variable, job loss. The
strength of the statistically significant bivariate associations is moderate for each of six
independent variables and job loss: criminal conviction, where χ2 (1, N = 497) = 62.928, p <
.001, V = .356; the difference in years between the age of the arrested officer and the age of the
victim, where χ2 (65, N = 961) = 85.555, p = .045, V = .298; the State in which the arrested

164
officer’s employing law enforcement agency was located, where χ2 (49, N = 961) = 81.166, p =
.003, V = .291; sex-related, where χ2 (1, N = 961) = 51.384, p < .001, V = .231; year of arrest,
where χ2 (6, N = 961) = 41.162, p < .001, V = .207; and, forcible fondling, where χ2 (1, N = 961)
= 39.122, p < .001, V = .202.
Table 70 presents a backward stepwise binary logistic regression model predicting job
loss as a final adverse employment action against an officer after being arrested for an offense
relating to officer-involved domestic violence. Bivariate correlations for each of the independent
variables in the model indicate that none of the variables are highly correlated with each other.
Multicollinearity is not a problem as indicated by no tolerance scores below .883 and no variance
inflation factors above 1.132. Autocorrelation is not a problem as indicated by a Durbin-Watson
score of 1.941. Logistic regression results indicate that the overall model of nine predictors is
statistically reliable in distinguishing between police crime arrest cases where officers kept their
job and cases where officers lost their job after being arrested for an incident of officer-involved
domestic violence. Wald statistics indicate that all of the independent variables in the model
significantly predict job loss in officer-involved domestic violence arrest cases. The logistic
2
regression model correctly classified 72.2% of the cases (AUC = .800, 95% CI [.762, .839], RROC

= .600).
Interpretation of odds ratios in officer-involved domestic violence arrest cases provides
context for prediction of job loss. Duty status predicts job loss, as the simple of job loss are 5.3
times greater if the officer was on-duty when he or she was alleged to have committed officerinvolved domestic violence that resulted in the officer’s arrest. The simple odds of job loss in
officer-involved domestic violence arrest cases are 1.6 times greater if the arrested officer used
other body parts (not hands or fist) as a weapon against the crime victim. The simple odds of job

165
loss go down by 50.9% in officer-involved domestic violence arrest cases if the officer was
charged with simple (misdemeanor) assault. The simple odds of job loss are 4.4 times greater if
the officer arrested in an officer-involved domestic violence case was convicted of at least one
criminal offense. The simple odds of job loss, however, decreased by 49% if an officer arrested
for a crime stemming from officer-involved domestic violence was suspended from his or her job
as a sworn officer for a period of time after being arrested. Officers arrested for crimes relating
to officer-involved domestic violence are more likely to lose their job as a sworn law
enforcement officer if their employing law enforcement agency is located in a rural area. The
simple odds of job loss increase by 52.1% for every one unit increase in rurality of the county (or
independent city) where the officer’s employing agency is located. Here again we also see a
relationship between job loss in a police crime arrest case and instances where officers have been
sued in federal court at some point during their career for violating someone’s civil rights. The
simple odds of job loss in police crime arrest cases stemming from officer-involved domestic
violence are 2.1 times greater if at some point during the arrested officer’s career, that officer
was sued in a state court for violating someone’s civil rights and the civil case was removed by a
civil defendant to a United States District Court pursuant to the provisions of 28 U.S.C. §1441.
Figure 25 presents the results of predicting job loss and included a total of 965 arrest
cases. The tree had an overall classification score of 69.2% (AUC = .739, 95% CI [.707, .770],
2
= .478) and selected the variable case disposition: officer was convicted of a crime as the
RROC

splitting criterion. Officers who were convicted of at least one criminal offense charged (node 1)
lost their job in 51.8% of the cases. In contrast, officers who were not convicted of any offense
charged (node 2) lost their job in 25% of the cases. The officers who were convicted in node 1
were further partitioned by the variable year of arrest. Officers arrested during the years 2005-

166
2007 lost their job in 37.8% of the cases and officers arrested during years 2008-2011 lost their
job in 61.4% of the cases. The officers who were not convicted in node 2 were partitioned by the
variable full-time sworn personnel. Arrested officers employed by law enforcement agencies
with 1-249 sworn officers lost their job in 32.5% of the cases and arrested officers employed by
agencies with 250-1000+ sworn officers lost their job in 16.7% of the cases. The tree also
included the following variables in tiers three through five: age categorical and geographic
division.

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Part VI: Profit-motivated Police Crime Data Set Models
There are 1,592 cases in the data set in which sworn nonfederal law enforcement officers
were arrested for profit-motivated crimes during the period January 1, 2005, through December
31, 2011. The profit-motivated arrest cases involve 1,396 individual officers employed by 782
state, local, special, constable, and tribal law enforcement agencies located in 531 counties and
independent cities in 47 states and the District of Columbia (all states except Idaho, Maine, and
North Dakota). Of these 1,396 officers who were arrested for profit-motivated crimes, 94 of the
arrested officers have more than one case ( X = 1.14, Mdn = 1.00, Mode = 1, SD = .808) because
they have more than one crime victim (one criminal case per crime victim) and/or were arrested
for a profit-motivated crime on more than one occasion during the study years 2005-2011. More
than two-thirds of the profit-motivated criminal cases involved an arrested officer who is known
to have lost his or her job (n = 1,080, 67.9%). The known final adverse employment outcomes
in these cases include no action against the arrested officer (n = 135, 8.5%), suspended (n = 376,
23.6%), voluntarily resigned (n = 503, 31.6%), and involuntarily terminated (n = 577, 36.3%).
More than half of the profit-motivated arrest cases resulted in a conviction (n = 914, 57.4%) on
one or more offenses charged in the case.
Officers Arrested for Profit-motivated Police Crime, Offenses & Employing Agencies
Table 71 presents descriptive information on the profit-motivated police crime arrest
cases in terms of the arrested officer and the offenses charged, as well as information on the
employing law enforcement agency and the arresting law enforcement agency. Most of the
profit-motivated arrest cases involve male officers (n = 1,497, 94.1%). The modal category for
known officer age at time of arrest is ages 36-39 (n = 244, 15.3%). The youngest officer arrested
for a profit-motivated police crime was 20 years old at time of arrest, and the oldest was 79 years

168
old ( X age = 38.01, Mdn age = 37,00, Mode age = 41, SD = 9.097 years). The modal category
for known years of service as a sworn law enforcement officer at time of arrest is 3-5 years (n =
197, 12.4%) ( X years of service = 10.90, Mdn years of service = 10.00, Mode years of service =
4, SD = 7.808 years). Most of the profit-motivated police crime arrest cases involve sworn
officers in patrol and street-level ranks, including nonsupervisory officers, deputies, troopers,
and detectives (n = 1,243, 78.1%). Other profit-motivated police crime arrest cases include midrank line and field supervisors (n = 208, 13.1%) and high-ranking police managers and
executives (n = 140, 8.8%). More than two-thirds of the profit-motivated arrest cases involve
crimes that were committed while on-duty (n = 1,093, 68.7%). Even so, two-thirds of the cases
(n = 1,081, 67.9%) involve an arrest made by some law enforcement agency other than the
agency employing the arrested officer.
Most of the profit-motivated police crime arrest cases involve officers employed by
municipal police departments (n = 1,162, 73.0%) or sheriff’s offices (n = 251, 15.8%). The
arrest cases also involve officers who were employed by primary state police agencies (n = 62,
3.9%), county police departments (n = 66, 4.2%), special police departments (n = 42, 2.6%),
constable agencies (n = 7, 0.4%), and tribal police departments (n = 1, 0.1%). The modal
category for size of the employing law enforcement agency by the number of full-time sworn
officers employed is 1,000 or more full-time sworn officers (n = 495, 31.1%) and zero part-time
sworn officers (n = 1,209, 76.1%). The majority of officers arrested in profit-motivated police
crime arrest cases were employed by a nonfederal law enforcement agency located in a nonrural
metropolitan county or independent city (n = 1,312, 82.5%). The employing agencies are located
throughout the United States, including agencies in the Southern states (n = 726, 45.6%),

169
Northeastern states (n = 362, 22.8%), Midwestern states (n = 329, 20.7%), and Western states (n
= 174, 10.9%).
Table 72 presents the profit-motivated police crime arrest cases in terms of the most
serious offense charged in each case. There are 46 separate criminal offense categories
represented as the most serious offense charged in the profit-motivated police crime arrest cases
in years 2005-2011. Most common as the most serious offense charged in the profit-motivated
cases are unclassified thefts (n = 255, 16.0%), false pretenses (known as theft by deception in
Model Penal Code states) (n = 199, 12.5%), drug offenses (n = 189, 11.9%), robbery (n = 103,
6.4%), thefts from buildings (n = 92, 5.8%), and extortion and blackmail (n = 85, 5.3%).
Victims of Profit-motivated Police Crime
Table 73 presents victim characteristics in profit-motivated police crime arrest cases in
years 2005-2011. Victim information was not ascertainable from the source documents in many
of the profit-motivated police crime arrest cases. Most of the known victims are male (n = 195,
83.3%). Almost none of the known victims are children under the age of 18 (98.2% of the
known victims are adults age 18 or older). Most of the known victims are strangers or
nonstranger acquaintances (n = 350, 21.9%) to the arrested officer.
Predicting Conviction in Profit-motivated Police Crime Arrest Cases
In this section the regression models predict criminal conviction on any offense charged
in profit-motivated police crime arrest cases versus nonconviction. Conviction data are available
on over two-thirds of the profit-motivated cases (n = 1,105, 69.5%). Of those cases with known
criminal case outcomes, most of the officers arrested for profit-motivated crimes were convicted
(n = 914, valid 82.7%) on at one offense charged in the case. Bivariate Chi-Square associations,
presented in Table 74, are statistically significant at the p < .05 level for 49 independent variables

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and the dependent variables, conviction on any offense charged. There are six bivariate
associations of moderate strength as indicated by the Cramer’s V scores for the statistically
significant Chi-Square statistics. The statistically significant bivariate associations of moderate
strength are the victim’s relationship to the arrested officer, where χ2 (6, N = 266) = 25.017, p <
.001, V = .307; age of the officer at time of arrest, where χ2 (49, N = 1,010) = 85.828, p = .001, V
= .292; the State where the arrested officer’s employing law enforcement agency is located,
where χ2 (47, N = 1,105) = 88.251, p < .001, V = .283; years of service at time of arrest, where χ2
(39, N = 799) = 62.423, p = .010, V = .280; job loss, where χ2 (1, N = 1,105) = 82.094, p < .001,
V = .273; and categorical age, where χ2 (10, N = 1,105) = 44.496, p < .001, V = .201.
Table 75 presents a backward stepwise binary logistic regression model predicting
conviction in profit-motivated police crime arrest cases. Bivariate correlations computed for
each of the independent variables in the logistic regression model revealed that none of the
variables were highly correlated with each other. Multicollinearity is not a problem as indicated
by no tolerance scores below .696 and no variance inflation factors above 1.436. The DurbinWatson score of 1.659 indicates that autocorrelation is not a problem in the model. Logistic
regression results indicate that the overall model of nine predictors is statistically reliable in
distinguishing between conviction in profit-motivated police crime arrest cases and
nonconviction in profit-motivated police crime arrest cases. The model correctly classified
2
= .186). Wald statistics indicate that
83.8% of the cases (AUC = .593, 95% CI [.547, .639], RROC

all of the independent variables in the binary logistic regression model significantly predict
conviction in profit-motivated police crime arrest cases.
Odds ratio interpretations provide context for prediction of conviction in profit-motivated
police crime arrest cases. The single largest predictor of conviction in the profit-motivated arrest

171
cases is whether the officer’s crime involved a drug-related shakedown. The simple odds of
conviction are 109 times greater if the profit-motivated police crime involved a drug-related
shakedown. Job loss also predicts conviction in profit-motivated police crime arrest cases. The
simple odds of conviction in a profit-motivated case are 23.2 times greater if the arrested officer
ultimately losses their job (either through involuntary termination or through voluntary
resignation) as the final adverse employment outcome following an officer’s arrest for a profitmotivated police crime. Violence-related police crime also predicts criminal conviction in
profit-motivated police crime arrest cases. The simple odds of conviction are 4.3 times higher if
an officer’s profit-motivated police crime is also violence-related. The type of nonfederal law
enforcement agency employing the arrested officer and the State where the employing agency is
located also predict criminal conviction in profit-motivated police crime arrest cases. The simple
odds of conviction in profit-motivated police crime arrest cases increase by 68% for every one
unit increase in agency type. Agency type is a categorical variable where 1 = primary state
police agency, 2 = primary state police agency, 3 = sheriff’s office, 4 = county police
department, 5 = municipal police department, 6 = special police department, 7 = constable
agency, 8 = tribal police department, and 9 = regional police department. As a literal
interpretation (remembering that this variable is a nominal-level measurement), that means as
you move away from primary state police agencies to other types of police departments, the
greater the likelihood of conviction. There is no practical interpretation of the odds ratio for
State where the officer’s employing nonfederal law enforcement agency was located, other than
to note that the simple odds of conviction in profit-motivated police crime arrest cases varies
from State to State.

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Several variables in the logistic regression model predict when criminal conviction (on
any offense charged in the case against the arrested officer) is less likely. Here, the number of
part-time sworn officers employed by the agency where the arrested officer works predicts
conviction. The simple odds of conviction go down by 27.2% for every one unit categorical
increase in the number of part-time sworn officers employed by the agency. The media and
public perception surrounding an officer’s arrest for profit-motivated police crime also impacts
on conviction. The simple odds of conviction goes down by 96.3% if the arrested officer’s chief
is under scrutiny as a result of the officer’s arrest for a profit-motivated police crime. Although
in the same model profit-motivated police crime arrest cases that involve a drug-related
shakedown increases the simples odds of conviction, drug-related shakedowns in the form of offduty robberies decreases the odds of conviction. The simple odds of conviction decrease by
98.9% if the officer’s profit-motivated police crime arrest case involves a drug-related
shakedown in the form of an off-duty robbery. Finally, the age of the victim in a profitmotivated police crime arrest case predicts the likelihood of conviction. The simple odds of
conviction decrease by 30% for every one unit categorical increase in the age of the victim in a
profit-motivated police crime arrest case.
Figure 26 presents the results of predicting conviction for the profit-motivated cases and
included a total of 1,105 police crime arrest cases. The tree had an overall classification score of
2
= .562) selected the variable job loss as the
85.2% (AUC = .781, 95% CI [.744, .818], RROC

splitting criterion. Officers who had not lost their job (node 1) were convicted in 62.4% of the
cases. In contrast, officers who lost their job (node 2) were convicted in 87.9% of the cases. The
officers who had not lost their job in node 1 were partitioned by the variable year of arrest.
Officers who were arrested from 2005-2006 were convicted in 83.6% of the cases. Officers who

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were arrested in years 2007-2009 were convicted in 34.2% of the cases and officers who were
arrested in years 2010- 2011 were convicted in 59.9% of the cases. The officers who had lost
their job in node 2 were partitioned by the variable urban/rural continuum. Officers who were
employed by agencies located metropolitan counties (or independent cities) of 250,000 to 1
million; a nonmetropolitan county with an urban population of 20,000 or more, not adjacent to a
metro area; county in a metropolitan areas of fewer than 250,000 population, adjacent to a
metropolitan area; or a nonmetropolitan county with an urban population of 20,000 or more,
adjacent to a metropolitan area were convicted in 79.6% of the cases. Officers who were
employed by a law enforcement agency located in a county within a metropolitan area of 1
million population or more; nonmetropolitan county with an urban population 2,500 to 19,999,
adjacent to a metropolitan area; nonmetropolitan county with urban population of 2,500 to
19,999, not adjacent to a metropolitan area; nonmetropolitan county completely rural or less than
2,500 urban population, not adjacent to a metropolitan area; nonmetropolitan county completely
rural or less than 2,500 urban population, adjacent to a metropolitan area were convicted in
92.6% of the cases. The tree also included the following variables in tier three: drug/narcotic
violation, urban/rural continuum, arresting agency, and geographic region.
Predicting Job Loss in Profit-motivated Police Crime Arrest Cases
In this section the regression models predict job loss following an officer’s arrest for a
profit-motivated police crime. Bivariate Chi-Square associations are statistically significant at
the p < .05 level for 47 independent variables and the dependent variable, job loss binary (coded
as 0 = kept job and 1 = lost job) and are presented in Table 76. There are five bivariate
associations of moderate strength as indicated by the Cramer’s V scores for the statistically
significant Chi-Square statistics. They are: year of arrest, where χ2 (6, N = 1,591) = 155.340, p <

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.001, V = .312; State where the arrested officer’s employing law enforcement agency is located,
where χ2 (47, N = 1,591) = 126.335, p < .001, V = .282; whether the officer was convicted (for
any offense charged in the arrest case), where χ2 (1, N = 1,105) = 82.094, p < .001, V = .273;
whether the officer was suspended for a period of time following his or her arrest for a profitmotivated crime, where χ2 (1, N = 1,591) = 87.439, p < .001, V = .234; and, years of service as a
sworn law enforcement officer at time of arrest, where χ2 (39, N = 1,126) = 56.457, p = .035, V =
.224.
Table 77 presents a backward stepwise binary logistic regression model predicting job
loss in profit-motivated police crime arrest cases. None of the independent variables are highly
correlated with each other, as indicated by bivariate correlations that were computed for each of
the independent variables in the logistic regression model. Multicollinearity is not a problem as
indicated by no tolerance scores below .766 and no variance inflation factors above 1.306.
Autocorrelation is not a problem in the model as indicated by a Durbin-Watson score of 1.523.
Logistic regression results indicate that the overall model of seven predictors is statistically
reliable in distinguishing between officers who lost their jobs after being arrested in a profitmotivated police crime case, and others who kept their jobs after being arrested in a profitmotivated police crime case. Wald Statistics indicate that all of the independent variables in the
model significantly predict job loss in profit-motivated police crime arrest cases. The model
2
= .336).
correctly classified 91.7% of the cases (AUC = .668, 95% CI [.626, .711], RROC

Context for prediction of job loss in profit-motivated police crime arrest cases is provided
through interpretation of odds ratios. Five of the independent variables in the logistic regression
model predict when an officer is more likely to lose his or her job as a sworn law enforcement
officer after being arrested for a profit-motivated police crime. The single largest predictor of

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job loss is when the profit-motivated crime is also drug-related. The simple odds of job loss are
45.6 times greater in a profit-motivated police crime arrest case if the case also involves drug
selling, dealing, and/or trafficking. Conviction also predicts job loss, but here again it is not
possible to establish causal order between conviction and job loss with this data set. The simple
odds of job loss are ten times greater in profit-motivated police crime arrest cases if the arrested
officer is ultimately convicted on one or more criminal offenses charged in the case. Profitmotivated crimes that are committed by an officer in their official capacity as a sworn law
enforcement officer (as opposed to profit-motivated crimes committed by an officer in their
individual capacity) also predict job loss. The simple odds of job loss are nine times greater in
profit-motivated police crime arrest cases if the crime was committed by the arrested officer in
their official capacity. Similarly, profit-motivated crimes that are also internal crimes against the
organization (that is, a crime against the arrested officer’s employing law enforcement agency)
increase the simple odds of job loss by 46.4%. The geographic location of the arrested officer’s
employing law enforcement agency also predicts job loss in profit-motivated police crime arrest
cases. The simple odds of job loss increase by 69.5% in profit-motivated cases with every one
unit increase in the category of geographic division across the United States. There is no
practical interpretation of the odds ratio, however, for geographic division in this binary logistic
regression model.
Two independent variables in the logistic regression model predict when an officer is
going to keep their job as a sworn law enforcement officer after being arrested in a profitmotivated police crime arrest case. The first of these is when an officer is charged with the
offense of obstruction of justice in a profit-motivated case, where the simple of job loss decrease
by 93.5%. In some of the profit-motivated police crime arrest cases there was discussion in the

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news article narratives of an agency scandal or cover up in the aftermath of an officer being
arrested. The simple odds of job loss decrease by 92.5% in profit-motivated police crime arrest
cases if there is discussion of a scandal or cover up at the employing officer’s law enforcement
agency.
Figure 27 presents the results of predicting conviction for the profit-motivated cases and
included a total of 1,591 arrest cases. The tree had an overall classification score of 76.6%
2
= .576) and selected the variable case disposition:
(AUC = .788, 95% CI [.763, .812], RROC

officer was convicted of a crime as the splitting criterion. Officers who were convicted (node 1)
lost their job in 54.8% of the cases. In contrast, officers who were not convicted (node 2) lost
their job in 87.9% of the cases. The officers who were convicted in node 1 were partitioned by
the variable year of arrest. Officers who were arrested from 2005-2007 were convicted in 51.8%
of the cases and officers who were arrested in years 2008-2011 were convicted in 81.3% of the
cases. Officers who were not convicted in node 2 were partitioned by the variable discussion of
an agency scandal or cover up. Officers involved in cases that discussed an agency scandal or
cover up lost their job in 24.2% of the cases. In contrast, officers who were not involved in cases
that involved some sort of an agency scandal or cover up lost their job in 60.8% of the cases.
The tree also included the following variables in tiers three though five: geographic division,
year, state, discussion of an agency scandal or cover up, years of service, and type of agency.

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Part VII: Employing Law Enforcement Agencies of Arrested Officers
The 6,724 police crime arrest cases in this study involve 5,545 individual sworn law
enforcement officers employed by 2,529 nonfederal law enforcement agencies located in
headquartered in 1,166 counties and independent cities in all 50 states and the District of
Columbia.5 In this part the unit of analysis is changed from arrest case to arrested individual
officer. The purpose of changing the unit of analysis to individual officers is to provide rates of
officers arrested per agency, per 1,000 officers, and per 100,000 population. Individual
nonfederal sworn law enforcement officers were arrested at a rate of 0.72% officers arrested per
1,000 officers, and at a rate of 1.7 officers arrested per 100,000 population nationwide.
We urge caution on one critically important point: the tables (and table appendices)
presented in this part of the report, and the data from which they were compiled, cannot support
a direct comparison of one agency to another nor indeed any cluster of agencies to others.
Though it is a natural tendency to make such comparisons, the many localized conditions
invisible to the research team in our data sources prevent accurate comparisons. Among the
factors that data sources do not (and likely often cannot) discern are whether an officer’s arrest
was instigated by his or her own department as a result of its internal standards for conduct and
integrity; whether the arrested officer’s criminal offense(s) would not have resulted in the
officer’s arrest if it had been less publicly visible; and whether arrests of police officers by
outside law enforcement agencies were made at the request of an officer’s employing agency or

In Part I of this report we identified 2,529 nonfederal law enforcement agencies located in 1,205 counties and
independent cities across the United States. In coding of police crime arrest cases for this study, the counties where
an employing law enforcement agency was located were logged for both (a) the county (or independent city) where
the employing agency was headquartered, and (b) the county (or independent city) where the arrested officer was
stationed. In the case of a state trooper, for example, the county would be recorded for the location of the agency
headquarters as well as the county where the arrested trooper’s assigned barracks was located.

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in spite of attempts by that agency to conceal the offense(s) and/or protect the officer’s (and thus,
the employing agency’s) public image.
Rates of Officers Arrested in the 200 Largest Agencies
Table 78 presents the 200 largest employing state and local law enforcement agencies in
terms of rates of officers arrested during the study period years 2005-2011. The table is sorted
by the number of full-time sworn officers employed from largest agency to the smallest agency
within the largest 200 agencies represented in the study. The same table is also presented in
alternative versions as Appendix B-1 (200 largest agencies in the study sorted by rates of officers
arrested per 1,000 officers) and Appendix C-1 (200 largest agencies in the study sorted by rates
of officers arrested per 100,000 population). The ten large agencies with the highest rates of
officers arrested per 1,000 officers are New Orleans Police Department (rate of 44.21 officers
arrested per 1,000 officers), Milwaukee Police Department (rate of 36.74 officers arrested per
1,000 officers), Memphis Police Department (rate of 29.70 officers arrested per 1,000 officers),
New Mexico State Police (rate of 24.62 officers arrested per 1,000 officers), Pittsburgh Police
Department (rate of 23.57 officers arrested per 1,000 officers), Shreveport Police Department
(rate of 23.48 officers arrested per 1,000 officers), Polk County (FL) Sheriff’s Office (rate of
21.67 officers arrested per 1,000 officers), Kern County (CA) Sheriff’s Office (rate of 21.48
officers arrested per 1,000 officers), Indianapolis Police Department (rate of 20.87 officers
arrested per 1,000 officers), and Minneapolis Police Department (rate of 19.96 officers arrested
per 1,000 officers). See Appendix B-1.
The ten large agencies with the highest rates of officers arrested per 100,000 population
are New Orleans Police Department (rate of 18.32 officers arrested per 100,000 population),
Baltimore Police Department (rate of 8.86 officers arrested per 100,000 population), Milwaukee

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Police Department (rate of 7.70 officers arrested per 100,000 population), Norfolk Police
Department (rate of 5.77 officers arrested per 100,000 population), Memphis Police Department
(rate of 4.96 officers arrested per 100,000 population), Washington Metropolitan Police
Department (rate of 4.82 officers arrested per 100,000 population), Shreveport Police
Department (rate of 4.71 officers arrested per 100,000 population), Philadelphia Police
Department (rate of 4.33 officers arrested per 100,000 population), Jackson Police Department
(rate of 4.67 officers arrested per 100,000 population), and Indianapolis Police Department (rate
of 3.65 officers arrested per 100,000 population. See Appendix C-1.
Rates of Officers Arrested in Nonmetropolitan Agencies
Table 79 presents the employing law enforcement agencies located in nonmetropolitan
(rural) counties and independent cities in terms of rates of officers arrested during the study
period years 2005-2011. The table is sorted by agency name in alphabetical order. The same
table information is also presented in alternative versions as Appendix A-1 (sorted by the number
of full-time sworn personnel employed), Appendix B-2 (sorted by rate of officers arrested per
1,000 officers), and Appendix C-2 (sorted by rate of officers arrested per 100,000 population).
The law enforcement agencies located in nonmetropolitan counties and independent cities
with the highest rates of officers arrested per 1,000 officers are Mounds (IL) Police Department
(rate of 2000.00 officers arrested per 1,000 officers) and Atwater (MN) Police Department,
Berlin Borough (PA) Police Department, Berlin Heights (OH) Police Department, Bowman (SC)
Police Department, Burr Oak (MI) Police Department, Cooter (MO) Police Department, Elgin
(ND) Police Department, Hamburg (IA) Police Department, Lamoure (ND) Police Department,
Lockhart (AL) Police Department, Marion Township (WI) Police Department, Nicholas County
(KY) Sheriff’s Office, Oakwood (OH) Police Department, Perryville (KY) Police Department,

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Petroleum County (MT) Sheriff’s Office, Pineview (GA) Police Department, Tipton (OK) Police
Department, Turkey Creek (LA) Police Department, Wakeman (OH) Police Department, Wilson
(KS) Police Department, and Zolfo Springs (FL) Police Department (each with a rate of 1000.00
officers arrested per 1,000 police officers). See Appendix B-2.
The law enforcement agencies located in nonmetropolitan counties and independent cities
with the highest rates of officers arrested per 100,000 population are Petroleum County (MT)
Sheriff’s Office (rate of 202.43 officers arrested per 100,000 population), Terrell County (TX)
Sheriff’s Office (rate of 101.63 officers arrested per 100,000 population), Talbot County (GA)
Sheriff’s Office (rate of 43.70 officers arrested per 100,000 population), La Salle County (TX)
Sheriff’s Office (rate of 43.57 officers arrested per 100,000 population), Elgin (ND) Police
Department (rate of 41.77 officers arrested per 100,000 population), Griggs County (ND)
Sheriff’s Office (rate of 41.32 officers arrested per 100,000 population), Crystal City (TX) Police
Department (rate of 34.26 officers arrested per 100,000 population), Mounds (IL) Police
Department (rate of 32.46 officers arrested per 100,000 population), Pulaski County (IL)
Sheriff’s Office (rate of 32.46 officers arrested per 100,000 population), and Carter County (MO)
Sheriff’s Office (rate of 31.92 officers arrested per 100,000 population). See Appendix C-2.
Rate of Officers Arrested in Primary State Police Agencies
Table 80 presents the employing primary state police agencies in terms of rates of
officers arrested during the study period years 2005-2011. The table is sorted by agency name in
alphabetical order. The same table information is also presented in alternative versions as
Appendix A-2 (sorted by the number of full-time sworn personnel employed), Appendix B-3
(sorted by rate of officers arrested per 1,000 officers), and Appendix C-3 (sorted by rate of
officers arrested per 100,000 population).

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The five primary state police agencies with the highest rates of officers arrested per 1,000
officers are New Mexico State Police (rate of 24.62 officers arrested per 1,000 officers),
Arkansas Highway Patrol (rate of 13.42 officers arrested per 1,000 officers), South Carolina
Highway Patrol (rate of 10.34 officers arrested per 1,000 officers), Mississippi Highway Safety
Patrol (rate of 10.10 officers arrested per 1,000 officers), and Rhode Island State Police (rate of
9.95 officers arrested per 1,000 officers). See Appendix B-3. The five primary state police
agencies with the highest rates of officers arrested per 100,000 population are New Mexico State
Police (rate of 0.63 officers arrested per 100,000 population), Delaware State Police (rate of 0.33
officers arrested per 100,000 population), New Hampshire State Police (rate of 0.23 officers
arrested per 100,000 population), South Carolina Highway Patrol (rate of 0.22 officers arrested
per 100,000 population), and Mississippi Highway Safety Patrol (rate of 0.20 officers arrested
per 100,000 population). See Appendix C-3.
Rates of Officers Arrested in Sheriff’s Offices
Table 81 presents the employing sheriff’s offices in terms of rates of officers arrested
during the study period years 2005-2011. The table is sorted by agency name in alphabetical
order. The same table information is also presented in alternative versions as Appendix A-3
(sorted by the number of full-time sworn personnel employed), Appendix B-4 (sorted by rate of
officers arrested per 1,000 officers), and Appendix C-4 (sorted by rate of officers arrested per
100,000 population).
The sheriff’s offices with the highest rates of officers arrested per 1,000 officers are
Nicholas County (KY) Sheriff’s Office (rate of 1000.00 officers arrested per 1,000 officers),
Petroleum County (MT) Sheriff’s Office (rate of 1000.00 officers arrested per 1,000 officers),
Carter County (MO) Sheriff’s Office (rate of 666.67 officers arrested per 1,000 officers), Oregon

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County (MO) Sheriff’s Office (rate of 400.00 officers arrested per 1,000 officers), Talbot County
(GA) Sheriff’s Office (rate of 375.00 officers arrested per 1,000 officers), Baker County (GA)
Sheriff’s Office (rate of 333.33 officers arrested per 1,000 officers), Butler County (AL)
Sheriff’s Office (rate of 333.33 officers arrested per 1,000 officers), Gallatin County (IL)
Sheriff’s Office (rate of 333.00 officers arrested per 1,000 officers), Griggs County (ND)
Sheriff’s Office (rate of 333.00 officers arrested per 1,000 officers), and Guadalupe County
(NM) Sheriff’s Office (rate of 333.00 officers arrested per 1,000 officers). See Table B-4.
The sheriff’s offices with the highest rates of officers arrested per 100,000 population are
Petroleum County (MT) Sheriff’s Office (rate of 202.43 officers arrested per 100,000
population), Terrell County (TX) Sheriff’s Office (rate of 101.63 officers arrested per 100,000
officers), Talbot County (GA) Sheriff’s Office (rate of 43.70 officers arrested per 100,000
officers), La Salle County (TX) Sheriff’s Office (rate of 43.57 officers arrested per 100,000
officers), Griggs County (ND) Sheriff’s Office (rate of 41.32 officers arrested per 100,000
officers), Pulaski County (IL) Sheriff’s Office (rate of 32.46 officers arrested per 100,000
officers), Carter County (MO) Sheriff’s Office (rate of 31.92 officers arrested per 100,000
officers), Baker County (GA) Sheriff’s Office (rate of 28.98 officers arrested per 100,000
officers), Lake County (CO) Sheriff’s Office (rate of 27.36 officers arrested per 100,000
officers), and Henry County (VA) Sheriff’s Office (rate of 25.85 officers arrested per 100,000
population).
Rates of Officers Arrested in County Police Departments
Table 82 presents the employing county police departments in terms of rates of officers
arrested during the study period years 2005-2011. The table is sorted by agency name in
alphabetical order. The same table information is also presented in alternative versions as

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Appendix A-4 (sorted by the number of full-time sworn personnel employed), Appendix B-5
(sorted by rate of officers arrested per 1,000 officers), and Appendix C-5 (sorted by rate of
officers arrested per 100,000 population).
The county police departments included in the study with the highest rate of officers
arrested per 1,000 officers are Polk County (GA) Police Department (rate of 85.71 officers
arrested per 1,000 officers), Oldham County (KY) Police Department (rate of 64.52 officers
arrested per 1,000 officers), Dougherty County (GA) Police Department (rate of 42.55 officers
arrested per 1,000 officers), Horry County (SC) Police Department (rate of 37.04 officers
arrested per 1,000 officers), Kauai (County) (HI) Police Department (rate of 24.00 officers
arrested per 1,000 officers), Indianapolis (IN) Metropolitan Police Department (rate of 20.86
officers arrested per 1,000 officers), Fulton County (GA) Police Department (rate of 15.50
officers arrested per 1,000 officers), Maui (County) (HI) Police Department (rate of 15.20
officers arrested per 1,000 officers), Gaston County (NC) Police Department (rate of 15.04
officers arrested per 1,000 officers), and Roanoke County (VA) Police Department (rate of 14.81
officers arrested per 1,000 officers). See Appendix B-5.
The county police departments included in the study with the highest rates of officers
arrested per 100,000 population are Polk County (GA) Police Department (rate of 7.23 officers
arrested per 100,000 population), Kauai (County) (HI) Police Department (rate of 4.47 officers
arrested per 100,000 population), Indianapolis (IN) Metropolitan Police Department (rate of 3.65
officers arrested per 100,000 population), Horry County (SC) Police Department (rate of 3.34
officers arrested per 100,000 population), Oldham County (KY) Police Department (rate of 3.32
officers arrested per 100,000 population), Maui (County) (HI) Police Department (rate of 3.23
officers arrested per 100,000 population), Honolulu (City and County) (HI) Police Department

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(rate of 2.73 officers arrested per 100,000 population), Savannah-Chatham (GA) Metropolitan
Police Department (rate of 2.26 officers arrested per 100,000 population), Prince George’s
County (MD) Police Department (rate of 2.20 officers arrested per 100,000 population),
Roanoke County (VA) Police Department (rate of 2.17 officers arrested per 100,000 population),
and Charlotte-Mecklenburg (NC) Police Department (rate of 2.17 officers arrested per 100,000
population). See Appendix C-5.
Rates of Officers Arrested in 500 Largest Municipal Police Departments
Table 83 presents the 500 largest employing municipal police departments in terms of
rates of officers arrested during the study period years 2005-2011. The table is sorted by agency
name in alphabetical order. The same table information is also presented in alternative versions
as Appendix A-5 (sorted by the number of full-time sworn personnel employed), Appendix B-6
(sorted by rate of officers arrested per 1,000 officers), and Appendix C-6 (sorted by rate of
officers arrested per 100,000 population).
The large municipal police departments included in the study with the highest rates of
officers arrested per 1,000 officers are Hackensack (NJ) Police Department (rate of 76.92
officers arrested per 1,000 officers), Norwich (CT) Police Department (rate of 61.73 officers
arrested per 1,000 officers), Riviera Beach (FL) Police Department (rate of 55.56 officers
arrested per 1,000 officers), Lorain (OH) Police Department (rate of 50.0 officers arrested per
1,000 officers), Charleston (WV) Police Department (rate of 49.45 officers arrested per 1,000
officers), Schenectady (NY) Police Department (rate of 48.19 officers arrested per 1,000
officers), Edinburg (TX) Police Department (rate of 45.45 officers arrested per 1,000 officers),

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Cleveland (TN) Police Department (rate of 44.94 officers arrested per 1,000 officers), Muskogee
(OK) Police Department (rate of 44.44 officers arrested per 1,000 officers), and New Orleans
(LA) Police Department (rate of 44.21 officers arrested per 1,000 officers). See Appendix B-6.
The large municipal police departments with the highest rates of officers arrested per
100,000 population are New Orleans (LA) Police Department (rate of 18.32 officers arrested per
100,000 population), Portsmouth (VA) Police Department (rate of 10.47 officers arrested per
100,000 population), Baltimore (MD) Police Department (rate of 8.86 officers arrested per
100,000 population), Milwaukee (WI) Police Department (rate of 7.70 officers arrested per
100,000 population), Clarksville (TN) Police Department (rate of 7.54 officers arrested per
100,000 population), Kansas City (KS) Police Department (rate of 6.35 officers arrested per
100,000 population), Petersburg (VA) Police Department (rate of 6.17 officers arrested per
100,000 population), Macon (GA) Police Department (rate of 5.79 officers arrested per 100,000
population), Norfolk (VA) Police Department (rate of 5.77 officers arrested per 100,000
population), and Muskogee (OK) Police Department (rate of 5.63 officers arrested per 100,000
population). See Appendix C-6.
Rates of Officers Arrested in Special State and Local Law Enforcement Agencies
Table 84 presents the special state and local employing law enforcement agencies in
terms of rates of officers arrested during the study period years 2005-2011. The table is sorted
by agency name in alphabetical order. The same table information is also presented in
alternative versions as Appendix A-6 (sorted by the number of full-time sworn personnel
employed), Appendix B-7 (sorted by rate of officers arrested per 1,000 officers), and Appendix
C-7 (sorted by rate of officers arrested per 100,000 population).

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The special law enforcement agencies with the highest rates of officers arrested per 1,000
officers are Connally I.S.D. (TX) Police Department (rate of 333.33 officers arrested per 1,000
officers), Grambling State University (LA) Police Department (rate of 222.22 officers arrested
per 1,000 officers), Ennis I.S.D. (TX) Police Department (rate of 200.00 officers arrested per
1,000 officers), Ventura College (CA) Police Department (rate of 200.00 officers arrested per
1,000 officers), California Exposition and State Fair (CA) Police (rate of 166.67 officers arrested
per 1,000 officers), Lancaster I.S.D. (TX) Police Department (rate of 166.67 officers arrested per
1,000 officers), University of Maryland Eastern Short (MD) Public Safety (rate of 166.67
officers arrested per 1,000 officers), University of West Alabama (AL) Police (rate of 166.67
officers arrested per 1,000 officers), Greenville Technical College (SC) Public Safety (rate of
111.11 officers arrested per 1,000 officers), and Lehigh-Northampton Airport Authority (PA)
Police Department (rate of 111.11 officers arrested per 1,000 officers). See Appendix B-7.
The special law enforcement agencies included in the study with the highest rates of
officers arrested per 100,000 population are University of Tennessee at Knoxville (TN) Police
(rate of 10.47 officers arrested per 100,000 population), University of West Alabama (AL) Police
(rate of 7.27 officers arrested per 100,000 population), Tuskegee University (AL) Police
Department (rate of 4.66 officers arrested per 100,000 population), Grambling State University
(LA) Police Department (rate of 4.28 officers arrested per 100,000 population), University of
Maryland Eastern Shore (MD) Public Safety (rate of 3.78 officers arrested per 100,000
population), Georgia Public Safety Training Center (GA) (rate of 3.78 officers arrested per
100,000 population), Ohio Department of Natural Resources (OH) Office of Law Enforcement
(rate of 3.24 officers arrested per 100,000 population), Virginia Marine Resources Commission
(VA) (rate of 3.02 officers arrested per 100,000 population), Brigham Young University – Idaho

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(ID) Police Department (rate of 2.66 officers arrested per 100,000 population), and Missouri
University of Science and Technology (MO) Police Department (rate of 2.21 officers arrested
per 100,000 population).

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CONCLUSION
Cases in which sworn law enforcement officers act as criminals—whether dealing drugs,
or driving drunk, or sexually molesting a vulnerable citizen—strike a direct blow to the law
enforcement enterprise and the essence of what it means to be a law enforcement officer: protect
and serve. These cases threaten to undermine public trust in both the authority and legitimacy of
state and local law enforcement organizations, and the work of law-abiding sworn officers who
go about their job selflessly, efficiently, and professionally every day. Police crime as a topic
worthy of empirical study however is not clearly understood, and would probably best be
described as untapped or at the very least not sufficiently explored.
The contrast between the topic's substantive weight and comparatively light coverage
within the scholarship is mostly due to an absence of suitable data. The traditional sources of
data and methods of study, whether official statistics, self-report surveys, or direct observations,
either do not exist in any usable format or are ill-equipped to identify, count, or provide the basis
for empirical analyses of instances in which police perpetrate crimes themselves. These cases
have thus far escaped large-scale empirical scrutiny, but they are intrinsically newsworthy
events. Those in the news media need to identify stories that will be of interest to their audience,
and cases of police crime typically include storylines that are clearly newsworthy. This project
utilized a methodology designed to capitalize on the newsworthy character of police crime,
identify these events, and subject them to analyses that have thus far been impossible. Given the
previous unavailability of data and the relative absence of empirical studies dedicated to the
topic, our work should be considered exploratory. The primary aim was to uncover cases of
police crime arrests and to provide the basis for what we hope will become an important
contribution to the establishment of a more substantive and useful line of research on the topic.

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The remainder of this section is divided into several parts. We initially discuss our
findings in terms of both some general observations on the entire data set and selected results
within the types of police crime that we believe justify further comment. The second section
covers implications for policy and practice. Here, we discuss noteworthy results in regard to the
analyses on final adverse employment actions, or the sorts of discipline imposed (if any) by
police organizations in cases of police crime. The points on final adverse employment outcomes
provides context for the identification and discussion of specific recommendations to law
enforcement agencies based on our research. The final section is a discussion on the
implications of our study for research.
Discussion of Findings
The identification of notable general observations in regard to the entire data set is
complicated by the scope of the study and this report—the research study covers seven calendar
years and describes 6,724 criminal cases involving the arrest of 5,545 individual police officers
who perpetrated a wide range of criminal acts under varying circumstances. The arrested
officers were employed by 2,529 state and local law enforcement agencies (see Appendix D)
located in 1,205 counties and independent cities in all 50 states and the District of Columbia.
The project has revealed and confirmed at least three comprehensive truths about police crime.
The first general observation is that police crimes are not uncommon. Police officers get
arrested for crimes with some regularity in jurisdictions around the nation, including rural areas,
small towns, suburbs, and large urban centers. Law enforcement officers in the current study
were arrested at a rate of 0.72 officers arrested per 1,000 officers, and at a rate of 1.7 officers
arrested per 100,000 population nationwide. Of course, only a small percentage of the total
number of law enforcement officers will ever be arrested for a criminal offense, but our data

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directly contradicts some of the prevailing assumptions and the proposition that only a small
group of rotten apples perpetrate the vast majority of police crime. Those assumptions relate to
previous studies, journalistic investigations, and special commissions that described the
misconduct of a single and/or handful of rogue officers operating within a single and/or handful
of jurisdictions. The assumption that only a small group of rogue officers perpetrate crimes also
presumably stems from traditional notions and public expectations regarding the straight-laced,
law-abiding police persona. In contrast, our method provided for the identification of an
unprecedented number of police crimes that occurred within virtually every sort of place, and the
opportunity to draw some conclusions about how often these events occur across the nation. The
data demonstrate that police crimes are not isolated events, and that hundreds of police officers
every year get arrested for crimes including assault, rape, robbery, and murder across the United
States.
The second general observation is that police crime is occupationally-derived. That is,
the cases identified in our research are largely the product of opportunities and other factors
inherent within the context of police work. Scholars have long recognized the occupationallyderived sources of police misconduct, including the reality that patrol officers operate within a
context of low supervision, low public visibility, and face-to-face encounters in which officers
enjoy considerable power and authority over vulnerable citizens. Our methodology involved the
collection of data derived from published news stories that detailed these events rather than
summary statistics or official reports, so the research team was able to discern how on-the-job
realities contributed to the perpetration of these crimes. For example, the news accounts detailed
how the existence of illicit markets for cocaine and face-to-face encounters with drug dealers
facilitate opportunities for officers to engage in drug use, shakedown citizens who deal drugs,

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and/or deal drugs themselves. The news accounts also describe how this context facilitates and
promotes sex-related police crime, whereby police sexually abuse vulnerable citizens they
encounter through their work, including young female drivers, prostituted women, or youths
involved in police explorer programs. Evidence of the occupationally-derived context of these
events is not limited to on-duty crimes. Almost 60% of all of the cases identified in the study
occurred when the officer was technically off-duty. The data demonstrate that the source of a
significant portion of these so-called off-duty crimes also lies within the context of police work
and the perpetrator's role as a police officer, including instances where off-duty officers flash a
badge, an official weapon, or otherwise use their power, authority, and the respect afforded to
them as a means to commit crime. More broadly, the data show that police crime is not solely or
even primarily the product of deviant or defective people; but rather, deviant or defective people
who work within an occupational context that provides them unique and unprecedented
opportunities to perpetrate crimes whether they are on or off-duty.
The third general observation is that police crime is a complex and multi-dimensional
phenomenon both within and across the five types of police crime: alcohol-related, drug-related,
sex-related, violence-related, and profit-motivated police crime. The complex and multidimensional character of police crime was initially underscored within the context of the Mollen
Commission's two-year investigation of drug corruption in New York City during the 1980s and
early 1990s. The Commission identified a complex web of relationships among the operation of
drug markets, drug corruption, and a long list of associated crimes perpetrated by police. Our
data confirmed these observations across jurisdictions throughout the nation; we found that drugrelated police crime tends to spawn the perpetration of various other kinds of criminal behavior
by police. Perhaps more importantly, both the findings of the Mollen Commission and those of

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our own research suggest that the complexity of relationships involved in drug-related police
crime extends to other kinds of police crime and the nature of the phenomenon in general. For
example, the overall complexity of police crime is also demonstrated by the absence of any
clearly discernible line between criminal behavior that occurs on- and off-duty. More evidence
is provided by the analyses linking police crime arrests to federal civil rights lawsuits. These
analyses suggest that at least a portion of those police arrested for a criminal offense are problem
officers that exhibit shortcomings in other aspects of the job. Finally, and perhaps most clearly,
the multi-dimensional character of police crime is demonstrated within the complex web of
statistically significant relationship among variables in our data. For example, Chi-square
analyses involving the entire data set identified 90 variables that were significantly associated
with conviction, and 120 variables that were significantly associated with job loss. The
multivariate analyses identified 16 significant predictors of conviction and 11 significant
predictors of job loss. These points demonstrate the difficulties inherent in both the
identification of clear-cut explanations of police crime and the interpretation of statistical models
designed to isolate these relationships. Perhaps scholarship on the topic needs to start with the
premise that recognizes the multivariate complexity of police crime and the difficulties
associated with both explaining its etiology; and by extension, the formulation of mitigation
strategies.
Sex-related Police Crime
Two points of discussion in regard to the sex-related police crimes include the serious
nature of these offenses and the characteristics of the victims. There were a total of 1,475 cases
of sex-related police crime that ranged widely in seriousness from peeping tom cases to those
that involved murder. Perhaps most notable however is the fact that almost 30% of all the sex-

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related cases were classified as cases of police sexual violence. A review of the most serious
offenses charged in the cases reveals the serious and often-times egregious nature of these
events. There were a total of 422 forcible and statutory rapes, 352 cases of forcible fondling, and
94 sodomy cases. The proportion of cases that involved sexual violence and the most serious
kinds of sex-related offenses was larger than expected based on the existing research and the
comparative absence of empirical studies on the problem of police sexual violence. The large
proportion of serious cases in our data was clearly influenced in part by methodology, since
news-based searches probably tend to capture cases that compelled an arrest because they were
egregious and could not be ignored. No matter the methodology used to identify them, the sheer
number of egregious cases constitutes prima facie evidence that these are not isolated events and
should be the subject of additional organizational resources and empirical scrutiny.
The second concern in regard to sex-related crimes is the age of the victims. Children
seem to be particularly vulnerable to police who perpetrate sex-related crimes. Almost one-half
of the known victims of sex-related crimes were children, and the second-most commonly
occurring category in terms of the victim's relationship to the arrested officer was an unrelated
child. Certain types of sex crime tend to involve victims who are children, including crimes
involving pornography, obscene material, fondling, and promoting the sexual performance of a
child.
The sources of sex-related crimes against children are often occupationally derived. For
example, the news-based content analyses identified various scenarios in which adults allowed
sworn law enforcement officers both access and opportunity to victimize children under their
care, presumably because they trusted them. The criminal courts seemed to respond to these
cases severely, as a clear majority of the sex-related cases with known dispositions included a

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conviction on at least one charge. The likelihood of conviction increased in cases that involved
younger victims, suggesting the expression of some form of moral outrage against police who
use their power and position to victimize children.
Alcohol-related Police Crime
There were 1,405 cases of alcohol-related police crime identified in the study. There
were 960 cases where police were arrested for driving under the influence, and the most serious
offense charged in close to 60% of the alcohol-related cases was DUI. Thus, police DUI is the
major issue of concern in regard to alcohol-related police crime. The study seems to identify
cases where an officer did something that compelled an official law enforcement response rather
than some type of professional courtesy and non-arrest; there are comparatively few run-of-themill cases of DUI in our data. Some of the incident events that accompanied the DUI and seem
to have influenced an arrest included a traffic accident, injuries (sometimes fatal), hit-and-run,
and refusals to cooperate with BAC and/or field sobriety tests. These cases seemed to compel
police action and an arrest because they required some sort of official documentation (e.g., a
traffic accident report), included independent witnesses or victims, or were so egregious that
police ignored the fact that they involved fellow police officers. Moreover, most of these cases
involved a law enforcement agency other than the arrested officer's employer, suggesting that
professional courtesies are more likely to occur among police who work together. In many
instances, it should be noted however, when an officer was suspected of driving under the
influence (and employed by the same agency as the officer conducting the traffic stop or
handling a DUI traffic accident investigation), an outside agency was often brought in to conduct
field sobriety tests and effectuate the arrest of an intoxicated officer. Police DUI criminal case
outcomes seem to be significantly influenced by the geographic location of the case. State

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location was the most influential predictor of conviction. State-level reforms of DUI laws
obviously influence cases outcomes overall, so it seems likely that legislative changes also
influence the disposition of cases of police DUI.
Drug-related Police Crime
There were 739 cases of drug-related police crime. In many ways, our findings both
confirm and extend the findings of the Mollen Commission in regard to the etiology and impact
of drug-related police crime in New York City several decades ago. For example, while all
classes of commonly abused drugs were present, stimulants including cocaine and crack were the
most prevalent category of drug involved in these cases. Cocaine was involved in close to onethird of all the drug-related cases; and taken together cocaine and crack were involved in almost
40% of all the drug-related cases. Moreover, cocaine was the strongest predictor for three of six
decision trees to predict various forms of drug-related misconduct and crime. We found that—
similar to New York City during the 1990s—officers involved in drug-related corruption around
the nation often perpetrate various other kinds of crimes including drug trafficking, facilitation of
the drug trade, shakedowns, and other forms of theft. Less than one-third of the drug-related
cases involved the personal use of drugs by police officers. Our study also seems to suggest the
potential for ongoing problems associated with offenses related to opioid analgesics including
oxycodone and hydrocodone. More than 20% of all the drug-related cases involved either of
these two prescription pain killers. Significant public attention has recently focused on problems
related to opioid analgesics, including increases in overdose deaths and prescription violations
involving these powerful drugs. Our data suggest that these issues also impact police officers
and the nature and character of drug-related police crime.

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In terms of criminal case outcomes, police who perpetrate drug-related crimes are
significantly more likely to be convicted if the officer ultimately loses their job subsequent to the
drug-related arrest. More broadly, we found that job loss predicts conviction in just about every
sort of case of police crime, including those that are sex-related, alcohol-related, drug-related,
violence-related, and profit-motivated. The two primary outcomes analyzed in this study
including criminal case disposition and employment outcome are not isolated events. Job loss
and conviction influence one another in almost every case of police crime. Job loss provides a
context to interpret the perceived need for a criminal conviction and vice versa.
Violence-related Police Crime
There were 3,328 violence-related police crimes identified in the study. These cases
involved the most serious types of violent crime including 104 cases of murder or nonnegligent
manslaughter and 92 robberies; but, they most commonly involved either a simple assault (n =
870) or aggravated assault (n = 570). Less than one-half of the violence-related cases are known
to have resulted in a criminal conviction on at least one charge. But, if the case also involved the
perpetration of a sex-related crime the odds of conviction increase significantly. Moreover,
criminal convictions in violence-related police crimes seem to follow patterns that are consistent
with those of violent crimes in general. For example, cases of violence-related police crime are
significantly less likely to result in conviction if the officer knows the victim. This was also the
case for alcohol-related police crimes, suggesting that in some ways court outcomes in cases of
police crime are influenced by the same factors that shape court outcomes for any other type of
defendant.
Officer-involved domestic violence has become an important topic of concern for
scholars and police executives, and there were 961 cases of the officer-involved domestic

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violence included within the violence-related police crimes. Conviction on at least one charge is
significantly more likely in cases that involve fatal injuries and/or those that involved a violation
of a protection order. Other factors that increase the odds of conviction appear to relate to
factors other than the specific act of domestic violence, including obstruction, vandalism, or if
the case also involves a sex-related offense.
Profit-motivated Police Crime
There were 1,592 cases of profit-motivated police crime identified in the study. There
were 46 different offense categories for the most serious offense charged in these cases, but the
most serious offense charged in about 40% of these cases was unclassified theft, false
pretense/swindle, and/or thefts that involved a drug violation. Most of these cases also occurred
on the street-level—almost 80% of the profit-motivated crimes were perpetrated by officers in a
patrol or other street-level function. Thus, the profit-motivated police crimes in our study more
closely resembled the types of thefts perpetrated by traditional street-level criminals rather than
business executives, professionals, or other societal elites who are commonly the subject of
white-collar crime research. A conviction on at least one criminal charge in the profit-motivated
cases depended significantly on whether the case involved a drug-related shakedown. The
simple odds of conviction are 109 times greater if a profit-motivated case involved a drug-related
shakedown. Similar to other types of police crime, profit-motivated cases that involved an
officer who ultimately lost their job were significantly more likely to result in a criminal
conviction on at least one charged offense.
Civil Rights Litigation as a Correlate of Police Crime
The study included analyses that involved a cross-check of the names of each of the
arrested officers against the master name index in the federal court's PACER system. These

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analyses were used to explore whether arrested officers had ever been named in their official
capacity as a party defendant in a 42 U.S.C. §1983 federal court civil action at some point during
their law enforcement career, and to identify the factors that predict these actions. More
broadly, these analyses can be used to identify arrested officers who could potentially be
described as problem-prone since the federal lawsuit(s) may be indicative of performance
problems in a variety of job-related functions. We are aware of no previous empirical studies
that attempt to link the perpetration of police crimes to other forms of misconduct using the
PACER database. These types of analyses could potentially lead to new and improved
mechanisms to identify and mitigate various forms of police misconduct.
We found that many of the incidents giving rise to the federal court civil actions occurred
years or even decades prior to the arrest, and the lawsuits often had nothing to do with the
incident that prompted the arrest. Thus, the arrest case and the civil lawsuit(s) potentially
indicate the existence of problems across multiple job-related functions, or at least separate
instances of the same types of problems over time. The analyses designed to predict being
named as a party-defendant in federal court civil actions involving claims pursuant to 42 U.S.C.
§1983 seem to indicate at least three important facts. First, officers who are ultimately named as
defendants in §1983 federal court civil actions tend to get sued more than once for a variety of
civil rights causes of action. These lawsuits are not often isolated events. Second, officers who
perpetrate crimes while on-duty are significantly more likely to have been named as a Section
1983 defendant. These are officers whose misconduct tends to occur on-the-job and within their
official capacity, so their misdeeds are more likely to be the subject of civil rights litigation and
defined as involving a specific denial of a citizen's civil rights acting within their state actor role
as a police officer. Third, certain types of police crime tend to predict prior federal civil rights

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litigation. Many of these predictors seemed to be acts that were predatory or at least in some
ways violent or aggressive, including crimes involving cocaine, murder, non-negligent
manslaughter, kidnapping/abduction, and cases of family violence. More research focused on
the link between police crime and prior federal civil rights litigation is obviously necessary in
order to more fully identify, describe, and explore these relationships.
Implications for Policy and Practice
Mechanisms designed to identify and mitigate police misconduct include those that
originate both internal and external to the law enforcement organization. External controls
include local government, citizen review boards, the media, civil lawsuits, and the criminal
courts among others. These external controls clearly maintain certain advantages in terms of the
identification and mitigation of police misconduct. For example, the media often serves to
identify potential cases of misconduct. Likewise, local governments can and often do influence
the budget of law enforcement agencies. So too, civilian review boards have the potential to
integrate citizens and grass-roots organizations into the process of police discipline and reform.
The various external controls, however, do share some problems. Civilian review of the streetlevel decisions of sworn law enforcement officers may engender a loss of trust and hesitancy in
situations that require immediate judgments. Likewise, street-level law enforcement officers will
probably mistrust any sort of external control. Also, external controls are in most cases limited
because they do not exert direct control over the policies and disciplinary processes of state and
local law enforcement organizations.
Internal mechanisms to control police misconduct originate from within each law
enforcement organization and primarily involve strategies to develop professionalism through
hiring and training; and, the creation, maintenance, and enforcement of procedures and rules that

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comprise much of the bureaucracy of the police organization. The advantages of internal
controls are clear: members of the organization including law enforcement executives, first-line
supervisors, and internal affairs units are likely in the best position to identify and punish most
types of police crime. Thus, one of the primary goals of our research was to determine the
degree to which state and local law enforcement organizations identify and respond to cases of
police crime. These data are substantive in terms of policy and practice because they provide a
point of reference in terms of how frequently agencies identify police crimes through an arrest
and opportunities to assess the typical discipline imposed by law enforcement organizations in
these cases—and ultimately, the basis upon which to devise strategies to improve the internal
control of police misconduct.
Our data indicate that in most cases of police crime the arrest is made by a law
enforcement agency other than the officer's employing agency. Two-thirds of the 6,724 police
crime arrest cases originated from an arrest made by an agency other than the employing agency.
Why do employing law enforcement agencies often fail to make the arrest in cases of police
crime? The content analyses identified clear cases in which the employing agency should have
made the arrest and failed to do so. Policing scholars have always recognized that police do not
typically arrest other police officers. Sworn officers are likely to use their discretion and extend
a professional courtesy to other law enforcement officers, especially co-workers or in some cases
their close friends. These kinds of cases represent a failure of internal control and demonstrate
what has sometimes been referred to as a fox in the henhouse problem, one that illustrates the
need for some type of external controls of police misconduct and crime. One the other hand, the
low proportion of arrests made by the employing agency may also be legitimate. For example,
officers in many cases do not live within the jurisdictions they police, or they may live within the

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jurisdiction but choose to perpetrate their crimes elsewhere. The content analyses also identified
many arrest cases in which law enforcement executives purposively contacted other state and
local law enforcement agencies to investigate suspected police crimes or arrest one of their own
sworn officers to promote objectivity and limit the potential for bias in the investigation.
Clearly, determinations in regard to whether agencies can be relied upon to identify cases and the
overall effectiveness of internal mechanisms of control in this regard cannot be made solely on
the basis of whether the employing agency made the arrest in a case of police crime.
The ultimate internal control involves the imposition of progressive discipline and final
adverse employment outcomes, including reassignment, suspension, resignation, and termination
from the job. Very little is known about the operation of internal mechanisms of control and the
imposition of adverse employment outcomes in cases of police crime beyond perhaps studies
focused on a single police agency. The next part of this section is a discussion on the imposition
of adverse employment outcomes in terms of both the entire data set and selected results within
the types of police crime that we believe justify further comment. We then provide specific
recommendations to law enforcement agencies based on some of these findings.
Overall, sworn state and local law enforcement officers are known to have lost their job
as a result of the arrest in slightly more than one-half of the cases (54%). Clearly, law
enforcement organizations do not automatically terminate sworn officers who perpetrate crimes.
The organizational response to police crime varies rather widely across alcohol-related, drugrelated, sex-related, violence-related, and profit-motivated police crime. An arrest in and of
itself matters much less than the type of underlying criminal behavior that prompted the arrest.
For example, sworn law enforcement officers were known to have lost their job as a result of an
arrest in only 38% of the alcohol-related cases, while officers were known to have lost their job

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as a result of the arrest in 72% of the sex-related cases and 70% of the drug-related cases. The
percentage of cases in which officers were known to have lost their jobs for violence-related and
profit-motivated cases was in between these extremes (53% and 68% respectively). The odds of
job loss decreased among drug-related cases when those cases also included an alcohol-related
offense. Differential rates of job loss across types of police crime may reflect perceptions of
threats to organizational legitimacy or degrees of outrage that vary considerably across crime
types. For example, cases involving police sexual violence (i.e., both sex- and violence-related
police crime) are significantly more likely to result in job loss than other types of police crime.
Other specific types of police crime are significantly less likely to result in job loss, including
drug-related crimes that involve marijuana and crimes of family violence. The comparatively
low rate at which family violence crimes result in job loss seems particularly troubling given the
fact that persons convicted of a qualifying misdemeanor crime of domestic violence—including
sworn law enforcement officers—are prohibited from possessing any firearm or ammunition
pursuant to the Lautenberg Amendment (1996).
More broadly, the data clearly demonstrate that the imposition of adverse employment
outcomes in cases of police crime is not an isolated event and depends upon other factors that
provide a meaningful context to these decisions and shape the perceptions of law enforcement
administrators and their determinations about appropriate levels of discipline. The most obvious
of the contextual factors that influence the likelihood of job loss is the imposition of other forms
of organizational discipline. State and local law enforcement organizations are significantly less
likely to separate (through involuntary termination or voluntary resignation) sworn officers from
the job who have instead been suspended and/or re-assigned as a result of an arrest. The
operation of external mechanisms of control also influences organizational decisions on whether

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or not to terminate the employment of arrested sworn officers, specifically criminal court
dispositions and whether or not an officer had been sued in federal court for a civil rights
violation. The odds that an officer will lose his or her job significantly increase if they are
criminally convicted on at least one charged offense associated with the arrest. Law enforcement
agencies are likely to view the conviction as a confirmation of both the criminal act and a degree
of public condemnation that is impossible to dismiss or ignore. Likewise, law enforcement
organizations clearly consider federal litigation and charges of other civil rights violations over
the course of the career as a context relevant to determining whether the arrestee is a problemprone officer who is more trouble than he or she is worth to the organization. Law enforcement
administrators and scholars interested in determining effective organizational responses to police
misconduct and crime need to recognize organizational discipline as a multivariate phenomenon
that should be interpreted within the context of other forms of control.
Decisions in regard to adverse employment outcomes also seem to depend on the degree
to which the arrested officer was engaged in his or her official role as a police officer at the time
of the arrest. For example, the odds of job loss in cases involving alcohol-related crimes
significantly increased if the officer was on-duty, and the odds of job loss significantly increased
in cases involving sex-related crime and profit-motivated crime if the officer was acting in his or
her official capacity. Criminal behavior that occurs on-duty and/or while an officer is acting in
his or her official capacity relates more directly to the job and poses more significant threats to
organizational legitimacy; and hence, tends to increase the likelihood that severe penalties will
be imposed in any particular case of police crime.
Finally, the likelihood of job loss sometimes depends on the closely associated factors of
agency size and the character of the jurisdiction, particularly for cases that involve either alcohol

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and/or drugs. For example, cases that involve alcohol-related crimes are significantly more
likely to result in job loss as the size of the employing police agency decreases. Likewise, cases
that involve drug-related crime are significantly more likely to result in job loss when they occur
in non-metropolitan counties. Sworn law enforcement officers who perpetrate these types of
crimes seem to be more vulnerable to the most punitive employment outcomes when they work
in smaller police agencies located within nonmetropolitan jurisdictions than officers who patrol
big-city beats. Crimes such as police DUI and or small-scale drug use are probably much more
newsworthy and difficult to ignore when they occur in small towns or rural jurisdictions. On the
other hand, these types of police crime may be less likely to be defined as a big deal by law
enforcement executives and residents who live in large metropolitan areas. Officers employed
by large urban law enforcement agencies may also in some ways be protected from the most
severe penalties by the complexity of large bureaucratic structures and/or more intricate
disciplinary procedures.
The points on final adverse employment outcomes provide context for the identification
and discussion of specific recommendations to law enforcement agencies based on our research.
Our first and perhaps most salient general observation about the data was that police crimes are
not uncommon and that they occur with some regularity in jurisdictions across the nation. The
sheer number of police crimes directly contradicts the presumption that they are perpetrated by a
small cadre of problem-prone officers; and, the fact that roughly two-thirds of all the cases
originated from an arrest made by an agency other than the employing agency reveals that in at
least some cases agencies are not aware of the crimes perpetrated by their own officers. State
and local law enforcement organizations routinely compel applicants to disclose criminal arrests
or orders of protection against them during pre-employment screening. Clearly, these

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requirements need to be extended to all on-the-job sworn employees to compel them to disclose
any criminal arrests or orders of protection so that police agencies can document and respond to
known cases of police crime. Strategies to mitigate police crime cannot succeed without
identification, and our research demonstrates the problems associated with the omission of
obvious and necessary policies to compel the disclosure of criminal arrests and protection orders.
Relatedly, state and local law enforcement organizations need to conduct routine annual
criminal background checks of every sworn officer. Annual criminal background checks serve
two primary purposes. First, they provide an additional check necessary to ensure compliance
with self-report procedures outlined above and to identify cases in which officers fail to disclose
arrests, convictions, or orders of protection lodged against them. Second, annual criminal
background checks would increase compliance with the requirements of the Lautenberg
Amendment (1996). Our data on sworn law enforcement officers arrested for crimes associated
with domestic violence demonstrates how some officers escape appropriate penalties, and
maintain their law enforcement job that requires them to carry a firearm. The problem stems at
least in part from the absence of any central registry or database of persons convicted of
misdemeanor crimes of domestic violence. Law enforcement agencies should require annual
criminal background checks to ensure the identification and documentation of police crimes
within the organization and to maintain compliance with federal law designed to protect victims
of family violence—especially in cases where the abuser is among those expected to enforce
domestic violence statutes.
We found that the organizational response to police crime varies rather widely across
jurisdictions, and that law enforcement agencies do not automatically terminate officers who
perpetrate crimes. These disparities likely relate to a host of factors associated with

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organizational characteristics, characteristics of the offense(s), and the need to consider
disciplinary actions within the unique context of each individual case of police crime. So too,
many state and local law enforcement agencies operate under terms of a collective bargaining
agreement that structures organizational responses and acceptable forms of progressive discipline
and imposition of adverse employment outcomes. However in most cases, law enforcement
executives would benefit from some sort of formalized procedures designed to guide disciplinary
decisions and organizational responses to police crimes within the ranks. The guidelines would
necessarily be broad in scope and allow a degree of administrative discretion and case-by-case
consideration, but their existence would at the least ensure that some type of process is in place
and contribute to both regularity and consistency of organizational responses over time.
Finally, state and local law enforcement agencies should move toward the integration of
data on police crimes within existing systems designed to increase accountability as well as those
intended to provide practical assistance to sworn law enforcement officers both on and off the
job. Many law enforcement agencies have already implemented comprehensive personnel
assessment systems that collect a wide range of data and have the means to address a broad range
of problems, most commonly misconduct related to the use of force and citizen complaints.
These early intervention systems need to include the collection of data on the criminal arrests of
sworn law enforcement officers, because we know that police crimes occur with some regularity
and that their occurrence can in some cases be used as an indicator of problems in other areas of
the job. More bluntly, systems designed to provide an early warning of officers who are
problem-prone cannot be considered complete if they are unable to identify sworn law
enforcement officers who have perpetrated a criminal offense.

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Employee Assistance Programs (EAPs) could also utilize data on criminal arrests of
sworn law enforcement officers to identify and target officers who need help. These programs
typically provide personal and job-related counseling services. Police crime arrests are an
obvious indicator of the need for services to address a host of occupationally-derived problems
including psychological and mental health issues related to violence exposure, trauma, posttraumatic stress disorder, substance abuse, lob-related stress, and work-family spillover effects.
This is especially true for sworn law enforcement officers who are combat veterans of the
military. In this regard, the content analyses revealed numerous arrest cases that were obviously
part of a much wider milieu and the build-up of long-term stressors—the proverbial tip of an
iceberg of job-related and personal crises that can in some cases lead to complete psychological
breakdowns that produce catastrophic consequences for the officer and his or her victims. These
events and unraveling can occur across the life course of any sworn law enforcement officer’s
career. Law enforcement agencies need to identify police crimes so they can use the information
to help officers who obviously need it and refer them to programs designed to provide practical
and in some cases life-saving assistance to them.
Implications for Further Research
The current research project identified and analyzed an unprecedented amount of data on
the arrests of nonfederal sworn law enforcement officers in the United States. The research data
collection methodology was designed by Stinson (2009) and allows for the aggregation of
information on the phenomenon of police crime that would not otherwise been possible (Payne,
2013). It would also be very difficult to process and code the content of the vast amount of raw
data analyzed in the current project without sophisticated database resources. The project is
supported by OnBase, an enterprise content management (ECM) database system. Originally the

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research team utilized OnBase solely to archive digital image files of all the paper-based news
articles, court records, and coding sheets that were collected and analyzed by Stinson and
colleagues as part of their research on police crime. Over the past several years Stinson has
enhanced the project database with additional electronic files, including video files, audio files,
and the ability to search the full text of all the digital images contained in the project’s digital
imaging database. Appendix E presents an entity relationship of Stinson’s police crime database.
Several database components were added to the project ECM database to support the
current study, including an object-relational database that allows for data to be organized in
tables (a relational database component), as well as seamless integration with the digital imaging
and video files (an object-oriented database component) among others. Coding of content was
completed with a customized PC-based coding instrument using the IBM/SPSS Data Collection
software application. The coding instrument system pulls case-specific data from the relational
database into the on-screen coding instrument for each case to be coded, thus reducing coder
duplication of effort and the potential for coding errors. The data from the completed coding
instrument for each case is converted to an SPSS data file for subsequent statistical analyses, and
data files are also converted to an electronic coding sheet in an MS Word (a facsimile of our
paper-based coding instrument) and indexed in OnBase with other case-specific electronic files.
The data collection methodology and ECM research project database design deployed in
the current study can serve as an integrated model for other social science research projects
utilizing big data in a variety of structured and unstructured paper-based and electronic formats.
Most large research universities already utilize ECM database systems for nonresearch
applications (including admissions, operations, and administrative functions). These ECM
database systems could be made available to faculty researchers for database development in

209
large-scale content analysis research projects and other research methodologies. Similarly,
OnBase and other ECM database systems are often utilized by federal, state, and local
government agencies, including courts and law enforcement agencies. The costs of making such
systems available to research teams for research database projects would be de minimis when the
ECM system is already deployed for other uses within an organization.
Stinson and colleagues continue to collect data on the criminal arrests of sworn law
enforcement officers at state and local law enforcement agencies across the United States at a
rate of approximately 1,100 new arrest cases involving approximately 800-950 sworn law
enforcement officers arrested annually. The project ECM database now includes 11,131 criminal
arrest cases (as of this writing, for the eleven year period January 2005 through December 2015)
involving the arrests of 9,391 individual sworn law enforcement officers who were employed by
3,666 nonfederal state and local law enforcement officers located in 1,543 counties and
independent cities in all 50 states and the District of Columbia. The research team plans to
continue with the research project as a longitudinal trend study of police crime in the United
States.
We hope that the products disseminated from this study (and our larger project on police
crime research) serve as a starting point for the research projects of other scholars and student
researchers. An electronic copy of the data set (together with supporting documentation) for the
current study will be deposited in SPSS portable files with the National Archives of Criminal
Justice Data (NACJD) at the Inter-university Consortium for Political and Social Research
(ICPSR).

210
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232
DISSEMINATION OF RESEARCH FINDINGS
Publications
Journal Articles
Stinson, P. M. (2015). Police crime: The criminal behavior of sworn law enforcement officers.
Sociology Compass, 9(1), 1-13. doi:10.1111/soc4.12234
Stinson, P. M., Brewer, S. L., Mathna, B. E., Liederbach, J., & Englebrecht, C. M. (2014). Police
sexual misconduct: Arrested officers and their victims. Victims & Offenders, 10(2), 117-151.
doi:10.1080/15564886.2014.939798
Stinson, P. M., Liederbach, J., Brewer, S. L., Jr., & Mathna, B. E. (2014). Police sexual
misconduct: A national scale study of arrested officers. Criminal Justice Policy Review, 26(7),
665-690. doi:10.1177/0887403414526231
Stinson, P. M., & Watkins, A. M. (2014). The nature of crime by school resource officers:
Implications for SRO programs. Sage Open, 4(1), 1-10. doi:10.1177/2158244014521821
Stinson, P. M., Todak, N. E., & Dodge, M. (2013). An exploration of crime by policewomen.
Police Practice and Research: An international journal, 16(1), 79-93.
doi:10.1080/15614263.2013.846222
Stinson, P. M., Liederbach, J., Brewer, S. L., Jr., & Todak, N. E. (2013). Drink, drive, go to jail?
A study of police officers arrested for drunk driving. Journal of Crime and Justice, 37(3), 356376. doi:10.1080/0735648X.2013.805158
Stinson, P. M., Liederbach, J., Brewer, S. L., Jr., Schmalzried, H. N., Mathna, B. E., & Long, K.
L. (2013). A study of drug-related police corruption arrests. Policing: An International Journal
of Police Strategies & Management, 36(3), 491-511. doi:10.1108/PIJPSM-06-2012-0051
Stinson, P. M. & Liederbach, J. (2013). Fox in the henhouse: A study of police officers arrested
for crimes associated with domestic and/or family violence. Criminal Justice Policy Review, 24,
601-625. doi:10.1177/0887403412453837
Non-refereed Articles
Stinson, P. M. & Liederbach, J. (2013). Research-in-brief: Sex-related police misconduct. Police
Chief, 80(8), 14-15.
Stinson, P. M. & Liederbach, J. (2012). Research-in-brief: Misconduct by experienced police
officers. Police Chief, 79(11), 12.
Stinson, P. M. & Liederbach, J. (2012). Research-in-brief: Officer-involved domestic violence.
Police Chief, 79(9), 14.

233
Technical Reports
Stinson, P. M., Calogeras, Z. J., DiChiro, N. L., & Hunter, R. K. (2015). California police sexual
misconduct arrest cases, 2005-2011. Prepared for the California Research Bureau, California
State Library, Sacramento, CA.
Podcasts
Stinson, P. M. (2015, December). Police shootings: Does video evidence matter? Police Integrity
Lost (Audio podcast], Episode 27. Available at iTunes.
Stinson, P. M. (2015, November). What we know about police crime. Police Integrity Lost
(Audio podcast], Episode 26. Available at iTunes.
Stinson, P. M. (2015, October). Violence by school resource officers. Police Integrity Lost
(Audio podcast], Episode 25. Available at iTunes.
Stinson, P. M. (2015, July). Police crime in America: Phil Stinson at Porcfest 2015. Police
Integrity Lost [Audio podcast], Episode 24. Available at iTunes.
Stinson, P. M. (2015, May). Police shootings in Albuquerque. Police Integrity Lost [Audio
podcast], Episode 23. Available at iTunes.
Stinson, P. M. (2015, January). Research on crimes committed by sworn police officers. Police
Integrity Lost [Audio podcast], Episode 22. Available at iTunes.
Stinson, P. M. (2014, December). Police crime: Grand juries, juries and conviction of officers.
Police Integrity Lost [Audio podcast], Episode 21. Available at iTunes.
Stinson, P. M. (2014, November). Constitutional torts, section 1983, and police misconduct:
Presentation at 2014 ASC Conference. Police Integrity Lost [Audio podcast], Episode 20.
Available at iTunes.
Stinson, P. M. (2014, August). Gun-involved police crime arrests. Police Integrity Lost [Audio
podcast], Episode 19. Available at iTunes.
Stinson, P. M., & Brewer, S. L. (2014, February). Victims of police sexual misconduct:
Presentation at 2014 ACJS conference. Police Integrity Lost [Audio podcast], Episode 18.
Available at iTunes.
Stinson, P. M. (2014, January). Research performance progress report for July-December 2013.
Police Integrity Lost [Audio podcast], Episode 17. Available at iTunes.
Stinson, P. M., & Watkins, A. M. (2013, December). Crime by school resource officers. Police
Integrity Lost [Audio podcast], Episode 16. Available at iTunes.

234
Stinson, P. M., & Brewer, S. L. (2013, November). Police integrity lost: Presentation at 2013
ASC conference. Police Integrity Lost [Audio podcast], Episode 15. Available at iTunes.
Stinson, P. M., & Liederbach, J. (2013, October). Police sexual misconduct: Policy implications.
Police Integrity Lost [Audio podcast], Episode 14. Available at iTunes.
Stinson, P. M. (2013, August). Police Crime Research Findings. Police Integrity Lost [Audio
podcast], Episode 13. Available at iTunes.
Stinson, P. M. (2013, July). Research performance progress report for January-June 2013.
Police Integrity Lost [Audio podcast], Episode 12. Available at iTunes.
Stinson, P. M., & Liederbach, J. (2013, June). Police sexual misconduct arrests. Police Integrity
Lost [Audio podcast], Episode 11. Available at iTunes.
Stinson, P. M. (2013, May). Using a content management system: Police crime research methods
part 3. Police Integrity Lost [Audio podcast], Episode 10. Available at iTunes.
Stinson, P. M., Brewer, S. L., & Liederbach, J. (2013, April). Drunk driving cops. Police
Integrity Lost [Audio podcast], Episode 9. Available at iTunes.
Stinson, P. M., & Brewer, S. L. (2013, March). Using Google News for data collection: Police
crime research methods part 2. Police Integrity Lost [Audio podcast], Episode 8. Available at
iTunes.
Stinson, P. M., & Brewer, S. L. (2013, February). Decision Tree Analysis: Police crime research
methods part 1. Police Integrity Lost [Audio podcast], Episode 7. Available at iTunes.
Stinson, P. M., & Liederbach, J. (2013, January). Late-stage police crime. Police Integrity Lost
[Audio podcast], Episode 6. Available at iTunes.
Stinson, P. M., & Todak, N. E. (2012, December). Crime by policewomen. Police Integrity Lost
[Audio podcast], Episode 5. Available at iTunes.
Stinson, P. M., & Liederbach, J. (2012, November). Police Criminal Misuse of TASERs. Police
Integrity Lost [Audio podcast], Episode 4. Available at iTunes.
Stinson, P. M., & Liederbach, J. (2012, October). Off-Duty police crime. Police Integrity Lost
[Audio podcast], Episode 3. Available at iTunes.
Stinson, P. M., & Liederbach, J. (2012, September). Officer-involved domestic violence. Police
Integrity Lost [Audio podcast], Episode 2. Available at iTunes.
Stinson, P. M., Liederbach, J., & Brewer, S. L. (2012, August). Police drug corruption. Police
Integrity Lost [Audio podcast], Episode 1. Available at iTunes.

235
Stinson, P. M. & Liederbach, J. (2012). Fox in the henhouse: A study of police officers arrested
for crimes associated with domestic and/or family violence. Criminal Justice Policy Review
[Audio podcast]. Available at:
http://cjp.sagepub.com/content/early/2012/07/18/0887403412453837/suppl/DC1
Research Briefs
Stinson, P. M., Liederbach, J., Brewer, S. L., & Todak, N. E. (2013). Officers arrested for drunk
driving: Police Integrity Lost Research Brief One-Sheet, No. 6. Bowling Green, OH: Bowling
Green State University. Available at: http://www.bgsu.edu/departments/dhs/crju/file129810.pdf
Stinson, P. M., Reyns, B. W., & Liederbach, J. (2013). Police criminal misuse of conductive
energy devices: Police Integrity Lost Research Brief One-Sheet, No. 5. Bowling Green, OH:
Bowling Green State University. Available at:
http://www.bgsu.edu/departments/dhs/crju/file129809.pdf
Stinson, P. M., & Liederbach, J. (2012). Officer-involved domestic violence: Police Integrity
Lost Research Brief One-Sheet, No. 4. Bowling Green, OH: Bowling Green State University.
Available at: http://www.bgsu.edu/downloads/chhs/file118292.pdf
Stinson, P. M., Liederbach, J., Brewer, S., Schmalzried, H., Mathna, B. E., & Long, K. (2012).
Police drug corruption: What are the drugs of choice? Police Integrity Lost Research Brief OneSheet, No. 3. Bowling Green, OH: Bowling Green State University.
Available at: http://www.bgsu.edu/downloads/chhs/file118291.pdf
Stinson, P. M., Liederbach, J., & Freiburger, T. L. (2012). Off-duty crime by police officers:
Police Integrity Lost Brief One-Sheet, No. 2. Bowling Green, OH: Bowling Green State
University. Available at: http://www.bgsu.edu/downloads/chhs/file118290.pdf
Stinson, P. M., Liederbach, J., & Freiburger, T. L. (2012). Late-stage police crime: Is it an exit
Strategy? Police Integrity Lost Research Brief One-Sheet, No. 1. Bowling Green, OH: Bowling
Green State University. Available at: http://www.bgsu.edu/downloads/chhs/file118289.pdf
Presentations
Stinson, P. M. (2015, November 4). Police crime: What we’ve learned and what we don’t know.
Center for Family and Demographic Research, Bowling Green State University, Bowling Green,
OH.
Stinson, P. M. (2015, October 29). Police crime in the United States: From shootings to
shoplifting. Owens Community College, Findlay, OH.
Stinson, P. M. (2015, October 21). Use of data in research on police crime. Introduction to Data
Science Seminar, Department of Mathematics & Statistics, Bowling Green State University,
Bowling Green, OH.

236
Ory, R. & Stinson, P. M. (2015, September 24). Intercoder reliability assessment of
supplemental document coding in a quantitative content analysis study of police crime in the
United States. Annual meeting of the Midwestern Criminal Justice Association, Chicago, IL.
Stinson, P. M. (2015, June 27). Police integrity lost: Police crime in the United States. 12th
Annual Porcupine Freedom Festival (“PorcFest”), Free State Project, Lancaster, NH.
Buerger, M. E., & Stinson, P. M. (2015, March 16). Police misconduct & implicit bias research:
Bridging the gap? Ohio Attorney General, Bureau of Criminal Investigation, Bowling Green,
OH.
Stinson, P. M., Brewer, S. L., & Bridges, J. (2015, March 5). Violence-related police crime
arrests in the United States. Annual meeting of the Academy of Criminal Justice Sciences,
Orlando, FL.
Stinson, P. M., Brewer, S. L., Lanese, T., & Wilson, M. A. (2014, November 21). Federal civil
rights litigation pursuant to 42 U.S.C. §1983 as a correlate of police misconduct. Annual
meeting of the American Society of Criminology, San Francisco, CA.
Stinson, P. M., Carmack, E. J., Frankhouser, J. M., & Wilson, M. A. (2014, November 20).
Police crime arrests in the United States, 2011. Annual meeting of the American Society of
Criminology, San Francisco, CA.
Stinson, P.M., Brewer, S.L., Englebrecht, C.M., Liederbach, J., & Mathna, B.E. (2014, February
21). Police sexual misconduct: Arrested officers and their victims. Annual meeting of the
Academy of Criminal Justice Sciences, Philadelphia, PA.
Stinson, P. M. (2013, December 12). Police integrity lost: A study of law enforcement officers
arrested: Lessons learned. Bowling Green State University, College of Health and Human
Services, Colloquium.
Stinson, P. M., Liederbach, J., Lab, S. P., & Brewer, S. L. (2013, November 21). Police integrity
lost: A study of law enforcement officers arrested. Annual meeting of the American Society of
Criminology, Atlanta, GA.
Stinson, P. M., Brewer, S. L., Englebrecht, C. M., Mathna, B. E., & Liederbach, J. (2013,
November 21). Police sexual misconduct arrests: An exploratory CART analysis. Annual
meeting of the American Society of Criminology, Atlanta, GA.
Stinson, P. M. (2013, July 30). Police integrity lost: Preliminary findings of a national study of
law enforcement officers arrested. Conference on Police Misconduct sponsored by the APA
Division on Psychologists in Public Service, Section on Police and Public Safety at the annual
meeting of the American Psychological Association, Honolulu, HI.

237
Stinson, P. M., Liederbach, J., Brewer, S. L., Englebrecht, C. E., & Mathna, B. E. (2013, March
21). Police sexual misconduct arrests: An exploratory study. Annual meeting of the Academy of
Criminal Justice Sciences, Dallas, TX.
Stinson, P. M., Liederbach, J., Todak, E. N., & Brewer, S. L. (2012, November 15). Drunk
driving cops: A study of police officers arrested for DUI, 2005-2010. Annual meeting of the
American Society of Criminology, Chicago, IL.
Stinson, P. M., Liederbach, J., Brewer, S. L., Jr., Schmalzried, H. N., Mathna, B. E., & Long, K.
L. (2012, November 15). CHAID analysis of drug-related police corruption arrests. Annual
meeting of the American Society of Criminology, Chicago, IL.
Stinson, P. M. (2012, June 20). Police integrity lost: Preliminary findings from a study of law
enforcement officers arrested, 2005-2011. 2012 National Institute of Justice (NIJ) Conference,
Arlington, VA.
Stinson, P. M., Liederbach, J., Schmalzried, H., Long, K. L., & Mathna, B. E. (2012, March 16).
Officers’ drugs of abuse: A study of drug-related police crime. Annual meeting of the Academy
of Criminal Justice Sciences, New York, NY.

238
Table 1. Police Crime Arrest Cases, 2005-2011: Arrested Officers and Employing Agencies (N = 6,724)
n
(%)
n
(%)
Sex
Male
Female

6,357
367

(94.5)
(5.5)

Age
19-23
24-27
28-31
32-35
36-39
40-43
44-47
48-51
52-55
56 or older
Missing

136
603
886
967
1,081
885
614
343
178
170
861

(2.0)
(9.0)
(13.2)
(14.4)
(16.1)
(13.2)
(9.1)
(5.1)
(2.6)
(2.5)
(12.8)

Years of Service
0-2
3-5
6-8
9-11
12-14
15-17
18-20
21-23
24-26
27 or more years
Missing

756
954
666
622
507
409
387
194
129
156
1,944

(11.2)
(14.2)
(9.9)
(9.3)
(7.5)
(6.1)
(5.8)
(2.9)
(1.9)
(2.3)
(28.9)

Arresting Agency
Employing Agency
Another Agency

2,277
4,447

(33.9)
(66.1)

Officer Duty Status
On-Duty
Off-Duty

2,793
3,931

(41.5)
(58.5)

Rank
Officer
Detective
Corporal
Sergeant
Lieutenant
Captain
Major
Colonel
Deputy Chief
Chief

5,095
369
123
581
177
63
12
4
49
251

(75.8)
(5.5)
(1.8)
(8.7)
(2.6)
(0.9)
(0.2)
(0.1)
(0.7)
(3.7)

Function
Patrol & Street Level
Line/Field Supervisor
Management

5,464
881
379

(81.3)
(13.1)
(5.6)

Region of United States
Northeastern States
Midwestern States
Southern States
Western States

1,430
1,380
2,906
1,008

(21.3)
(20.5)
(43.2)
(15.0)

Level of Rurality
Metropolitan County
Non-Metro County

5,711
1,013

(84.9)
(15.1)

(%)

Agency Type
Primary State Police
Sheriff's Office
County Police Dept.
Municipal Police Dept.
Special Police Dept.
Constable
Tribal Police Dept.
Regional Police Dept.

269
1,109
226
4,915
174
15
14
2

(4.0)
(16.5)
(3.4)
(73.1)
(2.6)
(0.2)
(0.2)
(0.0)

Full-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499
500-999
1,000 or more

31
59
274
376
684
679
704
859
653
548
1,857

(0.4)
(0.9)
(4.1)
(5.6)
(10.2)
(10.1)
(10.5)
(12.8)
(9.7)
(8.1)
(27.6)

Part-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499

5,008
218
528
419
372
120
49
8
2

(74.5)
(3.3)
(7.9)
(6.2)
(5.5)
(1.8)
(0.7)
(0.1)
(0.0)

239
Table 2. Most Serious Offense Charged in Police Crime Arrest Cases, 2005-2011 (N = 6,724)
n
(%)
Simple Assault
Driving Under the Influence
Aggravated Assault
Forcible Fondling
Forcible Rape
Drug / Narcotic Violation
All Other Larceny
All Other Offenses
Intimidation
False Pretenses / Swindle
Weapons Law Violation
Official Misconduct / Oppression / Violation of Oath
False Report / False Statement
Murder / Nonnegligent Manslaughter
Burglary / Breaking & Entering
Robbery
Theft from Building
Statutory Rape
Other Sex Crime
Extortion / Blackmail
Forcible Sodomy
Obstructing Justice
Pornography / Obscene Material
Civil Rights Violation
Bribery
Embezzlement
Disorderly Conduct
Negligent Manslaughter
Arson
Counterfeiting/ Forgery
Destruction of Property / Vandalism

877
841
572
352
322
308
274
265
255
218
143
139
129
125
112
109
103
100
98
95
94
93
86
84
80
79
67
62
57
51
46

(13.0)
(12.5)
(8.5)
(5.2)
(4.8)
(4.6)
(4.1)
(3.9)
(3.8)
(3.2)
(2.1)
(2.1)
(1.9)
(1.9)
(1.7)
(1.6)
(1.5)
(1.5)
(1.5)
(1.4)
(1.4)
(1.4)
(1.3)
(1.2)
(1.2)
(1.2)
(1.0)
(0.9)
(0.8)
(0.8)
(0.7)

Online Solicitation of a Child
Prostitution
Stolen Property Offenses
Indecent Exposure
Kidnapping / Abduction
Shoplifting
Hit & Run
Restraining Order Violation
Impersonation
Drunkenness
Theft from Motor Vehicle
Family Offenses, Nonviolent
Liquor Law Violation
Evidence: Destroying / Tampering
Assisting or Promoting Prostitution
Wire Fraud
Sexual Assault with an Object
Gambling: Operating / Promoting
Credit Card Fraud / ATM Fraud
Motor Vehicle Theft
Trespass of Real Property
Wiretapping, Illegal
Incest
Gambling: Betting / Wagering
Theft of Motor Vehicle Parts or Accessories
Peeping Tom
Welfare Fraud
Pocket-picking
Theft from Coin-operated Machine
Bad Checks

Note . Table equals 99.9%. The last four categories collectively account for the missing 0.1%.

(%)

44
42
39
38
37
31
27
25
22
22
21
20
19
17
12
10
10
9
7
7
7
6
4
3
3
2
1
1
1
1

(0.7)
(0.6)
(0.6)
(0.6)
(0.6)
(0.5)
(0.4)
(0.4)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
(0.2)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.0)
(0.0)
(0.0)
(0.0)
(0.0)
(0.0)
(0.0)

240
Table 3. Victim Characteristics in Police Crime Arrest Cases, 2005-2011 (N = 6,724)
n
(%) (Valid %)
Victim's Sex
Female
Male
Missing

2,246
1,422
3,056

(33.4)
(21.2)
(45.4)

(61.2)
(38.8)

Victim's Age
Birth-11
12-13
14-15
16-17
18-19
20-24
25-32
33-41
42 or older
Missing

185
141
248
213
121
223
296
214
207
4,876

(2.7)
(2.1)
(3.7)
(3.2)
(1.8)
(3.3)
(4.4)
(3.2)
(3.1)
(72.5)

(10.0)
(7.6)
(13.4)
(11.5)
(6.6)
(12.1)
(16.0)
(11.6)
(11.2)

(%)

(Valid %)

346
68
202
136
177
95
673
2,237
2,790

(5.1)
(1.0)
(3.0)
(2.0)
(2.7)
(1.4)
(10.0)
(33.3)
(41.5)

(8.8)
(1.7)
(5.1)
(3.5)
(4.5)
(2.4)
(17.1)
(56.9)

Victim's Law Enforcement Status
Victim is Not a Police Officer
Victim is a Police Officer
Missing

3,738
229
2,757

(55.6)
(3.4)
(41.0)

(94.2)
(5.8)

Victim Adult or Child
Adult
Child
Missing

3,051
939
2,734

(45.3)
(14.0)
(40.7)

(76.5)
(23.5)

241
Table 4. Chi-Square Predicting Conviction
Variable Label
Gender
Rank
FTSWORN cat
State
Urban to Rural
Arresting Agency
Aggravated Assault
Simple Assault
Intimidation
Bribery
Burglary
Drug/Narcotic
Embezzlement
Extortion/Blackmail
False Pretenses/Swindle
Impersonation
Wire Fraud
Theft from Building
Theft / all other larceny
Pornography
Robbery
Forcible Rape
Forcible Sodomy
Forcible Fondling
Statutory Rape
Online Solicitation of a Child
Stolen Property Offenses
Weapons Law violation
Criminal Civil Rights Violation
Driving Under the Influence
Evidence: Destroying/Tampering
Family Offenses, Nonviolent
Liquor Law violation
Gender of Victim

Variable
V5
V7
V10
V11
V13
V14
V16
V17
V18
V19
V20
V23
V25
V26
V27
V29
V31
V43
V47
V49
V52
V53
V54
V56
V58
V60
V62
V63
V65
V67
V69
V71
V73
V81

N
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
2225

χ2
5.482
26.257
36.001
142.585
36.934
8.497
34.925
127.681
11.156
10.856
13.870
56.106
19.423
26.793
8.964
5.048
5.931
5.831
7.746
21.882
12.581
3.967
13.219
11.077
10.100
6.657
10.333
6.421
6.770
13.548
5.688
25.104
4.962
6.922

df
1
9
10
50
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.019
.002
.000
.000
.000
.004
.000
.000
.001
.001
.000
.000
.000
.000
.003
.025
.015
.016
.005
.000
.000
.046
.000
.001
.001
.010
.001
.011
.009
.000
.017
.000
.026
.009

V
.037
.082
.096
.190
.097
.046
.094
.180
.053
.053
.059
.119
.070
.083
.048
.036
.039
.038
.044
.075
.057
.032
.058
.053
.051
.041
.051
.040
.041
.059
.038
.080
.036
.056

242
Age of Victim
Is the Victim a Child?
Victim's Relationship to Offender
Internal vs. Organizational
Drug-related
Alcohol-related
Sex-related
Violence-related
Profit-motivated
Police Sexual Violence
Driving While Female Encounter
Officer was Reassigned
Officer was Suspended
Discussion of Agency Scandal/Coverup
Citizen Complaint as Method of Detection
DUI-related Traffic Accident
Injuries in DUI-related Traffic Accident
DUI in a Personally-owned Vehicle
Off-Duty Intervened in Existing Dispute
Family Violence
Heroin
Oxycodone
Benzodiazepines
Cocaine
Crack
Amphetamine/Methamphetamine
Marijuana
OIDV: Weapon: Gun dept-issued
OIDV: Weapon: Hands / Fist
OIDV: Weapon: Miscellaneous Objects
OIDV: Verbal Threats / Violent Ultimatums
OIDV: Gun was Confiscated
OIDV: Confiscated Gun was Returned
OIDV: Order of Protection Filed
OIDV: Victim Injured, Nonfatal
OIDV: Victim Injured, Fatal

V83
V84
V85
V91
V93
V94
V95
V96
V97
V98
V99
V102
V104
V107
V108
V110
V111
V115
V125
V126
V129
V133
V137
V139
V140
V141
V148
V156
V164
V166
V167
V168
V169
V171
V173
V174

1186
2397
2374
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934

110.509
34.675
114.234
20.205
64.029
3.794
41.888
72.332
82.610
14.144
4.130
13.688
28.227
4.408
18.048
20.021
13.864
15.255
5.234
84.832
8.206
8.108
3.833
40.311
7.553
5.265
12.971
4.004
90.491
8.456
12.370
7.587
15.729
12.261
97.134
4.710

1
1
7
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.005
.000
.000
.000
.000
.051
.000
.000
.000
.000
.042
.000
.000
.036
.000
.000
.000
.000
.022
.000
.004
.004
.050
.000
.006
.022
.000
.045
.000
.004
.000
.000
.000
.000
.000
.030

.305
.120
.219
.072
.128
.031
.103
.136
.145
.060
.032
.059
.085
.033
.068
.071
.059
.062
.036
.147
.046
.045
.031
.101
.044
.037
.057
.032
.152
.046
.056
.044
.063
.056
.157
.035

243
Most Serious Offense Charged
Drugs: Personal Use / Using
Drugs: Selling / Dealing / Trafficking
Drugs: Sexually-motivated Drug Crime
Drugs: Facilitating Drug Trade
Drugs: Shakedowns from Street Dealers
Drugs: Shakedowns from Warrantless Searches
Drugs: Shakedowns from Legitimate Raids/Searches
Drugs: Shakedowns from Car Stops & Drug Couriers
Drugs: Shakedowns from Off-Duty Robberies
Drugs: Theft from Evidence Room
Drugs: Shakedown (Aggregate of V202 thru V207)
Narcotics
Stimulants
Hallucinogens
Cannabis
Age Categorical
Years of Service Categorical
Geographic Region
Geographic Division
Victim Age Categorical

V183
V196
V197
V199
V201
V202
V204
V205
V206
V207
V208
V210
Narcotics
Stimulants
Hallucinogens
Cannabis
AgeCategorical
YearsServCat
GeogRegion
GeogDivision
VictimAgeCat

3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934
3934

394.757
6.964
53.674
6.282
25.713
12.135
9.089
5.931
15.316
10.471
8.363
27.249
14.283
48.853
5.157
12.971
48.283
33.658
16.627
35.632
40.074

61
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
10
3
8
9

.000
.008
.000
.012
.000
.000
.003
.015
.000
.001
.004
.000
.000
.000
.023
.000
.000
.000
.001
.000
.000

.317
.042
.117
.040
.081
.056
.048
.039
.062
.052
.046
.083
.060
.111
.036
.057
.111
.092
.065
.095
.101

244
Table 5. Logistic Regression Model Predicting Conviction (N = 2,195)
95% CI for Exp(B)
B

Rank

-0.097

Full-Time Sworn Personnel Categorical

0.025

Wald
15.141

p
<.001

Exp(B)
0.908

0.865

0.953

-0.061

0.020

8.987

.003

0.941

0.904

0.979

Child Victim

0.379

0.135

7.830

.005

1.461

1.120

1.905

Victim Relationship to Offender

0.074

0.028

7.111

.008

1.076

1.020

1.136

Alcohol-related Crime

0.463

0.162

8.178

.004

1.589

1.157

2.183

Sex-related Crime

0.792

0.128

38.390

<.001

2.208

1.719

2.837

Profit-motivated Crime

0.572

0.198

8.334

.004

1.772

1.202

2.612

Suspended

-0.342

0.112

9.247

.002

0.710

0.570

0.886

DUI in a Personally-owned Vehicle

1.058

0.374

7.990

.005

2.880

1.383

5.998

OIDV: Weapon: Verbal Threat / Violent Ultimatum

0.566

0.255

4.925

.026

1.760

1.068

2.901

OIDV: Victim Injured, Nonfatal

-0.491

0.184

7.152

.007

0.612

0.427

0.877

OIDV: Victim Injured, Fatal

2.068

0.629

10.794

.001

7.907

2.303

27.147

Most Serious Offense Charged

0.006

0.003

5.727

.017

1.006

1.001

1.011

Drug Shakedown

2.641

1.027

6.610

.010

14.024

1.873

104.991

Age Categorical

0.033

0.013

6.831

.009

1.034

1.008

1.060

Years of Service Categorical

0.034

0.009

14.985

<.001

1.035

1.017

1.053

- 2 Log Likelihood

2457.484

Model Chi-Square

299.873

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.128
.179

95% CI for AUC

.436

.718

UL
.697

.740

245
Table 6. Chi-Square Predicting Job Loss
Variable Label
Age
Years of Service
Gender
Duty Status
Rank
Type of Agency
Fulltime Sworn Personnel
State
Parttime Sworn Personnel
Urban to Rural Continuum
Aggravated Assault
Simple Assault
Bribery
Burglary
Drug/Narcotic violation
Drug Equipment violation
Embezzlement
Extortion
False Pretenses/Swindle
Wire Fraud
Kidnapping/Abduction
Theft from Building
Theft / All Other Larceny
Robbery
Forcible Rape
Forcible Sodomy
Sexual Assault with an Object
Forcible Fondling
Statutory Rape
Online Solicitation of a Child
Other Sex Crime
Stolen Property Offenses
Criminal Civil Rights Violation
Disorderly Conduct

Variable
V3
V4
V5
V6
V7
V9
V10
V11
V12
V13
V16
V17
V19
V20
V23
V24
V25
V26
V27
V31
V39
V43
V47
V52
V53
V54
V55
V56
V58
V60
V61
V62
V65
V66

N
5863
4780
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

χ2
77.984
69.760
11.162
207.332
50.971
40.599
112.921
220.807
65.610
105.161
14.486
126.794
46.927
13.198
78.436
3.841
14.052
22.444
7.341
16.759
21.085
13.043
36.182
24.813
51.500
13.908
6.851
111.539
25.744
6.775
60.635
18.218
40.001
35.365

df
56
43
1
1
9
7
10
50
8
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.028
.006
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.050
.000
.000
.007
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000

V
.115
.121
.041
.176
.087
.078
.130
.181
.099
.125
.046
.137
.084
.044
.108
.024
.046
.058
.033
.050
.057
.044
.073
.061
.088
.045
.032
.129
.062
.032
.095
.052
.077
.073

246
Driving Under the Influence
Drunkenness
Evidence: Destroying/Tampering
False Report / False Statement
Family Offenses, Nonviolent
Hit & Run
Official Misconduct
Gender of Victim
Victim is a Police Officer
Age of Victim
Child Victim
Victim Relationship to the Offender
Organizational vs. Against Citizenry
Internal vs. Organizational
Official Capacity
Drug-related
Alcohol-related
Sex-related
Violence-related
Profit-motivated
Police Sexual Violence
Driving While Female Encounter
Officer was Reassigned
Officer was Demoted
Officer was Suspended
Officer's Chief is Under Scrutiny
DUI-related Traffic Accident
Injuries in DUI-related Traffic Accident
DUI in a Take-home Police Vehicle
DUI in a Police Vehicle Out of Jurisdiction
DUI in a Personally-Owned Vehicle
DUI: Refused Field Sobriety Tests
DUI: Refused BAC Test
Off-Duty: Ordinance re On-Duty 24 Hours
Off-Duty: Showed Police Weapon
Off-Duty: Intervened in Existing Dispute

V67
V68
V69
V70
V71
V72
V75
V81
V82
V83
V84
V85
V90
V91
V92
V93
V94
V95
V96
V97
V98
V99
V102
V103
V104
V106
V110
V111
V113
V114
V115
V116
V117
V118
V121
V125

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

223.713
6.544
6.992
4.776
7.468
5.191
41.620
13.138
5.541
111.533
41.756
173.685
84.564
74.138
213.535
91.325
181.978
246.197
5.439
162.689
120.585
63.373
111.394
11.856
178.976
4.045
68.821
15.669
4.246
4.277
127.325
21.562
27.880
14.671
17.053
4.690

1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.000
.011
.008
.029
.006
.023
.000
.000
.000
.008
.000
.000
.000
.000
.000
.000
.000
.000
.020
.000
.000
.000
.000
.000
.000
.044
.000
.000
.039
.039
.000
.000
.000
.000
.000
.000

.182
.031
.032
.027
.033
.028
.079
.060
.037
.247
.102
.210
.112
.105
.178
.117
.165
.191
.028
.156
.134
.097
.129
.042
.163
.025
.101
.048
.025
.025
.138
.057
.064
.047
.050
.026

247
Family Violence
DUI: Officer Resisted Arrest
DUI: In Possession of Firearm while DUI
Heroin
Hydrocodone
Oxycodone
Other Narcotics
Benzodiazepines
Cocaine
Crack
Amphetamine/Methamphetamine
Marijuana
OIDV: Weapon: Gun - Personally Owned
OIDV: Weapon: Hands/Fist
OIDV: Weapon: Miscellaneous Objects
OIDV: Gun was Confiscated
OIDV: Confiscated Gun was Later Returned
OIDV: Order of Protection Files
OIDV: Victim Injury: Nonfatal
42 USC 1981 Civil Defendant at Some Point
42 USC 1983 Civil Defendant at Some Point
42 USC 1985 Civil Defendant at Some Point
42 USC 1997 Civil Defendant at Some Point
28 USC 1441 Civil Action Removed
Civil Rights Defendant - Aggregate
18 USC 242 Criminal Deprivation of Rights
Most Serious Offense Charged
Drugs: Using / Personal use
Drugs: Selling / Dealing / Trafficking
Drugs: Facilitating Drug Trade
Drugs: Shakedowns from Street Dealers
Drugs: Shakedowns from Radio Runs
Drugs: Shakedowns from Legit Raids/Search
Drugs: Shakedowns from Car Stops
Drugs: Shakedowns from Off-Duty Robbery
Drugs: Theft from Evidence Room

V126
V127
V128
V129
V131
V133
V135
V137
V139
V140
V141
V148
V157
V164
V166
V168
V169
V171
V173
V175
V176
V177
V178
V179
V180
V181
V183
V196
V197
V201
V202
V203
V205
V206
V207
V208

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

121.166
6.174
7.087
21.896
14.659
20.173
9.726
7.020
24.365
6.594
9.954
27.796
4.191
60.566
13.953
9.579
5.859
5.811
38.573
4.963
18.643
7.043
5.469
17.956
19.365
63.724
752.849
15.137
60.761
49.058
11.463
4.809
6.345
19.536
11.829
28.799

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.000
.013
.008
.000
.000
.000
.002
.008
.000
.010
.002
.000
.041
.000
.000
.002
.015
.016
.000
.026
.000
.008
.019
.000
.000
.000
.000
.000
.000
.000
.001
.028
.012
.000
.001
.001

.134
.030
.032
.057
.047
.055
.038
.032
.060
.031
.038
.064
.025
.095
.046
.038
.030
.029
.076
.027
.053
.032
.029
.052
.054
.097
.335
.047
.095
.085
.041
.027
.031
.054
.042
.065

248
Drugs: Shakedowns - Aggregate
Narcotics
Depressants
Stimulants
Cannabis
DUI in a Police Vehicle
Age Categorical
Years of Service Categorical
Metro County versus Nonmetro County
Geographic Region
Geographic Division
Rank Function
Victim Age Categorical
Victim Age Difference

V210
Narcotics
Depressants
Stimulants
Cannabis
DUIPoliceVe
AgeCat
YearsServCat
CountyDichot
GeogRegion
GeogDivision
RankFunction
VictimAgeCat
VicAgeDiff

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

30.844
45.919
5.367
39.666
27.796
4.610
162.221
110.430
52.989
39.547
52.482
26.425
53.406
162.449

1
1
1
1
1
1
10
10
1
3
8
2
9
103

.001
.000
.021
.000
.000
.032
.000
.000
.000
.000
.000
.000
.000
.000

.068
.083
.028
.077
.064
.026
.155
.128
.089
.077
.088
.063
.089
.155

249
Table 7. Logistic Regression Model Predicting Job Loss (N = 1,288)
95% CI for Exp(B)
B

Wald

Exp(B)

Urban to Rural County Continuum (V13)

0.110

0.053

4.350

.037

1.116

1.007

1.237

Internal versus Organizational Crime (V91)

0.155

0.022

49.935

<.001

1.168

1.119

1.219

Alcohol-related Crime (V94)

0.431

0.173

6.204

.013

1.538

1.096

2.159

Sex-related Crime (V95)

0.857

0.150

32.577

<.001

2.356

1.755

3.162

Police Sexual Violence (V98)

0.548

0.213

6.621

.010

1.730

1.139

2.626

Officer was Reassigned (V102)

-1.062

0.327

10.519

.001

0.346

0.182

0.657

Officer was Suspended (V104)

-0.955

0.153

38.988

<.001

0.385

0.285

0.519

Family Violence (V126)

-0.670

0.158

17.993

<.001

0.512

0.376

0.697

Marijuana (V148)

-3.128

0.778

16.173

<.001

0.044

0.010

0.201

42 USC §1985 Civil Defendant at Some Point (V177)

1.251

0.485

6.670

.010

3.495

1.352

9.036

Geographic Division

0.053

0.026

4.286

.038

1.054

1.003

1.109

- 2 Log Likelihood

1502.588

Model Chi-Square

241.666

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.171
.231

95% CI for AUC

.276

.638

UL
.625

.652

250
Table 8. Chi-Square Predicting Sex-Related Police Crime Arrests
Variable Label
Years of Service
Gender
Duty Status
Rank
Arson
Aggravated Assault
Simple Assault
Bribery
Burglary
Counterfeiting
Destruction of Property / Vandalism
Drug / Narcotic Violation
Embezzlement
Extortion / Blackmail
False Pretenses / Swindle
Impersonation
Murder & Nonnegligent Manslaughter
Kidnapping / Abduction
Shoplifting
Theft from Building
Theft from Motor Vehicle
Theft / All Other Larceny
Robbery
Stolen Property Offenses
Weapons Law violations
Criminal Civil Rights violations
Disorderly Conduct
Drunkenness
Evidence: Destroying / Tampering
False Report / False Statement
Hit & Run
Obstruction of Justice
Official Misconduct
Peeping Tom

Variable
V4
V5
V6
V7
V15
V16
V17
V19
V20
V21
V22
V23
V25
V26
V27
V29
V36
V39
V42
V43
V46
V47
V52
V62
V63
V65
V66
V68
V69
V70
V72
V74
V75
V76

N
4780
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

χ2
79.685
87.851
16.704
25.566
8.698
71.524
150.617
44.045
7.159
16.887
27.393
115.283
33.883
4.816
68.565
6.878
31.126
142.166
8.980
33.304
6.446
71.221
27.327
24.250
67.587
4.322
17.537
10.320
3.975
68.564
32.100
11.470
56.537
7.163

df
43
1
1
9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.001
.000
.000
.002
.003
.000
.000
.000
.007
.000
.000
.000
.000
.028
.000
.009
.000
.000
.003
.000
.011
.000
.000
.000
.000
.038
.000
.001
.046
.000
.000
.001
.000
.007

V
.129
.114
.050
.062
.036
.103
.150
.081
.033
.050
.064
.131
.071
.027
.101
.032
.068
.145
.037
.070
.031
.103
.064
.060
.100
.025
.051
.039
.024
.101
.069
.041
.092
.033

251
Restraining Order violation
Trespass of Real Property
All Other Offenses
Gender of Victim
Victim is a Police Officer
Age of Victim
Victim is a Child
Relationship of Victim to Offender
Organizational vs. Against Citizenry
Internal vs. Organizational
Official Capacity
Drug-related
Alcohol-related
Violence-related
Profit-motivated
Driving While Female Encounter
Officer was Reassigned
Discussion of Agency Scandal or Cover up
Citizen Complaint as Method of Detection
Off-Duty: 24-Hour Ordinance
Off-Duty: Identified Self as Police Officer
Off-Duty: Showed a Police Weapon
Off-Duty: Flashed Badge
Off-Duty: Conducted a Search
Off-Duty: Made an Arrest
Family Violence
Heroin
Hydrocodone
Oxycodone
Other Narcotics
Benzodiazepines
Cocaine
Crack
Amphetamine / Methamphetamine
Marijuana
Testosterone

V77
V78
V80
V81
V82
V83
V84
V85
V90
V91
V92
V93
V94
V96
V97
V99
V102
V107
V108
V118
V119
V121
V122
V123
V124
V126
V129
V131
V133
V135
V137
V139
V140
V141
V148
V151

6724
6724
6724
3668
3967
1848
3990
3934
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

4.596
6.388
48.023
651.711
58.859
592.004
684.979
909.238
182.373
84.426
8.465
137.383
260.078
357.899
492.036
587.698
5.261
28.459
520.439
3.865
12.151
30.300
4.592
5.322
5.884
57.839
8.841
12.488
25.191
8.135
5.322
56.932
11.586
12.453
19.006
4.760

1
1
1
1
1
79
1
7
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.032
.011
.000
.000
.000
.000
.000
.000
.000
.000
.004
.000
.000
.000
.000
.000
.022
.000
.000
.049
.000
.000
.032
.021
.015
.000
.003
.000
.000
.000
.021
.000
.001
.000
.000
.029

.026
.031
.085
.422
.122
.566
.414
.481
.165
.112
.035
.143
.197
.231
.271
.296
.028
.065
.278
.024
.043
.067
.026
.028
.030
.093
.036
.043
.061
.035
.028
.092
.042
.043
.053
.027

252
Other Anabolic Steroid
OIDV: Weapon: Dept-issued Gun
OIDV: Weapon: Personal Gun
OIDV: Weapon: Hands/Fist
OIDV: Weapon: Other Body parts
OIDV: Weapon: Miscellaneous Objects
OIDV: Verbal Threats & Violent Ultimatums
OIDV: Gun was Confiscated
OIDV: Article Mentions DV gun prohibition
OIDV: Order of Protection Filed
OIDV: Officer Violated Order of Protection
OIDV: Victim Injured, Nonfatal
OIDV: Victim Injured, Fatal
42 USC 1981 Civil Defendant at Some Point
42 USC 1983 Civil Defendant at Some Point
42 USC 1985 Civil Defendant at Some Point
28 USC 1441 Civil Rights Case Removed
Federal Civil Rights Defendant - Aggregate
18 USC 242 Criminal Defendant at Some Pt
Most Serious Offense Charged
Drugs: Personal Use
Drugs: Selling / Dealing / Trafficking
Drugs: Forge Prescription
Drugs: Sexually-motivated Drug Crime
Drugs: Planting Evidence
Drugs: Facilitating Drug Trade
Drugs: Shakedowns from Street Dealers
Drugs: Shakedowns from Radio Runs
Drugs: Shakedowns from Warrantless Searches
Drugs: Shakedowns from Legit Searches
Drugs: Shakedowns from Car Stops
Drugs: Shakedowns from Off-Duty Robberies
Drugs: Theft from Evidence Room
Drugs: Falsification
Drugs: Shakedowns - Aggregate
Narcotics

V152
V156
V157
V164
V165
V166
V167
V168
V170
V171
V172
V173
V174
V175
V176
V177
V179
V180
V181
V183
V196
V197
V198
V199
V200
V201
V202
V203
V204
V205
V206
V207
V208
V209
V210
Narcotics

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

5.768
13.016
13.098
66.856
54.637
13.858
11.503
6.235
7.923
11.052
7.700
18.431
8.698
9.284
6.376
10.874
10.452
6.649
4.816
5322.861
57.821
74.521
9.262
80.884
9.262
49.301
21.184
4.479
19.472
9.262
16.340
9.827
16.909
15.340
49.007
44.270

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
63
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.016
.000
.000
.000
.000
.000
.001
.013
.005
.001
.006
.000
.003
.002
.012
.001
.001
.010
.028
.000
.000
.000
.002
.000
.002
.000
.000
.034
.000
.002
.000
.002
.000
.000
.000
.000

.029
.044
.044
.100
.090
.045
.041
.030
.034
.041
.034
.052
.036
.037
.031
.040
.039
.031
.027
.890
.093
.105
.037
.110
.037
.086
.056
.026
.054
.037
.049
.038
.050
.049
.085
.081

253
Depressants
Stimulants
Hallucinogens
Cannabis
Anabolic Steroids
Age Categorical
Years of Service Categorical
Geographic Region
Geographic Division
Victim Age Categorical
Victim Age Difference

Depressants
Stimulants
Hallucinogens
Cannabis
AnabolicSter
AgeCat
YearsServCat
GeogRegion
GeogDivision
VictimAgeCat
VictimAgeDif

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

8.026
74.783
6.446
19.006
9.929
105.671
109.107
81.554
106.705
1494.957
1406.902

1
1
1
1
1
10
10
3
8
9
103

.005
.000
.011
.000
.002
.000
.000
.000
.000
.000
.000

.035
.105
.031
.053
.038
.125
.127
.110
.126
.472
.457

254
Table 9. Logistic Regression Model Predicting Sex-related Cases (N = 1,809)
95% CI for Exp(B)
B
Duty Status (V6)
Gender of Victim (V81)

Wald

Exp(B)

0.451

0.191

5.580

.018

1.570

1.080

2.282

-3.476

0.189

338.398

<.001

0.031

0.021

0.045

Child Victim (V84)

1.895

0.199

90.357

<.001

6.654

4.502

9.835

Victim Relationship to Offender (V85)

0.342

0.076

20.302

<.001

1.407

1.213

1.633

Alcohol-related Crime (V94)

-1.403

0.251

31.300

<.001

0.246

0.150

0.402

Profit-motivated Crime (V97)

-2.175

0.624

12.156

<.001

0.114

0.033

0.386

Agency Scandal / Cover up (V107)

-0.851

0.365

5.423

.020

0.427

0.209

0.874

1.981

0.224

78.517

<.001

7.247

4.676

11.230

Citizen Complaint as Method of Crime Detection (V108)
Off-Duty: Identified Self as an Officer (V119)

-1.114

0.412

7.308

.007

0.328

0.146

0.736

Family Violence (V126)

-1.320

0.343

14.793

<.001

0.267

0.136

0.524

OIDV: Weapon: Hands/Fist (V164)

-1.470

0.394

13.955

<.001

0.230

0.106

0.497

OIDV: Weapon: Miscellaneous Objects (V166)

-3.012

0.879

11.737

.001

0.049

0.009

0.276

42 USC §1983 Civil Defendant at Some Point (V176)

-0.724

0.184

15.493

<.001

0.485

0.338

0.695

0.055

0.006

72.946

<.001

1.056

1.043

1.069

Victim Age Difference
- 2 Log Likelihood

1051.788

Model Chi-Square

1433.190

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.547
.733

95% CI for AUC

.878

.939

UL
.931

.947

255
Table 10. Chi-Square Predicting Alcohol-related Police Crime Arrests
Variable Label
Age
Duty Status
Type of Agency
FT Sworn Categorical
State
Arresting Agency
Arson
Simple Assault
Intimidation
Bribery
Burglary
Counterfeiting/Forgery
Property Damage / Vandalism
Drug / Narcotic Violation
Embezzlement
Extortion / Blackmail
False Pretenses / Swindle
Credit Card / ATM Fraud
Impersonation
Wire Fraud
Kidnapping / Abduction
Shoplifting
Theft from Building
Theft from Motor Vehicle
Theft / All Other Larceny
Pornography
Prostitution
Assisting or Promoting Prostitution
Robbery
Forcible Rape
Forcible Sodomy
Forcible Fondling
Statutory Rape
Indecent Exposure

Variable
V3
V6
V9
V10
V11
V14
V15
V17
V18
V19
V20
V21
V22
V23
V25
V26
V27
V28
V29
V31
V39
V42
V43
V45
V47
V49
V50
V51
V52
V53
V54
V56
V58
V59

N
5863
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

χ2
89.049
630.074
23.819
34.433
182.106
25.529
5.855
10.496
50.668
39.829
22.387
20.501
9.494
111.902
34.343
34.669
66.634
5.544
8.144
8.197
22.804
8.463
28.843
6.074
73.232
19.694
10.571
4.312
27.760
35.279
25.952
70.116
23.282
4.173

df
56
1
7
10
50
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.003
.000
.001
.000
.000
.000
.016
.001
.000
.000
.000
.000
.002
.000
.000
.000
.000
.000
.004
.004
.000
.004
.000
.014
.000
.000
.001
.038
.000
.000
.000
.000
.000
.041

V
.123
.306
.060
.072
.165
.062
.030
.040
.087
.077
.058
.055
.038
.129
.071
.072
.100
.029
.035
.035
.058
.035
.065
.030
.104
.054
.040
.025
.064
.072
.062
.102
.059
.025

256
Online Solicitation of a Child
Other Sex Crime
Stolen Property Offenses
Criminal Civil Rights violation
Disorderly Conduct
Evidence: Destroying / Tampering
False Report / False Statement
Hit & Run
Obstruction of Justice
Official Misconduct
Restraining Order violation
Gender of the Victim
Victim is a Police Officer
Age of Victim
Victim is a Child
Victim Relationship to the Offender
Organizational vs. Against Citizenry
Internal vs. Organizational
Official Capacity
Drug-related
Sex-related
Violence-related
Profit-motivated
Police Sexual Violence
Driving While Female Encounter
Officer was Reassigned to Another Position
Officer was Demoted in Rank
Officer was Suspended
Discussion of Agency Scandal or Cover up
Citizen Complaint as Method of Detection
Off-Duty: 24-Hour Ordinance
Off-Duty: Identified Self as Police Officer
Off-Duty: In Police Uniform
Off-Duty: Flashed Badge
Family Violence
Heroin

V60
V61
V62
V65
V66
V69
V70
V72
V74
V75
V77
V81
V82
V83
V84
V85
V90
V91
V92
V93
V95
V96
V97
V98
V99
V102
V103
V104
V107
V108
V118
V119
V120
V122
V126
V129

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
3668
3967
1848
3990
3934
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

11.504
25.129
27.531
41.862
33.555
15.061
71.120
240.993
27.167
197.410
7.940
20.668
25.498
172.884
46.606
74.902
656.028
957.118
619.269
124.399
259.429
199.445
498.804
100.204
24.961
52.795
10.025
23.848
21.420
109.416
64.099
69.862
7.750
13.457
50.784
12.770

1
1
1
1
1
1
1
1
1
1
1
1
1
79
1
7
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.005
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.002
.000
.000
.000
.000
.000
.005
.000
.000
.000

.041
.061
.064
.079
.071
.047
.103
.189
.064
.171
.034
.075
.080
.306
.108
.138
.312
.377
.303
.136
.196
.172
.272
.122
.061
.089
.039
.060
.056
.128
.098
.102
.034
.045
.087
.044

257
Hydrocodone
Oxycodone
Cocaine
Crack
Amphetamine / Methamphetamine
Marijuana
Testosterone
Other Anabolic Steroids
OIDV: Weapon: Hands/Fist
OIDV: Weapon: Miscellaneous Object
OIDV: Verbal Threats or Violent Ultimatum
OIDV: Gun was Confiscated
OIDV: Order of Protection Filed
OIDV: Officer Violated Order of Protection
OIDV: Victim Injured: Nonfatal
OIDV: Victim Injured: Fatal
42 U.S.C. 1983 Defendant at Some Point
42 U.S.C. 1985 Defendant at Some Point
42 U.S.C. 1997 Plaintiff is a Prisoner
28 U.S.C. 1441 Civil Rights Case Removed
Federal Civil Rights Defendant - Aggregate
18 U.S.C. 242 Criminal Defendant
Most Serious Offense Charged
Drugs: Using / Personal Use
Drugs: Selling / Dealing / Trafficking
Drugs: Forged Prescription
Drugs: Planting Evidence
Drugs: Facilitating Drug Trade
Drugs: Shakedowns from Street Dealers
Drugs: Shakedowns from Radio Runs
Drugs: Shakedowns from Warrantless Search
Drugs: Shakedowns from Legit Search/Raids
Drugs: Shakedowns from Car Stops / Couriers
Drugs: Shakedowns from Off-Duty Robberies
Drugs: Theft from Evidence Room
Drugs: Falsification

V131
V133
V139
V140
V141
V148
V151
V152
V164
V166
V167
V168
V171
V172
V173
V174
V176
V177
V178
V179
V180
V181
V183
V196
V197
V198
V200
V201
V202
V203
V204
V205
V206
V207
V208
V209

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

18.415
21.331
46.465
13.009
11.678
21.913
4.502
5.364
17.435
5.920
6.447
4.227
8.370
9.560
23.276
5.883
42.196
6.191
12.140
9.198
38.788
26.456
3906.421
25.808
80.031
8.760
8.760
46.626
20.035
4.236
18.415
8.760
15.454
9.294
13.542
15.454

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
63
1
1
1
1
1
1
1
1
1
1
1
1
1

.000
.000
.000
.000
.000
.000
.034
.021
.000
.015
.011
.040
.004
.002
.000
.015
.000
.013
.000
.002
.000
.000
.000
.000
.000
.003
.003
.000
.000
.040
.000
.003
.000
.002
.000
.000

.052
.056
.083
.044
.042
.057
.026
.028
.051
.030
.031
.025
.035
.038
.059
.030
.079
.030
.042
.037
.076
.063
.762
.062
.109
.036
.036
.083
.055
.025
.052
.036
.048
.037
.045
.048

258
Drugs: Shakedowns - Aggregate
Narcotics
Stimulants
Cannabis
Anabolic Steroids
Age Categorical
Geographic Region
Geographic Division
Age of Victim Categorical
Victim Age Difference

V210
Narcotics
Stimulants
Cannabis
AnabolicSter
AgeCat
GeogRegion
GeogDivision
VicAgeCat
VicAgeDiff

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

46.348
46.079
65.383
21.913
9.292
43.280
31.558
40.663
102.029
144.561

1
1
1
1
1
10
3
8
9
103

.000
.000
.000
.000
.002
.000
.000
.000
.000
.004

.083
.083
.099
.057
.037
.080
.069
.078
.123
.147

259
Table 11. Logistic Regression Model Predicting Alcohol-related Arrest Cases (N = 3,248)
95% CI for Exp(B)
B

Wald

Exp(B)

Duty Status (V6)

-2.511

0.183

189.197

<.001

0.081

0.057

0.116

Child Victim (V84)

-1.323

0.219

36.481

<.001

0.266

0.173

0.409

Victim Relationship to Offender (V85)

0.161

0.025

42.205

<.001

1.175

1.119

1.234

Organizational Crime versus Against Citizenry (V90)

-1.339

0.245

29.768

<.001

0.262

0.162

0.424

Sex-related Crime (V95)

-1.359

0.210

41.766

<.001

0.257

0.170

0.388

Violence-related Crime (V96)

-0.352

0.163

4.656

.031

0.703

0.511

0.968

Police Sexual Violence (V98)

0.974

0.277

12.401

<.001

2.649

1.540

4.557

Suspended (V104)

0.458

0.131

12.246

<.001

1.581

1.223

2.044

Agency Scandal / Cover Up (V107)

0.780

0.254

9.420

.002

2.181

1.326

3.589

Citizen Complaint as Method of Crime Detection (V108)

-0.339

0.140

5.882

.015

0.713

0.542

0.937

Off-Duty: In Uniform (V120)

-2.421

1.040

5.421

.020

0.089

0.012

0.682

1.792

0.512

12.247

<.001

6.000

2.200

16.369

OIDV: Victim Injured, Fatal (V174)

-2.073

1.032

4.037

.045

0.126

0.017

0.951

42 USC §1997 Civil Defendant at Some Point (V178)

-0.626

0.307

4.162

.041

0.535

0.293

0.976

18 USC §242 Criminal Defendant at Some Point (V181)

-1.253

0.574

4.768

.029

0.286

0.093

0.880

Most Serious Offense Charged (V183)

0.012

0.003

16.185

<.001

1.012

1.006

1.018

Victim Age Difference

0.018

0.005

14.298

<.001

1.018

1.009

1.028

Marijuana (V148)

- 2 Log Likelihood

2102.385

Model Chi-Square

532.900

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.151
.272

95% CI for AUC

.604

.802

UL
.781

.823

260
Table 12. Chi-Square Predicting Drug-related Police Crime Arrests
Variable Label
Age
Duty Status
Rank
Fulltime Sworn Personnel Categorical
State
Arresting Agency
Aggravated Assault
Simple Assault
Intimidation
Burglary
Counterfeiting/Forgery
Extortion
Murder & Nonnegligent Manslaughter
Negligent Manslaughter
Shoplifting
Theft from Building
Pornography
Robbery
Forcible Rape
Forcible Sodomy
Forcible Fondling
Statutory Rape
Indecent Exposure
Online Solicitation of a Child
Other Sex Crime
Weapons Law violation
Criminal Deprivation of Civil Rights
Disorderly Conduct
Driving Under the Influence
Evidence: Destroying / Tampering
False Report / False Statement
Hit & Run
Liquor Law violation
Obstruction of Justice

Variable
V3
V6
V7
V10
V11
V14
V16
V17
V18
V20
V21
V26
V36
V37
V42
V43
V49
V52
V53
V54
V56
V58
V59
V60
V61
V63
V65
V66
V67
V69
V70
V72
V73
V74

N
5863
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

χ2
87.066
116.700
34.999
29.917
100.646
9.770
44.880
130.214
17.812
16.056
5.766
62.225
8.114
8.469
3.964
60.149
4.434
236.476
23.288
10.810
38.806
14.219
8.973
5.492
16.635
62.083
22.141
17.395
43.269
53.036
8.320
5.154
5.460
31.418

df
56
1
9
10
50
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.005
.000
.000
.001
.000
.002
.000
.000
.000
.000
.016
.000
.004
.004
.046
.000
.035
.000
.000
.001
.000
.000
.003
.019
.000
.000
.000
.000
.000
.000
.004
.023
.019
.000

V
.122
.132
.072
.067
.122
.038
.082
.139
.051
.049
.029
.096
.035
.035
.024
.095
.026
.188
.059
.040
.076
.046
.037
.029
.050
.096
.057
.051
.080
.089
.035
.028
.028
.068

261
Official Misconduct
Restraining Order violation
Gender of Victim
Age of Victim
Child Victim
Victim Relationship to Offender
Organizational vs. Against Citizenry
Internal vs. Organizational
Official Capacity
Alcohol-related
Sex-related
Violence-related
Profit-motivated
Police Sexual Violence
Driving While Female Encounter
Officer was Reassigned
Officer was Suspended
Officer's Supervisor was Disciplined
Officer's Chief is Under Scrutiny
Discussion of Agency Scandal / Cover Up
Citizen Complaint as Method of Detection
DUI-related Traffic Accident
Injuries in DUI-related Traffic Accident
DUI On-Duty in Police Vehicle
DUI in Personally-owned Vehicle
DUI: Refused Field Sobriety Tests
DUI: Refused BAC Tests
Off-Duty: Identified Self as Police Officer
Off-Duty: Showed Police Weapon
Off-Duty: Conducted a Search
Family Violence
OIDV: Weapon: Dept-issued Gun
OIDV: Weapon: Personal Gun
OIDV: Weapon: Hands / Fist
OIDV: Weapon: Miscellaneous Objects
OIDV: Verbal Threats / Violent Ultimatums

V75
V77
V81
V83
V84
V85
V90
V91
V92
V94
V95
V96
V97
V98
V99
V102
V104
V105
V106
V107
V108
V110
V111
V112
V115
V116
V117
V119
V121
V123
V126
V156
V157
V164
V166
V167

6724
6724
3668
1848
3990
3994
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

6.881
4.836
8.658
140.050
15.568
35.490
5.101
96.522
102.216
124.351
138.637
331.322
580.101
51.585
14.751
11.040
16.627
19.700
7.017
97.812
262.034
10.393
10.301
18.624
34.411
6.072
16.369
10.626
12.705
4.589
106.902
4.759
6.671
56.065
7.093
9.526

1
1
1
79
1
7
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.009
.028
.003
.000
.000
.000
.024
.000
.000
.000
.000
.000
.000
.000
.000
.001
.000
.000
.008
.000
.000
.001
.001
.000
.000
.014
.000
.001
.000
.032
.000
.029
.010
.000
.008
.002

.032
.027
.049
.275
.062
.095
.028
.120
.123
.136
.144
.222
.294
.088
.047
.041
.050
.054
.032
.121
.197
.039
.039
.053
.072
.030
.049
.040
.043
.026
.126
.027
.031
.091
.032
.038

262
OIDV: Gun was Confiscated
OIDV: Article Mentions Gun Prohibition
OIDV: Order of Protection Files
OIDV: Officer Violated Order of Protection
OIDV: Victim Injured, Nonfatal
OIDV: Victim Injured, Fatal
42 U.S.C. 1983 Civil Defendant at Some Point
42 U.S.C. 1985 Civil Defendant at Some Point
42 U.S.C. 1997 Civil Defendant at Some Point
28 U.S.C. 1441 Civil Rights Case Removed
Federal Civil Rights Defendant - Aggregate
18 U.S.C. 242 Criminal Defendant
Most Serious Offense Charged
Age Categorical
Geographic Division
Rank by Function
Victim Age Categorical
Victim Age Difference

V168
V170
V171
V172
V173
V174
V176
V177
V178
V179
V180
V181
V183
AgeCat
GeogDiv
RankFunc
VicAgeCat
VicAgeD

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

10.836
4.395
15.025
4.462
45.203
3.840
25.553
53.326
38.431
6.848
25.441
10.281
3127.330
52.409
22.297
8.329
209.599
217.928

1
1
1
1
1
1
1
1
1
1
1
1
63
10
8
2
9
103

.001
.036
.000
.035
.000
.050
.000
.000
.000
.009
.000
.001
.000
.000
.004
.016
.000
.000

.040
.026
.047
.026
.082
.240
.062
.089
.076
.032
.062
.039
.682
.088
.058
.035
.177
.180

263
Table 13. Logistic Regression Model Predicting Drug-related Arrest Cases (N = 1,697)
95% CI for Exp(B)
B
State (V11)

-0.048

Wald

Exp(B)

0.015

10.621

.001

0.953

0.926

0.981

Pornography (V49)

2.344

0.538

18.979

<.001

10.418

3.630

29.902

Forcible Rape (V53)

1.107

0.522

4.496

.034

3.026

1.087

8.422

False Report / False Statement (V70)

2.818

0.592

22.665

<.001

16.742

5.248

53.414

Obstruction of Justice (V74)

2.590

0.558

21.503

<.001

13.324

4.459

39.807

Alcohol-related Crime (V94)

1.532

0.507

9.125

.003

4.626

1.712

12.496

Sex-related Crime (V95)

1.396

0.563

6.149

.013

4.040

1.340

12.180

Profit-motivated Crime (V97)

3.629

0.691

27.598

<.001

37.691

9.731

145.984

Officer was Reassigned (V102)

1.374

0.674

4.154

.042

3.951

1.054

14.812

Injuries in DUI-related Traffic Accident (V111)

1.561

0.746

4.375

.036

4.764

1.103

20.569

- 2 Log Likelihood

254.464

Model Chi-Square

94.190

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.054
.291

95% CI for AUC

.290

.645

UL
.622

.668

264
Table 14. Chi-Square Predicting Violence-related Police Crime
Arrests
Variable Label
Age
Years of Service
Gender
Duty Status
Rank
Type of Agency
Full-Time Sworn Personnel - Categorical
State
Urban / Rural Continuum
Arresting Agency
Intimidation
Bribery
Counterfeiting / Forgery
Destruction of Property / Vandalism
Drug / Narcotic violation
Drug Equipment violation
Embezzlement
Extortion / Blackmail
False Pretenses / Swindle
Credit Card / ATM Fraud
Impersonation
Wire Fraud
Gambling: Betting / Wagering
Gambling: Operating / Promoting
Murder & Nonnegligent Manslaughter
Negligent Manslaughter
Kidnapping / Abduction
Shoplifting
Theft from Building
Theft from Motor Vehicle
Theft: All Other Larceny
Pornography
Prostitution

Variable
V3
V4
V5
V6
V7
V9
V10
V11
V13
V14
V18
V19
V21
V22
V23
V24
V25
V26
V27
V28
V29
V31
V32
V33
V36
V37
V39
V42
V43
V45
V47
V49
V50

N
5863
4780
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

χ2
111.463
81.581
26.172
107.403
82.183
22.106
33.913
133.605
31.404
27.106
223.928
8.133
72.379
21.213
296.215
18.245
116.048
47.265
233.199
16.863
31.433
26.671
3.922
11.781
57.019
13.992
197.344
31.509
103.792
22.617
251.356
30.348
19.132

df
56
43
1
1
9
7
10
50
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.000
.000
.000
.000
.000
.002
.000
.000
.000
.000
.000
.004
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.048
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000

V
.138
.131
.062
.126
.111
.057
.071
.141
.068
.063
.182
.035
.104
.056
.210
.052
.131
.084
.186
.050
.068
.063
.024
.042
.092
.046
.171
.068
.124
.058
.193
.067
.053

265
Assisting or Promoting Prostitution
Stolen Property Offenses
Weapons Law violation
Criminal Deprivation of Civil Rights
Disorderly Conduct
Driving Under the Influence
Evidence: Destroying / Tampering
False Report / False Statement
Hit & Run
Liquor Law violation
Obstruction of Justice
Official Misconduct
Restraining Order Violation
Trespass of Real Property
All Other Offenses
Gender of Victim
Victim is a Police Officer
Age of Victim
Child Victim
Victim Relationship to Offender
Organizational vs. Against Citizenry
Internal vs. Organizational
Official Capacity
Drug-related
Alcohol-related
Sex-related
Profit-motivated
Driving While Female Encounter
Officer was Suspended
Officer's Chief is Under Scrutiny
Discussion of Agency Scandal / Cover Up
Citizen Complaint as Method of Detection
DUI-related Traffic Accident
Injuries in a DUI-related Traffic Accident
DUI On-Duty in a Police Vehicle
DUI in a Take-home Police Vehicle

V51
V62
V63
V65
V66
V67
V69
V70
V72
V73
V74
V75
V77
V78
V80
V81
V82
V83
V84
V85
V90
V91
V92
V93
V94
V95
V97
V99
V104
V106
V107
V108
V110
V111
V112
V113

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
3668
3967
1848
3990
3934
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

6.525
72.860
13.850
30.259
109.947
678.239
43.248
95.618
39.878
22.781
18.982
10.967
11.806
18.916
23.533
4.806
5.895
166.314
21.316
206.633
332.506
316.816
41.853
331.322
199.445
357.899
1280.268
110.448
33.976
4.688
10.187
1327.054
280.360
65.197
39.546
62.541

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
79
1
7
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.011
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.001
.001
.000
.000
.028
.015
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.030
.001
.000
.000
.000
.000
.000

.031
.104
.045
.067
.128
.318
.080
.119
.077
.058
.053
.040
.042
.053
.059
.036
.039
.300
.073
.229
.222
.217
.079
.222
.172
.231
.436
.128
.071
.026
.039
.444
.204
.098
.077
.096

266
DUI in Police Vehicle Out of Jurisdiction
DUI in a Personally-owned Vehicle
DUI: Refused Field Sobriety Tests
DUI: Refused BAC Tests
Off-Duty: 24-Hour Ordinance
Off-Duty: Identified Self as Police Officer
Off-Duty: Showed a Police Weapon
Off-Duty: Flashed Badge
Off-Duty: Conducted a Search
Off-Duty: Made an Arrest
DUI: In Possession of a Firearm
Heroin
Morphine
Hydrocodone
Hydromorphone
Oxycodone
Other Narcotics
Benzodiazepines
Other Depressants
Cocaine
Crack
Amphetamine / Methamphetamine
Other Stimulants
MDMA & Analogs
Phencyclidine & Analogs
Other Hallucinogens
Marijuana
Testosterone
Other Anabolic Steroids
42 USC 1981 Civil Defendant at Some Point
42 USC 1983 Civil Defendant at Some Point
28 USC 1441 Civil Rights Case Removed
Federal Civil Rights Defendant - Aggregate
18 USC 242 Criminal Defendant
Most Serious Offense Charged
Drugs: Using / Personal Use

V114
V115
V116
V117
V118
V119
V121
V122
V123
V124
V128
V129
V130
V131
V132
V133
V135
V137
V138
V139
V140
V141
V143
V144
V146
V147
V148
V151
V152
V175
V176
V179
V180
V181
V183
V196

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

26.671
330.073
51.479
123.689
6.828
28.844
199.916
11.715
4.460
17.634
41.105
28.330
4.385
40.060
4.904
49.756
28.542
11.575
12.148
68.269
39.100
41.062
3.922
10.797
7.849
3.922
32.825
16.702
17.858
22.193
103.675
52.819
104.219
26.437
4883.929
160.243

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
63
1

.000
.000
.000
.000
.009
.000
.000
.001
.035
.000
.000
.000
.036
.000
.027
.000
.000
.001
.000
.000
.000
.000
.048
.001
.005
.048
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000

.063
.222
.087
.136
.032
.065
.172
.042
.026
.051
.078
.065
.026
.077
.027
.086
.065
.041
.043
.101
.076
.078
.024
.040
.034
.024
.070
.050
.052
.057
.124
.089
.124
.063
.852
.154

267
Drugs: Selling / Dealing / Trafficking
Drugs: Forged Prescription
Drugs: Sexually-motivated Drug Crime
Drugs: Planting Evidence
Drugs: Facilitating Drug Trade
Drugs: Shakedown from Radio Runs
Drugs: Shakedown from Warrantless Search
Drugs: Shakedown from Legit Raid/Search
Drugs: Shakedown from Car Stops
Drugs: Theft from Evidence Room
Drugs: falsification
Narcotics
Depressants
Stimulants
Hallucinogens
Cannabis
Anabolic Steroids
DUI in a Police Vehicle
Age Categorical
Years of Service Categorical
Metro vs. Nonmetro County
Geographic Region
Geographic Division
Rank by Function
Victim Age Categorical
Victim Age Difference

V197
V198
V199
V200
V201
V203
V204
V205
V206
V208
V209
Narcotics
Depress
Stimula
Hallucino
Cannabis
AnabSter
DUIinaP
AgeCat
YrsServCa
CountyDi
GeogReg
GeogDiv
RankFunc
VicAgeCat
VicAgeDif

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

151.410
32.499
7.052
25.027
114.069
11.998
6.895
21.657
7.371
51.606
32.781
135.577
26.700
118.852
22.617
32.825
32.269
108.082
43.530
44.068
13.247
21.266
33.277
47.771
944.302
879.176

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
10
1
3
8
2
9
103

.000
.000
.008
.000
.000
.001
.009
.000
.007
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000

.150
.070
.032
.061
.130
.042
.032
.057
.033
.088
.070
.142
.063
.133
.058
.070
.069
.127
.080
.081
.044
.056
.070
.084
.375
.362

268
Table 15. Logistic Regression Model Predicting Violence-related Arrest Cases (N = 1,286)
95% CI for Exp(B)
B
Age (V3)

0.057

0.016

Wald
13.075

p
<.001

Exp(B)
1.058

1.026

1.092

Years of Service (V4)

-0.040

0.017

5.230

.022

0.961

0.929

0.994

Type of Agency (V9)

0.163

0.080

4.143

.042

1.177

1.006

1.376

Child Victim (V84)

-1.277

0.217

34.716

<.001

0.279

0.182

0.427

Victim Relationship to Offender (V85)

-0.325

0.108

9.107

.003

0.723

0.585

0.892

0.064

0.026

6.239

.012

1.066

1.014

1.121

-3.998

0.477

70.258

<.001

0.018

0.007

0.047

Suspended (V104)

0.400

0.178

5.038

.025

1.492

1.052

2.115

Citizen Complaint as Method of Crime Detection (V108)

0.820

0.194

17.867

<.001

2.271

1.552

3.322

Internal versus Organizational Crime (V91)
Profit-motivated Crime (V97)

DUI-related Traffic Accident (V110)

-2.287

0.303

56.845

<.001

0.102

0.056

0.184

Most Serious Offense Charged (V183)

-0.054

0.006

84.071

<.001

0.948

0.937

0.959

- 2 Log Likelihood

948.709

Model Chi-Square

378.310

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.255
.396

95% CI for AUC

.688

.844

UL
.826

.862

269
Table 16. Chi-Square Predicting Profit-Motivated Police Crime
Arrests
Variable Label
Age
Years of Service
Duty Status
Rank
Full-Time Sworn Categorical
State
Part-Time Sworn Categorical
Urban to Rural Continuum
Arson
Aggravated Assault
Simple Assault
Intimidation
Burglary
Property Destruction / Vandalism
Drug / Narcotic violation
Impersonation
Murder & Nonnegligent Manslaughter
Negligent Manslaughter
Kidnapping / Abduction
Pornography
Prostitution
Forcible Rape
Forcible Sodomy
Sexual Assault with an Object
Forcible Fondling
Incest
Statutory Rape
Indecent Exposure
Online Solicitation of a Child
Other Sex Crime
Weapons Law violation
Criminal Deprivation of Civil Rights
Disorderly Conduct

Variable
V3
V4
V6
V7
V10
V11
V12
V13
V15
V16
V17
V18
V20
V22
V23
V29
V36
V37
V39
V49
V50
V53
V54
V55
V56
V57
V58
V59
V60
V61
V63
V65
V66

N
5863
4780
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

χ2
105.411
85.616
633.147
55.541
28.640
244.278
17.341
32.193
5.758
171.383
348.894
53.258
72.653
22.339
506.835
65.165
27.881
21.292
22.070
41.733
10.476
105.167
49.453
7.778
171.962
4.348
46.582
22.558
19.080
57.460
37.793
38.249
52.056

df
56
43
1
9
10
50
8
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.000
.000
.000
.000
.001
.000
.027
.000
.016
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.001
.000
.000
.005
.000
.037
.000
.000
.000
.000
.000
.000
.000

V
.134
.134
.307
.091
.065
.191
.051
.069
.029
.160
.228
.089
.104
.058
.275
.098
.064
.056
.057
.079
.039
.125
.086
.034
.160
.025
.083
.058
.053
.092
.075
.075
.088

270
Driving Under the Influence
Drunkenness
Evidence: Destroying / Tampering
False Report / False Statement
Family Offenses, Nonviolent
Hit & Run
Liquor Law violation
Obstruction of Justice
Official Misconduct
Restraining Order Violation
Trespass of Real Property
Gender of Victim
Victim is a Police Officer
Age of Victim
Victim is a Child
Victim Relationship to the Offender
Organizational vs. Against Citizenry
Internal vs. Organizational
Official Capacity
Drug-related
Alcohol-related
Sex-related
Violence-related
Police Sexual Violence
Driving While Female Encounter
Officer was Reassigned
Officer was Suspended
Officer's Supervisor was Disciplined
Officer's Chief is Under Scrutiny
Discussion of Agency Scandal / Cover Up
Citizen Complaint as Method of Detection
DUI-related Traffic Accident
Injuries in DUI-related Traffic Accident
DUI On-Duty in Police Vehicle
DUI in Take-Home Police Vehicle
DUI in Police Vehicle Out of Jurisdiction

V67
V68
V69
V70
V71
V72
V73
V74
V75
V77
V78
V81
V82
V83
V84
V85
V90
V91
V92
V93
V94
V95
V96
V98
V99
V102
V104
V105
V106
V107
V108
V110
V111
V112
V113
V114

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
3668
3967
1848
3990
3934
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

357.196
19.080
35.334
64.682
5.688
35.944
11.217
6.803
98.591
12.159
11.698
144.140
4.800
211.386
110.187
235.591
11.310
605.995
540.962
580.101
498.804
492.371
1280.268
182.244
52.721
31.477
41.137
35.912
7.628
183.301
445.610
171.858
74.117
17.188
25.730
9.653

1
1
1
1
1
1
1
1
1
1
1
1
1
79
1
7
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.000
.000
.000
.000
.017
.000
.001
.009
.000
.000
.000
.000
.028
.000
.000
.000
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.006
.000
.000
.000
.000
.000
.000
.002

.230
.053
.072
.098
.029
.073
.041
.032
.121
.043
.042
.198
.035
.338
.166
.245
.041
.300
.284
.294
.272
.271
.436
.165
.089
.068
.078
.073
.034
.165
.257
.160
.105
.051
.062
.038

271
DUI in Personally-owned Vehicle
DUI-Refused BAC Test
Off-Duty: 24-Hour Ordinance
Off-Duty: Identified Self as Police Officer
Off-Duty: Showed Police Weapon
Off-Duty: Conducted a Search
Family Violence
DUI: Officer Resisted Arrest
DUI: In Possession of a Firearm
Heroin
Hydrocodone
Oxycodone
Other Narcotics
Gamma Hydroxybutyric Acid
Cocaine
Crack
Amphetamine / Methamphetamine
MDMA & Analogs
Phencyclidine & Analogs
Marijuana
Testosterone
Other Anabolic Steroids
OIDV: Weapon: Dept-issued Gun
OIDV: Weapon: Personally-owned Gun
OIDV: Weapon: Knife
OIDV: Weapon: Hands / Fist
OIDV: Weapon: Other Body Parts
OIDV: Weapon: Miscellaneous Objects
OIDV: Verbal Threats / Violent Ultimatums
OIDV: Gun was Confiscated
OIDV: Confiscated Gun was Returned
OIDV: Article Mentions Gun Prohibition
OIDV: Order of Protection was Filed
OIDV: Officer Violated Order of Protection
OIDV: Victim Injured, Nonfatal
OIDV: Victim Injured, Fatal

V115
V117
V118
V119
V121
V123
V126
V127
V128
V129
V131
V133
V135
V136
V139
V140
V141
V144
V146
V148
V151
V152
V156
V157
V163
V164
V165
V166
V167
V168
V169
V170
V171
V172
V173
V174

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

206.189
62.246
11.218
32.075
26.293
5.929
331.114
14.042
28.911
82.346
19.915
36.133
12.698
6.454
380.676
76.983
57.944
27.585
18.071
176.476
11.666
38.325
14.648
27.001
4.660
170.143
22.830
20.619
38.805
37.870
5.906
16.243
38.163
11.218
136.696
9.653

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.000
.000
.001
.000
.000
.015
.000
.000
.000
.000
.000
.000
.000
.011
.000
.000
.000
.000
.000
.000
.001
.000
.000
.000
.031
.000
.000
.000
.000
.000
.015
.000
.000
.001
.000
.002

.175
.096
.041
.069
.063
.030
.222
.046
.066
.111
.054
.073
.043
.031
.238
.107
.093
.064
.052
.162
.042
.075
.047
.063
.026
.159
.058
.055
.076
.075
.030
.049
.075
.041
.143
.038

272
42 U.S.C. 1985 Civil Defendant
42 U.S.C. 1997 Civil Defendant
18 U.S.C. 242 Criminal Defendant
Most Serious Offense Charged
Drugs: Personal Use / Using
Drugs: Sexually-motivated Drug Crime
Drugs: Planting Evidence
Drugs: Falsification
Narcotics
Stimulants
Hallucinogens
Cannabis
Anabolic Steroids
DUI in a Police Vehicle
Age Categorical
Years of Service Categorical
Metro or Nonmetro County
Geographic Region
Geographic Division
Rank by Function
Victim Age Categorical
Victim Age Difference

V177
V178
V181
V183
V196
V199
V200
V209
Narcotics
Stimul
Hallucin
Cannab
Anabol
DUIinP
AgeCat
YrsServC
CountyDi
GeogReg
GeogDiv
RankFunc
VicAgeCat
VicAgeDiff

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

14.891
18.490
10.720
4449.343
5.059
5.370
14.244
25.507
112.680
441.677
44.397
176.476
43.764
44.963
31.473
24.044
9.943
27.852
54.990
39.513
637.177
603.574

1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
10
1
3
8
2
9
103

.000
.000
.001
.000
.025
.020
.000
.000
.000
.000
.000
.000
.000
.000
.000
.007
.002
.000
.000
.000
.000
.000

.047
.052
.040
.813
.027
.028
.046
.062
.129
.256
.081
.162
.081
.082
.068
.060
.038
.064
.090
.077
.308
.300

273
Table 17. Logistic Regression Model Predicting Profit-motivated Arrest Cases (N = 1,330)
95% CI for Exp(B)
B
Duty Status (V6)

0.926

Age of Victim (V83)

0.398

Wald
5.417

p
.020

Exp(B)
2.525

1.158

5.510

0.076

0.024

9.953

.002

1.079

1.029

1.131

Sex-related Crime (V95)

-2.340

0.655

12.784

<.001

0.096

0.027

0.347

Violence-related Crime (V96)

-2.808

0.435

41.592

<.001

0.060

0.026

0.142

Citizen Complaint as Method of Crime Detection (V108)

1.777

0.596

8.900

.003

5.911

1.840

18.997

42 USC §1985 Civil Defendant at Some Point (V177)

1.662

0.784

4.496

.034

5.268

1.134

24.477

Victim Age Difference

0.046

0.022

4.557

.033

1.047

1.004

1.092

- 2 Log Likelihood

205.905

Model Chi-Square

110.546

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.080
.377

95% CI for AUC

.740

.870

UL
.801

.939

274
Table 18. Chi-Square Predicting Being Named a Party-Defendant in 42 U.S.C. 1983 Litigation at Some Point
Variable Label
Age
Years of Service
Gender
Duty Status
Rank
Type of Agency
Full-Time Sworn Personnel Categorical
State
Part-Times Sworn Personnel Categorical
Urban to Rural Continuum Code
Arresting Agency
Aggravated Assault
Bribery
Counterfeiting / Forgery
Property Destruction / Vandalism
Extortion / Blackmail
Credit Card / ATM Fraud
Murder & Nonnegligent Manslaughter
Kidnapping / Abduction
Pornography / Obscene Material
Robbery
Forcible Sodomy
Forcible Fondling
Incest
Online Solicitation of a Child
Weapons Law violation
Criminal Civil Rights Deprivation
Driving Under the Influence
Drunkenness
False Report / False Statement
Family Offenses, Nonviolent
Liquor Law violation
Obstruction of Justice
Official Misconduct

Variable
V3
V4
V5
V6
V7
V9
V10
V11
V12
V13
V14
V16
V19
V21
V22
V26
V28
V36
V39
V49
V51
V54
V56
V57
V60
V63
V65
V67
V68
V70
V71
V73
V74
V75

N
5863
4780
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

χ2
198.143
250.387
30.465
203.914
125.643
30.828
60.276
346.944
25.560
75.487
4.414
38.690
9.418
5.135
5.593
7.905
4.256
16.915
41.130
11.702
19.811
20.992
3.894
4.417
3.964
3.964
97.041
55.349
3.964
23.451
5.824
7.133
27.533
50.495

df
56
43
1
1
9
7
10
50
8
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.000
.000
.000
.000
.000
.000
.000
.000
.001
.000
.036
.000
.002
.023
.018
.005
.039
.000
.000
.001
.000
.000
.048
.036
.046
.046
.000
.000
.046
.000
.016
.008
.000
.000

V
.184
.229
.067
.174
.137
.068
.095
.227
.062
.106
.026
.076
.037
.028
.029
.034
.025
.050
.078
.042
.054
.056
.024
.026
.024
.024
.120
.091
.024
.059
.029
.033
.064
.087

275
Gender of Victim
Age of Victim
Child Victim
Victim Relationship to Offender
Organizational vs. Against Citizenry
Internal vs. Organizational
Official Capacity
Drug-related
Alcohol-related
Sex-related
Violence-related
Police Sexual Violence
Driving While Female Encounter
Officer was Reassigned
Officer was Demoted in Rank
Officer's Supervisor was Disciplined
Officer's Chief is Under Scrutiny
Discussion of Agency Scandal / Cover Up
Citizen Complaint as Method of Detection
DUI-related Traffic Accident
Injuries in a DUI-related Traffic Accident
DUI in Personally-owned Vehicle
DUI: Refused Field Sobriety Tests
DUI: Refused BAC Test
Off-Duty: Identified Self as a Police Officer
Off-Duty: Showed Police Weapon
Off-Duty: Conducted a Search
Off-Duty: Made an Arrest
Family Violence
DUI: Officer Resisted Arrest
Oxycodone
Cocaine
Crack
Marijuana
OIDV: Weapon: Hands / Fist
OIDV: Verbal Threats / Violent Ultimatums

V81
V83
V84
V85
V90
V91
V92
V93
V94
V95
V96
V98
V99
V102
V103
V105
V106
V107
V108
V110
V111
V115
V116
V117
V119
V121
V123
V124
V126
V127
V133
V139
V140
V148
V164
V167

3668
1848
3990
3934
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

23.663
159.139
31.977
111.173
4.806
68.775
226.119
25.553
42.196
6.121
103.675
94.879
85.219
13.877
4.697
127.576
162.480
180.093
14.587
39.181
13.982
31.774
4.836
10.132
4.289
4.264
16.089
6.484
16.650
4.097
6.647
29.094
6.285
8.800
9.072
9.421

1
79
1
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.000
.000
.000
.000
.028
.000
.000
.000
.000
.013
.000
.000
.000
.000
.030
.000
.000
.000
.000
.000
.000
.000
.028
.001
.038
.039
.000
.011
.000
.043
.010
.000
.012
.003
.003
.002

.080
.293
.090
.168
.027
.101
.183
.062
.079
.030
.124
.119
.113
.045
.026
.138
.155
.164
.047
.076
.046
.069
.027
.039
.025
.025
.049
.031
.050
.025
.031
.066
.031
.036
.037
.037

276
OIDV: Victim Injury, Nonfatal
42 U.S.C. 1981 Civil Defendant
42 U.S.C. 1985 Civil Defendant
42 U.S.C. 1997 Civil Defendant
28 U.S.C. 1441 Civil Rights Action Removed
18 U.S.C. 242 Criminal Defendant
Most Serious Offense Charged
Drugs: Personal Use / Using
Drugs: Planting Evidence
Drugs: Facilitating Drug Trade
Drugs: Shakedown from Street Dealers
Drugs: Shakedown from Warrantless Search
Drugs: Shakedown from Legit Search/Raid
Drugs: Shakedown from Car Stops
Drugs: Falsification
Drugs: Shakedown - Aggregate
Drugs: Stimulants
Drugs: Cannabis
Age Categorical
Years of Service Categorical
Metro or NonMetro County
Geographic Region
Geographic Division
Rank by Function
Victim Age Categorical
Victim Age Difference

V173
V175
V177
V178
V179
V181
V183
V196
V200
V201
V202
V204
V205
V206
V209
V210
Stimul
Cannab
AgeCat
YrsServC
CountyD
GeogReg
GeogDiv
RankFunc
VictimAg
VicAgeDi

6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724
6724

8.627
435.624
632.692
1247.262
1192.487
171.608
320.050
6.408
38.120
3.833
118.716
95.587
28.690
75.474
31.330
99.877
21.921
8.800
108.469
155.578
15.672
65.296
123.337
55.134
90.534
157.015

1
1
1
1
1
1
63
1
1
1
1
1
1
1
1
1
1
1
10
10
1
3
8
2
9
103

.003
.000
.000
.000
.000
.000
.000
.011
.000
.050
.000
.000
.000
.000
.000
.000
.000
.003
.000
.000
.000
.000
.000
.000
.000
.000

.036
.255
.307
.431
.421
.160
.218
.031
.075
.024
.133
.119
.065
.106
.068
.122
.057
.036
.127
.152
.048
.099
.135
.091
.116
.153

277
Table 19. Logistic Regression Model Predicting Being Named a 42 USC §1983 Party-Defendant at Some Point (N = 1,278)
95% CI for Exp(B)
B

Wald

Exp(B)

Years of Service (V4)

0.067

0.010

43.606

<.001

1.069

1.048

1.091

Duty Status (V6)

1.086

0.187

33.762

<.001

2.962

2.054

4.273

Full-Time Sworn Personnel Categorical (V10)

0.071

0.030

5.498

.019

1.073

1.012

1.139

Murder & Nonnegligent Manslaughter (V36)

0.692

0.282

6.011

.014

1.998

1.149

3.475

Kidnapping/Abduction (V39)

0.753

0.272

7.669

.006

2.123

1.246

3.617

Victim Relationship to Offender (V85)

0.175

0.065

7.308

.007

1.191

1.049

1.352

Driving While Female Encounter (V99)

0.934

0.346

7.285

.007

2.544

1.291

5.011

Officer was Assigned to Another Position (V102)

0.854

0.334

6.544

.011

2.349

1.221

4.518

Officer's Supervisor was Disiplined / Reassigned (V105)

2.369

0.488

23.592

<.001

10.686

4.108

27.796

-1.348

0.465

8.421

.004

0.260

0.104

0.645

DUI in a Privately-owned Vehicle (V115)
Off-Duty: Identified Self as an Officer (V119)

1.060

0.301

12.374

<.001

2.885

1.599

5.208

Family Violence (V126)

0.737

0.285

6.688

.010

2.090

1.195

3.653

Cocaine (V139)

2.007

0.747

7.227

.007

7.440

1.722

32.140

42 USC §1981 Civil Defendant at Some Point (V175)

3.962

0.718

30.421

<.001

52.584

12.863

214.965

18 USC §242 Criminal Defendant at Some Point (V181)

1.837

0.639

8.264

.004

6.276

1.794

21.956

-0.019

0.004

23.805

<.001

0.981

0.974

0.989

Most Serious Offense Charged (V183)
- 2 Log Likelihood

1177.989

Model Chi-Square

328.366

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.227
.327

95% CI for AUC

.486

.743

UL
.723

.764

278
Table 20. Sex-related Police Crime Arrest Cases, 2005-2011: Arrested Officers and Employing Agencies (N = 1,475)
n
(%)
n
(%)
Sex
Male
Female
Age
19-23
24-27
28-31
32-35
36-39
40-43
44-47
48-51
52-55
56 or older
Missing

1,467
8

(99.5)
(0.5)

26
133
208
221
261
201
163
87
49
48
78

(1.8)
(9.0)
(14.1)
(15.0)
(17.6)
(13.6)
(11.1)
(5.9)
(3.3)
(3.3)
(5.3)

Years of Service
0-2
3-5
6-8
9-11
12-14
15-17
18-20
21-23
24-26
27 or more years
Missing

204
246
163
168
119
84
91
38
44
38
280

(13.8)
(16.7)
(11.0)
(11.4)
(8.1)
(5.7)
(6.1)
(2.6)
(3.0)
(2.6)
(19.0)

Arresting Agency
Employing Agency
Another Agency

556
919

(37.7)
(62.3)

Officer Duty Status
On-Duty
Off-Duty

682
793

(46.2)
(53.8)

Rank
Officer
Detective
Corporal
Sergeant
Lieutenant
Captain
Major
Colonel
Deputy Chief
Chief

1,168
53
31
110
37
9
1
0
7
59

(79.2)
(3.6)
(2.0)
(7.5)
(2.5)
(0.6)
(0.1)
(0.0)
(0.5)
(4.0)

Function
Patrol & Street Level
Line/Field Supervisor
Management

1,221
178
76

(82.8)
(12.1)
(5.1)

259
242
658
316

(17.6)
(16.4)
(44.6)
(21.4)

1,229
246

(83.3)
(16.7)

Region of United States
Northeastern States
Midwestern States
Southern States
Western States
Level of Rurality
Metropolitan County
Non-Metro County

(%)

61
280
30
1,040
56
4
3
1

(4.1)
(19.0)
(2.0)
(70.5)
(3.8)
(0.3)
(0.2)
(0.1)

Full-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499
500-999
1,000 or more

20
12
85
112
145
165
140
178
144
125
349

(1.3)
(0.8)
(5.8)
(7.6)
(9.8)
(11.2)
(9.5)
(12.1)
(9.8)
(8.5)
(23.6)

Part-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499

1,031
64
162
99
77
28
10
4
0

(69.9)
(4.3)
(11.0)
(6.7)
(5.2)
(1.9)
(0.7)
(0.3)
(0.0)

Agency Type
Primary State Police
Sheriff's Office
County Police Dept.
Municipal Police Dept.
Special Police Dept.
Constable
Tribal Police Dept.
Regional Police Dept.

279
Table 21. Most Serious Offense Charged in Sex-related Police Crime Arrest Cases, 2005-2011 (N = 1,475)
n
(%)
Forcible Fondling
Forcible Rape
Statutory Rape
Unclassified Sex Crime
Forcible Sodomy
Pornography / Obscene Material
Intimidation
Online Solicitation of a Child
Prostitution
Simple Assault
Indecent Exposure
Official Misconduct / Oppression
Aggravated Assault
Bribery
Kidnapping / Abduction
Criminal Deprivation of Civil Rights
Extortion / Blackmail
Assisting or Promoting Prostitution
Sexual Assault with an Object
Other / Unclassified Offenses

352
322
100
98
94
86
52
44
42
37
37
35
25
19
13
13
12
12
10
10

(23.9)
(21.8)
(6.8)
(6.6)
(6.4)
(5.8)
(3.5)
(3.0)
(2.8)
(2.5)
(2.5)
(2.4)
(1.7)
(1.3)
(0.9)
(0.9)
(0.8)
(0.8)
(0.7)
(0.7)

Burglary / Breaking & Entering
Disorderly Conduct
Arson
Unclassified Theft / Larceny
Drug / Narcotic violation
Incest
False Report / False Statement
Destroying / Tampering Evidence
Obstruction of Justice
False Pretenses / Swindle
Wire Fraud
Murder / Nonnegligent Manslaughter
Theft from Building
Robbery
Liquor Law violation
Peeping Tom
Credit Card / ATM Fraud
Stolen Property Offenses
Weapons Law violation
Restraining Order / Protection Order violation

(%)

7
7
6
6
4
4
4
3
3
2
2
2
2
2
2
2
1
1
1
1

(0.5)
(0.5)
(0.4)
(0.4)
(0.3)
(0.3)
(0.3)
(0.2)
(0.2)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)

280
Table 22. Victim Characteristics in Sex-related Police Crime Arrest Cases, 2005-2011 (N = 1,475)
n
(%) (Valid %)
Victim's Sex
Female
Male
Missing
Victim's Age
Birth-11
12-13
14-15
16-17
18-19
20-24
25-32
33-41
42 or older
Missing

1,160
143
172

(78.6)
(9.7)
(11.7)

(89.0)
(11.0)

108
118
202
139
54
73
64
42
14
661

(7.3)
(8.0)
(13.7)
(9.4)
(3.7)
(4.9)
(4.3)
(2.8)
(0.9)
(44.8)

(13.3)
(14.5)
(24.8)
(17.1)
(6.6)
(8.9)
(7.9)
(5.2)
(1.7)

Victim's Relationship
Current Spouse
Former Spouse
Current Girlfriend or Boyfriend
Former Girlfriend or Boyfriend
Child or Stepchild
Some Other Relative
Unrelated Child
Stranger or Acquaintance
Missing
Victim's Law Enforcement Status
Victim is Not a Police Officer
Victim is a Police Officer
Missing
Victim Adult or Child
Adult
Child
Missing

(%)

(Valid %)

10
5
15
12
59
55
527
658
134

(0.7)
(0.4)
(1.0)
(0.8)
(4.0)
(3.7)
(35.7)
(44.6)
(9.1)

(0.7)
(0.4)
(1.1)
(0.9)
(4.4)
(4.1)
(39.3)
(49.1)

1,320
24
131

(89.5)
(1.6)
(8.9)

(98.2)
(1.8)

721
656
98

(48.9)
(44.5)
(6.6)

(52.4)
(47.6)

281
Table 23. Bivariate Associations of Conviction in Sex-related Arrest Cases
Variable Label
Year of Arrest
Years of Service
Gender
Duty Status
Rank
Full-Time Sworn Personnel (categorical)
State
Part-Time Sworn Personnel (categorical)
Urban / Rural Continuum
Pornography
Forcible Sodomy
Indecent Exposure
Drunkenness
Age of Victim
Child Victim
Victim's Relationship to the Offender
28 U.S.C. §1441 Civil Case Removed
18 U.S.C. §242 Criminal Defendant
Job Lost
Geographic Region
Geographic Division
Rank Function
Victim Age Categorical

Variable
V2
V4
V5
V6
V7
V10
V11
V12
V13
V49
V54
V59
V68
V83
V84
V85
V179
V181
joblostbin
geogreg
geogdiv
rankfunc
vicagecat

N
986
820
986
986
986
986
986
986
986
986
986
986
986
541
920
905
986
986
986
986
986
986
986

χ
22.294
52.020
4.061
7.117
23.227
19.604
80.347
16.568
16.956
11.451
4.401
3.816
4.061
86.980
25.110
36.740
4.365
10.527
36.614
10.588
19.175
10.143
21.231

df
6
34
1
1
8
10
48
7
8
1
1
1
1
50
1
7
1
1
1
3
8
2
9

p
.001
.025
.044
.008
.003
.033
.002
.020
.031
.001
.036
.051
.044
.001
.000
.000
.037
.001
.000
.014
.014
.006
.012

V
.150
.252
.064
.085
.153
.141
.285
.130
.131
.108
.067
.062
.064
.401
.165
.201
.067
.103
.193
.104
.139
.101
.147

282
Table 24. Sex-related Arrest Cases: Logistic Regression Model Predicting Conviction (N = 447)
95% CI for Exp(B)
B

Years of Service

0.056

Pornography / Obscene Material
Child Victim
Lost Job

.007

1.737

0.769

5.107

0.728

0.279

6.832

1.661

0.293

32.044

354.228

Model Chi-Square

50.179

Cox & Snell R
Nagelkerke R
ROC R
AUC

7.353

- 2 Log Likelihood
2

Wald

0.021

Exp(B)

1.058

1.016

1.102

.024

5.678

1.259

25.608

.009

2.071

1.200

3.575

< .001

5.266

2.963

9.360

<.001

.106
.178

95% CI for AUC

.346

.673

UL
.621

.725

283
Table 25. Bivariate Associations of Job Loss in Sex-related Police Crime Arrest Cases
Variable Label
Year of Arrest
Age
Years of Service
Gender
Duty Status
Type of Agency
Full-Time Sworn Personnel (categorical)
State
Part-Time Sworn Personnel (categorical)
Urban / Rural Continuum
Arresting Agency
Bribery
Kidnapping / Abduction
Prostitution
Forcible Fondling
Incest
Indecent Exposure
Unclassified Sex Crime
Criminal Deprivation of Civil Rights
Internal Crime Against the Organization
Official Capacity
Violence-related
Police Sexual Violence
Driving While Female Encounter
Suspended
Crime Detected by Citizen Complaint
Conviction
Off-Duty: In Uniform
Marijuana
42 U.S.C. §1983 Civil Defendant
42 U.S.C. §1985 Civil Defendant
28 U.S.C. §1441 Civil Case Removed
18 U.S.C. §242 Criminal Defendant
Geographic Region
Geographic Division

Variable
V2
V3
V4
V5
V6
V9
V10
V11
V12
V13
V14
V19
V39
V50
V56
V57
V59
V61
V65
V91
V92
V96
V98
V99
V104
V108
V109
V120
V148
V176
V177
V179
V181
geogreg
geogdiv

N
1,475
1,397
1,195
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475
986
1,475
1,475
1,475
1,475
1,475
1,475
1,475
1,475

χ
135.487
68.062
62.775
20.753
5.692
15.908
41.867
111.725
39.503
20.333
5.911
13.207
5.104
9.096
6.148
5.991
7.600
8.111
4.892
34.420
7.595
6.956
4.849
15.105
42.412
9.246
36.614
5.478
4.857
9.706
6.845
5.434
7.627
21.667
27.896

df
6
46
36
1
1
7
10
48
7
8
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
3
8

p
.000
.019
.004
.000
.017
.026
.000
.000
.000
.009
.015
.000
.024
.003
.013
.014
.006
.004
.027
.000
.006
.008
.028
.000
.000
.002
.000
.019
.028
.002
.009
.020
.006
.000
.000

V
.303
.221
.229
.119
.062
.104
.168
.275
.164
.117
.063
.095
.059
.079
.065
.064
.072
.074
.058
.153
.072
.069
.057
.101
.170
.079
.193
.061
.057
.081
.068
.061
.072
.121
.138

284
Table 26. Sex-related Arrest Cases: Logistic Regression Model Predicting Job Loss (N = 801)
95% CI for Exp(B)
B
Year of Arrest

0.339

Age

0.061

Wald
31.037

p
< .001

Exp(B)
1.404

1.246

1.582

-0.036

0.012

8.489

.004

0.965

0.942

0.988

Internal Crime Against the Organization

0.121

0.045

7.167

.007

1.129

1.033

1.234

Official Capacity

0.890

0.282

9.973

.002

2.435

1.402

4.231

Driving While Female Encounter

0.999

0.455

4.825

.028

2.715

1.114

6.620

Suspended

-0.942

0.255

13.651

< .001

0.390

0.236

0.642

Conviction

1.341

0.237

31.918

< .001

3.824

2.401

6.089

Marijuana

-1.929

0.693

7.740

.005

0.145

0.037

0.566

0.798

0.254

9.879

.002

2.221

1.350

3.653

42 U.S.C. §1983 Civil Defendant at Some Point
- 2 Log Likelihood

625.507

Model Chi-Square

141.128

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.162
.262

95% CI for AUC

.544

.772

UL
.733

.810

285
Table 27. Bivariate Associations of Child Victims in Sex-related Arrest Cases
Variable Label
Age
Years of Service
Duty Status
Full-Time Sworn Personnel (categorical)
State
Urban to Rural Continuum
Arresting Agency
Aggravated Assault
Simple Assault
Intimidation
Bribery
Burglary
Extortion
Kidnapping / Abduction
Unclassified Theft / Larceny
Pornography
Prostitution
Forcible Rape
Forcible Fondling
Incest
Statutory Rape
Indecent Exposure
On-Line Solicitation of a Child
Unclassified Sex Crime
Weapons Law violation
Criminal Deprivation of Civil Rights
Disorderly Conduct
False Report / False Statement
Obstruction of Justice
Official Misconduct / Oppression
Unclassified Offenses
Gender of Victim
Internal Crime Against the Organization
Official Capacity

Variable
V3
V4
V6
V10
V11
V13
V14
V16
V17
V18
V19
V20
V26
V39
V47
V49
V50
V53
V56
V57
V58
V59
V60
V61
V63
V65
V66
V70
V74
V75
V80
V81
V91
V92

N
1,311
1,120
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,296
1,377
1,377

χ2
125.721
92.784
323.355
65.462
121.063
48.962
44.190
30.395
24.392
41.325
46.090
20.342
11.053
86.974
3.983
99.843
21.057
25.396
37.176
10.497
179.498
13.792
61.773
14.145
7.014
27.903
6.308
6.207
5.408
161.527
23.373
51.951
72.810
330.080

df
46
35
1
10
48
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1

p
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.001
.000
.046
.000
.000
.000
.000
.001
.000
.000
.000
.000
.008
.000
.012
.013
.020
.000
.000
.000
.000
.000

V
.310
.288
.485
.218
.297
.189
.179
.149
.133
.173
.183
.122
.090
.251
.054
.269
.124
.136
.164
.087
.361
.100
.212
.101
.071
.142
.068
.067
.063
.342
.130
.200
.230
.490

286
Violence-related
Profit-motivated
Police Sexual Violence
Driving While Female Encounter
Discussion of Agency Scandal or Cover Up
Method of Crime Detection
Conviction
Off-Duty: In Uniform
Off-Duty: Showed Police Weapon
Family Violence
OIDV: Weapon: Personally Owned Gun
OIDV: Weapon: Hands / Fists
OIDV: Verbal Threats / Violent Ultimatums
OIDV: Victim Injured, nonfatal
42 U.S.C. §1983 Civil Defendant Some Point
28 U.S.C. §1441 Civil Case Removed
18 U.S.C. §242 Criminal Defendant
Age Categorical
Years of Service Categorical
Geographic Division

V96
V97
V98
V99
V107
V108
V109
V120
V121
V126
V157
V164
V167
V173
V176
V179
V181
agecat
yrsservcat
geogdiv

1,377
1,377
1,377
1,377
1,377
1,377
920
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377
1,377

57.564
9.394
290.300
142.850
5.352
28.531
25.110
6.308
13.870
20.034
4.566
4.361
6.308
6.194
60.897
12.257
11.053
50.515
31.988
36.261

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
10
8

.000
.002
.000
.000
.021
.000
.000
.012
.000
.000
.033
.037
.012
.013
.000
.000
.001
.000
.000
.000

.204
.083
.459
.322
.062
.144
.165
.068
.100
.121
.058
.056
.068
.067
.210
.094
.090
.192
.152
.162

287
Table 28. Sex-related Arrest Cases: Logistic Regression Model Predicting Child Victims (N = 722)
95% CI for Exp(B)
B
Duty Status

-1.798

Wald

0.315

32.509

< .001

Exp(B)
0.166

0.089

0.307

Full-Time Sworn Personnel (categorical)

-0.195

0.054

13.134

< .001

0.823

0.741

0.915

Urban to Rural Continuum Code

-0.194

0.087

5.004

.025

0.824

0.695

0.976

Aggravated Assault

-2.688

0.771

12.155

< .001

0.068

0.015

0.308

Intimidation

-1.556

0.561

7.692

.006

0.211

0.070

0.634

Kidnapping / Abduction

-1.390

0.455

9.322

.002

0.249

0.102

0.608

1.357

0.652

4.336

.037

3.885

1.083

13.936

Pornography / Obscene Material
Forcible Fondling

1.245

0.259

23.100

< .001

3.474

2.091

5.774

Indecent Exposure

-1.357

0.517

6.880

.009

0.257

0.093

0.710

Unclassified Sex Crime

1.434

0.353

16.479

< .001

4.195

2.099

8.382

Weapons Law violation

-3.054

1.125

7.367

.007

0.047

0.005

0.428

Obstruction of Justice

-3.822

1.191

10.289

.001

0.022

0.002

0.226

Gender of Victim

1.816

0.452

16.148

< .001

6.144

2.535

14.896

Violence-related

-1.658

0.402

17.046

< .001

0.191

0.087

0.419

Police Sexual Violence

-0.986

0.312

10.008

.002

0.373

0.202

0.687

Driving While Female Encounter

-1.744

0.578

9.115

.003

0.175

0.056

0.542

Discussion of Agency Scandal or Cover Up

1.861

0.577

10.412

.001

6.427

2.076

19.900

Conviction

0.799

0.285

7.869

.005

2.223

1.272

3.883

Family Violence

0.941

0.399

5.557

.018

2.563

1.172

5.606

Geographic Division

0.101

0.047

4.596

.032

1.106

1.009

1.212

- 2 Log Likelihood

492.641

Model Chi-Square

505.330

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.503
.672

95% CI for AUC

.840

.920

UL
.902

.938

288
Table 29. Bivariate Associations of Conviction in Police Sexual Violence Arrest Cases
Variable Label
Year of Arrest
Urban to Rural Continuum
False Pretenses / Swindle
Forcible Sodomy
Forcible Fondling
Statutory Rape
Victim is a Police Officer
Victim is a Child
Victim's Relationship
Violence-related
Drugs: Personal Use / Using
Job Lost Binary
Georgraphic Division

Variable
V2
V13
V27
V54
V56
V58
V82
V84
V85
V96
V196
joblostbin
geogdiv

N
431
431
431
431
431
431
430
427
427
431
431
431
431

χ
18.185
20.573
4.021
6.671
6.754
3.966
4.112
9.742
20.988
4.955
4.021
22.139
15.821

df
6
8
1
1
1
1
1
1
6
1
1
1
8

p
.006
.008
.045
.010
.009
.046
.043
.002
.002
.026
.045
.000
.045

V
.205
.008
.097
.124
.125
.096
.098
.151
.222
.107
.097
.227
.192

289
Table 30. Police Sexual Violence Arrest Cases: Logistic Regression Model Predicting Conviction (N = 427)
95% CI for Exp(B)
B

Wald

Exp(B)

Forcible Sodomy

1.146

0.495

5.368

.021

3.146

1.193

8.297

Forcible Fondling

-0.779

0.260

8.965

.003

0.459

0.276

0.764

Child Victim

1.321

0.389

11.529

.001

3.748

1.748

8.037

Lost Job

1.329

0.325

16.750

< .001

3.778

1.999

7.140

- 2 Log Likelihood

381.630

Model Chi-Square

44.602

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.099
.157

95% CI for AUC

.422

.711

UL
.647

.774

290
Table 31. Bivariate Assocations of Job Loss in Police Sexual Violence Arrest Cases
Variable Label
Year of Arrest
Years of Service
Gender
Type of Agency
Full-Time Sworn (categorical)
State
Part-Time Sworn (categorical)
Simple Assault
Intimidation
Bribery
Burglary
Kidnapping / Abduction
Indecent Exposure
Other / Unclassified Sex Crime
Weapons Law violation
Official Misconduct / Oppression
All Other Offenses / Unclassified
Internal Crime Against the Organization
Alcohol-related
Driving While Female Encounter
Suspended
Officer's Supervisor was Disciplined
Conviction
Years of Service Categorical
Geographic Region
Geographic Division
Rank by Function
Victim Age Categorical

Variable
V2
V4
V5
V9
V10
V11
V12
V17
V18
V19
V20
V39
V59
V61
V63
V75
V80
V91
V94
V99
V104
V105
V109
yrsservcat
geogreg
geogdiv
rankfunc
vicagecat

N
622
495
622
622
622
622
622
622
622
622
622
622
622
622
622
622
622
622
622
622
622
622
431
622
622
622
622
622

χ
60.900
61.583
18.253
17.772
18.829
78.683
17.903
4.288
7.658
9.214
5.451
6.004
3.742
6.219
6.685
7.616
5.706
52.301
4.667
12.156
23.630
3.776
22.139
25.372
12.336
17.510
8.663
22.063

df
6
29
1
6
10
45
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
3
8
2
9

p
.000
.000
.000
.007
.042
.001
.012
.038
.006
.002
.020
.014
.053
.013
.010
.006
.017
.000
.031
.000
.000
.052
.000
.005
.006
.025
.013
.009

V
.313
.353
.171
.169
.174
.356
.170
.083
.111
.122
.094
.098
.078
.100
.104
.111
.096
.290
.087
.140
.195
.078
.227
.202
.141
.168
.118
.188

291
Table 32. Police Sexual Violence Arrest Cases: Logistic Regression Model Predicting Job Loss (N = 354)
95% CI for Exp(B)
B
Part-Time Sworn Personnel (categorical)
Weapons Law violation

0.639

0.217

Wald
8.682

p
.003

Exp(B)

1.894

1.238

2.896

-2.504

1.137

4.846

.028

0.082

0.009

0.760

Internal Crime Against the Organization

0.260

0.051

25.790

< .001

1.297

1.173

1.433

Driving While Female Encounter

1.178

0.494

5.681

.017

3.249

1.233

8.560

1.741

0.434

16.068

< .001

5.705

2.435

13.366

-0.262

0.083

9.904

.002

0.770

0.654

0.906

Conviction
Years of Service (categorical)
- 2 Log Likelihood

201.017

Model Chi-Square

64.762

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.167
.317

95% CI for AUC

.470

.735

UL
.651

.819

292
Table 33. Bivariate Associations of Conviction in Driving While Female Police Crime Arrest Cases
Variable Label
Year of Arrest
Full-Time Sworn Personnel (categorical)
Burglary
Drug / Narcotic violation
Unclassified Theft / Larceny
Sexual Assault with an Object
Forcible Fondling
Economically-motivated
Against the Citizenry
Internal Crime Against the Organization
Alcohol-related
Cocaine
42 U.S.C. §1983
Drugs: Using / Personal Use
Job Loss (binary)
Stimulant
Age Categorical
Rank Function

Variable
V2
V10
V20
V23
V47
V55
V56
V88
V90
V91
V94
V139
V176
V196
joblossbin
stimulant
agecat
rankfunc

N
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126
126

χ
14.407
22.232
9.101
4.514
4.827
6.081
5.277
4.514
4.514
7.204
9.895
4.514
3.728
4.514
3.967
4.514
18.588
5.769

df
6
9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
8
1

p
.025
.008
.003
.034
.028
.014
.022
.034
.034
.007
.002
.034
.054
.034
.046
.034
.017
.016

V
.338
.420
.269
.189
.196
.220
.205
.189
.189
.239
.280
.189
.172
.189
.177
.189
.384
.214

293
Table 34. Driving While Female Encounters Arrest Cases: Logistic Regression Model Predicting Conviction (N = 126)
95% CI for Exp(B)
B

0.557

5.659

.017

0.266

0.089

0.792

0.207

0.072

8.337

.004

1.230

1.069

1.416

-2.705

0.993

7.416

.006

0.067

0.010

0.468

1.438

0.579

6.167

.013

4.214

1.354

13.113

- 2 Log Likelihood

88.550

Model Chi-Square

31.206

Cox & Snell R
Nagelkerke R
ROC R
AUC

0.129

-1.324

42 U.S.C. §1983 Civil Defendant at Some Point

.046

Exp(B)

Forcible Fondling
Alcohol-related

3.969

-2.050

Internal Crime Against the Organization

1.029

Wald

Sexual Assault with an Object

0.017

0.967

<.001

.219
.358

95% CI for AUC

.662

.831

UL
.741

.921

294
Table 35. Bivariate Assocations of Job Loss in Drving While Female Arrest Cases
Variable Label
Year of Arrest
Age
Years of Service
Rank
State
Bribery
Kidnapping / Abduction
Forcible Rape
Indecent Exposure
Other / Unclassified Sex Crime
Official Misconduct / Oppression
Organizational versus Against Citizenry
Internal versus Organizational
Suspended
Conviction
Off-Duty: Identified Self as an Officer
Victim Age Categorical
Victim Age Difference

Variable
V2
V3
V4
V7
V11
V19
V39
V53
V59
V61
V75
V90
V91
V104
V109
V119
vicagecat
vicagediff

N
174
168
144
174
174
174
174
174
174
174
174
174
174
174
126
174
174
174

χ
15.536
47.043
42.173
14.145
53.896
6.714
4.631
4.293
12.402
5.870
5.792
5.476
13.514
4.094
3.967
5.476
22.217
51.795

df
6
33
22
4
33
1
1
1
1
1
1
1
1
1
1
1
7
28

p
.016
.054
.006
.007
.012
.010
.031
.038
.000
.015
.016
.019
.000
.043
.046
.019
.002
.004

V
.299
.529
.541
.285
.557
.196
.163
.157
.267
.184
.182
.177
.279
.153
.177
.177
.357
.546

295
Table 36. Driving While Female Encounters Arrest Cases: Logistic Regression Model Predicting Job Loss (N = 105)
95% CI for Exp(B)
B
Internal Crime Against the Organization

Wald

Exp(B)

0.357

0.141

6.441

.011

1.429

1.085

1.883

Victim Age Difference

-0.072

0.031

5.258

.022

0.931

0.875

0.990

- 2 Log Likelihood

41.223

Model Chi-Square

15.344

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.136
.326

95% CI for AUC

.532

.766

UL
.671

.860

296
Table 37. Alcohol-related Police Crime Arrest Cases, 2005-2011: Arrested Officers and Employing Agencies (N = 1,405)
n
(%)
n
(%)
Sex
Male
Female
Age
19-23
24-27
28-31
32-35
36-39
40-43
44-47
48-51
52-55
56 or older
Missing
Years of Service
0-2
3-5
6-8
9-11
12-14
15-17
18-20
21-23
24-26
27 or more years
Missing
Arresting Agency
Employing Agency
Another Agency

1,314
91

(93.5)
(6.5)

41
152
175
172
216
172
144
66
30
21
216

(2.9)
(10.8)
(12.5)
(12.2)
(15.4)
(12.2)
(10.3)
(4.7)
(2.1)
(1.5)
(15.4)

167
198
139
133
114
82
78
46
29
27
392

(11.9)
(14.1)
(9.9)
(9.5)
(8.1)
(5.8)
(5.5)
(3.3)
(2.1)
(1.9)
(27.9)

396
1,009

(28.2)
(71.8)

Officer Duty Status
On-Duty
Off-Duty

170
1,235

(12.1)
(87.9)

Rank
Officer
Detective
Corporal
Sergeant
Lieutenant
Captain
Major
Colonel
Deputy Chief
Chief

1,051
87
23
139
37
15
4
1
14
34

(74.8)
(6.2)
(1.6)
(9.9)
(2.6)
(1.1)
(0.3)
(0.1)
(1.0)
(2.4)

Function
Patrol & Street Level
Line/Field Supervisor
Management

1,138
199
68

(81.0)
(14.2)
(4.8)

309
358
548
190

(22.0)
(25.5)
(39.0)
(13.5)

1,212
193

(86.3)
(13.7)

Region of United States
Northeastern States
Midwestern States
Southern States
Western States
Level of Rurality
Metropolitan County
Non-Metro County

(%)

74
214
54
1,028
24
3
7
1

(5.3)
(15.2)
(3.8)
(73.2)
(1.7)
(0.2)
(0.5)
(0.1)

Full-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499
500-999
1,000 or more

1
6
53
53
143
130
170
210
127
113
399

(0.1)
(0.4)
(3.8)
(3.8)
(10.2)
(9.3)
(12.1)
(14.9)
(9.0)
(8.0)
(28.4)

Part-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499

1,082
37
104
80
69
20
12
1
0

(77.0)
(2.6)
(7.4)
(5.7)
(4.9)
(1.4)
(0.9)
(0.1)
(0.0)

Agency Type
Primary State Police
Sheriff's Office
County Police Dept.
Municipal Police Dept.
Special Police Dept.
Constable
Tribal Police Dept.
Regional Police Dept.

297
Table 38. Most Serious Offense Charged in Alcohol-related Police Crime Arrest Cases, 2005-2011 (N = 1,405)
n
(%)
Driving Under the Influence
Simple Assault
Aggravated Assault
Weapons Law violation
Forcible Fondling
Disorderly Conduct
Murder & Nonnegligent Manslaughter
Forcible Rape
Destruction of Property / Vandalism
Drunkenness
Liquor Law violation
All Other / Unclassified Offenses
Negligent Manslaughter
Intimidation
Hit & Run
False Report / False Statement
Forcible Sodomy
Unclassified Sex Crime
Family Offenses, nonviolent

817
149
103
47
27
27
26
26
23
22
19
16
14
11
9
6
5
5
5

(58.1)
(10.6)
(7.3)
(3.3)
(1.9)
(1.9)
(1.9)
(1.9)
(1.6)
(1.6)
(1.3)
(1.1)
(1.0)
(0.7)
(0.6)
(0.4)
(0.4)
(0.4)
(0.4)

Obstructing Justice
Burglary / Breaking & Entering
Drug / Narcotic violation
Extortion / Blackmail
Statutory Rape
Indecent Exposure
Arson
False Pretenses / Swindle
Reckless Endangerment
Impersonation
Sexual Assault with an Object
Official Misconduct / Official Oppression
Theft from Building
All Other / Unclassified Larceny
Prostitution
Robbery
Criminal Civil Rights violation
Evidence: Destroying / Tampering
Trespass of Real Property

(%)

5
4
5
4
4
4
3
3
3
2
2
2
1
1
1
1
1
1
1

(0.4)
(0.3)
(0.4)
(0.3)
(0.3)
(0.3)
(0.2)
(0.2)
(0.2)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)

298
Table 39. Victim Characteristics in Alcohol-related Police Crime Arrest Cases, 2005-2011 (N = 1,405)
n
(%) (Valid %)
Victim's Sex
Female
Male
Missing
Victim's Age
Birth-11
12-13
14-15
16-17
18-19
20-24
25-32
33-41
42 or older
Missing

263
242
900

(18.7)
(17.2)
(64.1)

(52.1)
(47.9)

14
12
20
17
23
53
66
29
49
1,122

(1.0)
(0.9)
(1.4)
(1.2)
(1.6)
(3.8)
(4.7)
(2.1)
(3.5)
(79.9)

(5.0)
(4.3)
(7.1)
(6.0)
(8.1)
(18.7)
(23.3)
(10.2)
(17.3)

(%)

(Valid %)

42
5
45
7
19
6
47
382
852

(3.0)
(0.4)
(3.2)
(0.5)
(1.4)
(0.4)
(3.3)
(27.2)
(60.6)

(7.6)
(0.9)
(8.1)
(1.3)
(3.4)
(1.1)
(8.5)
(69.1)

Victim's Law Enforcement Status
Victim is Not a Police Officer
Victim is a Police Officer
Missing

500
58
847

(35.6)
(4.1)
(60.3)

(89.6)
(10.4)

Victim Adult or Child
Adult
Child
Missing

495
69
841

(35.2)
(4.9)
(59.9)

(87.8)
(12.2)

299
Table 40: Police DUI Arrest Cases: Incident Events

DUI in Privately-Owned Vehicle
DUI Traffic Accident
DUI Traffic Accident with Injuries
Refused BAC Test
DUI Traffic Accident: Officer Fled Scene
Officer in Possession of Firearm while DUI
Refused Field Sobriety Tests
DUI in Take-Home Police Vehicle
Off-Duty: Identified Self as Police Officer
DUI Traffic Accident: Officer Charged with Hit & Run
Officer Resisted Arrest
DUI Actually On-Duty in Police Vehicle
DUI Traffic Accident: Fatality Resulting
DUI Traffic Accident: Flipped their Car
DUI in Police Vehicle while Out of Jurisdiction
Off-Duty: Officer's Employing Agency Requires 24/7 Availability
DUI Traffic Accident: Driving in Wrong Direction
Off-Duty: Flashed Badge
DUI Traffic Accident: Driving a Motorcycle
Off-Duty: Displayed Police Weapon
DUI Traffic Accident: Officer Denied Driving
Off-Duty: Family Violence
DUI Traffic Accident: While Evading
DUI Traffic Accident: Flipped Victim's Car
Off-Duty: In Police Uniform
Off-Duty: Made an Arrest
Off-Duty: Intervened in Existing Dispute Per Policy

(%)

836
492
231
195
103
83
81
78
77
76
44
42
39
33
28
23
17
16
15
14
12
10
9
4
2
2
1

(87.1)
(51.2)
(24.1)
(20.3)
(10.7)
(8.6)
(8.4)
(8.1)
(8.0)
(7.9)
(4.6)
(4.4)
(4.1)
(3.4)
(2.9)
(2.4)
(1.8)
(1.7)
(1.6)
(1.5)
(1.3)
(1.0)
(0.9)
(0.4)
(0.2)
(0.2)
(0.1)

300
Table 41. Police DUI Arrest Cases: Drug-related (N = 49)
n

(%)

Specific Drugs:
Other Depressants (Depressant)
Oxycodone (Narcotic)
Cocaine (Stimulant)
Hydrocodone (Narcotic)
Other Narcotics (Narcotic)
Benzodiazepines (Depressant)
Amphetamine / Methamphetamine (Stimulant)
Marijuana (Cannabis)
Hydromorphone (Narcotic)
Other Stimulants (Stimulant)
Morphine (Narcotic)
Codeine (Narcotic)
MDMA & Analogs (Hallucinogen)
Other Anabolic Steroids (Anabolic Steroid)

15
8
5
4
4
4
3
3
2
2
1
1
1
1

(30.6)
(16.3)
(10.2)
(8.2)
(8.2)
(8.2)
(6.1)
(6.1)
(4.1)
(4.1)
(2.0)
(2.0)
(2.0)
(2.0)

Arrested Officer and Agencies:
Officer is Male
On-Duty at Time of DUI Offense
Arresting Agency is Not Officer's Employer

46
16
34

(93.9)
(32.7)
(69.4)

Note. Categories are not mutually exclusive. Sum of (%) column ≠ 100

301
Table 42. Bivariate Associations of Conviction in Alcohol-related Police Crime Arrest Cases
Variable Label
Rank
State
Arresting Agency
Simple Assault
Property Destruction / Vandalism
Forcible Fondling
Driving Under the Influence
Drunkenness
Hit and Run
Liquor Law violation
Child Victim
Victim's Relationship to the Officer
Sex-related
Driving While Female Encounter
Officer was Demoted
DUI-related Traffic Accident
Injuries in a DUI-related Traffic Accident
DUI in a Personally-owned Vehicle
Off-Duty Ordinance On-Call 24/7
Family Violence
OIDV: Hands / Fist
OIDV: Victim Injured, nonfatal
Job Lost
Geographic Region
Geographic Division

Variable
V7
V11
V14
V17
V22
V56
V67
V68
V72
V73
V84
V85
V95
V99
V103
V110
V111
V115
V118
V126
V164
V173
joblostbin
geogreg
geogdiv

N
654
654
654
654
654
654
654
654
654
654
339
331
654
654
654
654
654
654
654
654
654
654
654
654
654

χ
20.366
95.625
5.294
7.182
4.327
3.681
11.679
4.477
3.987
6.721
4.004
23.297
3.913
6.187
4.653
17.885
11.293
12.756
3.612
10.441
21.157
13.340
12.462
11.270
19.323

df
8
48
1
1
1
1
1
1
1
1
1
7
1
1
1
1
1
1
1
1
1
1
1
3
8

p
.009
.000
.021
.007
.038
.055
.001
.034
.046
.010
.045
.002
.048
.013
.031
.000
.001
.000
.057
.001
.000
.000
.000
.010
.013

V
.176
.382
.090
.105
.081
.075
.134
.083
.078
.101
.109
.265
.077
.097
.084
.165
.131
.140
.074
.126
.180
.143
.138
.131
.172

302
Table 43. Alcohol-related Police Crime Arrest Cases: Logistic Regression Model Predicting Conviction (N = 330)
95% CI for Exp(B)
B

Wald

Exp(B)

Arresting Agency is Not Arrested Officer's Employer

0.577

0.291

3.924

.048

1.781

1.006

3.153

Sex-related

1.110

0.455

5.952

.015

3.035

1.244

7.406

-2.655

0.832

10.182

.001

0.070

0.014

0.359

Driving-While-Female Encounter
DUI in a Personally-Owned-Vehicle
OIDV: Weapon: Hands / Fist
Lost Job

1.286

0.386

11.078

.001

3.619

1.697

7.719

-1.015

0.368

7.590

.006

0.362

0.176

0.746

0.937

0.288

10.601

.001

2.551

1.452

4.483

- 2 Log Likelihood

322.629

Model Chi-Square

7.486

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.156
.229

95% CI for AUC

.376

.688

UL
.641

.734

303
Table 44. Bivariate Associations of Job Loss in Alcohol-related Arrest Cases
Variable Label
Duty Status
Full-Time Sworn Personnel (categorical)
State
Part-Time Sworn Personnel (categorical)
Urban-Rural County Continuum
Aggravated Assault
Bribery
Extortion / Blackmail
Murder & Nonnegligent Manslaughter
Negligent Manslaughter
Kidnapping / Abduction
Pornography / Obscene Material
Forcible Rape
Forcible Sodomy
Forcible Fondling
Statutory Rape
Unclassified Sex Crime
Weapons Law violation
Driving Under the Influence
Destroying / Tampering Evidence
False Report / False Statement
Liquor Law violation
Official Misconduct
Victim is a Police Officer
Child Victim
Victim's Relationship to Arrested Officer
Organizational versus Against Citizenry
Internal Crime Against the Organization
Official Capacity
Sex-related
Violence-related
Profit-motivated
Police Sexual Violence
Driving While Female Encounter

Variable
V6
V10
V11
V12
V13
V16
V19
V26
V36
V37
V39
V49
V53
V54
V56
V58
V61
V63
V67
V69
V70
V73
V75
V82
V84
V85
V90
V91
V92
V95
V96
V97
V98
V99

N
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
558
564
553
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405

χ2
26.456
48.473
106.935
29.408
32.022
8.017
4.919
8.209
10.863
6.527
14.689
4.300
20.642
9.988
9.468
8.395
19.762
5.803
52.293
4.398
4.148
8.634
6.270
5.634
8.325
15.616
23.750
21.321
40.030
55.140
39.284
6.527
40.277
5.700

df
1
10
50
7
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
7
1
2
1
1
1
1
1
1

p
.000
.000
.000
.000
.000
.005
.027
.004
.001
.011
.000
.038
.000
.002
.002
.004
.000
.016
.000
.036
.042
.003
.012
.018
.004
.029
.000
.000
.000
.000
.000
.011
.000
.017

V
.137
.186
.276
.145
.151
.076
.059
.076
.088
.068
.102
.055
.121
.084
.082
.077
.119
.064
.193
.056
.054
.078
.067
.100
.121
.168
.130
.123
.169
.198
.167
.068
.169
.064

304
Officer was Reassigned
Officer was Demoted
Officer's Supervisor was Disciplined
Citizen Complaint as Method of Detection
Conviction
DUI in Personally-owned Vehicle
Off-Duty: Agency has 24/7 On-Call Policy
Off-Duty: In Uniform
OIDV: Protection Order filed Against Officer
42 U.S.C. §1983 Civil Defendant Sometime
42 U.S.C. §1985 Civil Defendant Sometime
28 U.S.C. §1441 Civil Case Removed
Age (categorical)
Years of Service (categorical)
Metropolitan vs. Non-Metro County
Geographic Division
Rank Function
Victim Age (categorical)
Victim Age Difference

V102
V103
V105
V108
V109
V115
V118
V120
V171
V176
V177
V179
agecat
yrservcat
codichot
geogdiv
rankfunc
vicagecat
vicagediff

1,405
1,405
1,405
1,405
654
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405
1,405

13.956
5.065
8.477
15.326
12.462
17.964
13.702
4.919
5.316
9.680
9.651
5.189
39.806
31.374
18.301
25.037
6.496
59.634
125.538

1
1
1
1
1
1
1
1
1
1
1
1
10
10
1
8
2
9
67

.000
.024
.004
.000
.000
.000
.000
.027
.021
.002
.002
.023
.000
.001
.000
.002
.039
.000
.000

.100
.060
.078
.104
.138
.113
.099
.059
.062
.083
.083
.061
.168
.149
.114
.133
.068
.206
.299

305
Table 45. Alcohol-related Police Crime Arrest Cases, 2005-2011: Logistic Regression Model Predicting Job Loss (N = 327)
95% CI for Exp(B)
B

Wald

Exp(B)

Duty Status

1.066

0.473

5.091

.024

2.904

1.150

7.333

Sex-related

1.253

0.389

10.357

.001

3.499

1.632

7.503

Violence-related

0.866

0.310

7.802

.005

2.377

1.295

4.364

Officer was Reassigned to Another Position

-1.893

0.711

7.093

.008

0.151

0.037

0.607

Citizen Complaint as Method of Crime Detection

-1.075

0.303

12.566

< .001

0.341

0.188

0.618

0.965

0.295

10.660

.001

2.624

1.470

4.682

1.879

0.787

5.699

.017

6.549

1.400

30.639

Conviction
28 U.S.C. §1441 Civil Case Removed to Federal Court
- 2 Log Likelihood

360.536

Model Chi-Square

68.251

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.188
.258

95% CI for AUC

.322

.661

UL
.620

.702

306
Table 46. Bivariate Associations of Conviction in DUI Arrest Cases
Variable Label
Duty Status
State
Liquor Law violation
Official Capacity
Officer was Reassigned
Officer was Demoted
DUI-related Traffic Accident
Injuries in a DUI-related Traffic Accident
DUI in a Take-Home Police Vehicle
DUI Refused BAC Test
Off-Duty: Agency has 24/7 On-Call Policy
Oxycodone
Anabolic Steroids (excluding Testosterone)
OIDV: Weapon: Verbal Threats
OIDV: Victim Injured, nonfatal
DUI Traffic Accident: Officer Denied Driving
DUI in a Police Vehicle
Geographic Division
Geographic Region
Job Lost
Narcotics
Anabolic Steroids

Variable
V6
V11
V73
V92
V102
V103
V110
V111
V113
V117
V118
V133
V152
V167
V173
V225
duipveh
geogdiv
geogreg
joblostbin
narcotics
steroids

N
378
378
378
378
378
378
378
378
378
378
378
378
378
378
378
378
378
378
378
378
378
378

χ
5.917
85.253
7.734
6.255
6.748
3.712
8.850
5.972
5.060
6.210
5.782
4.051
4.051
4.051
4.051
7.734
4.929
25.595
18.376
6.222
4.051
4.051

df
1
46
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
8
3
1
1
1

p
.015
.000
.005
.012
.009
.054
.003
.015
.024
.013
.016
.044
.044
.044
.044
.005
.026
.001
.000
.013
.044
.044

V
.125
.475
.143
.129
.134
.099
.153
.126
.116
.128
.124
.104
.104
.104
.104
.143
.114
.260
.220
.128
.104
.104

307
Table 47. Police DUI Arrest Cases: Logistic Regression Model Predicting Conviction (N = 378)
95% CI for Exp(B)
B

Wald

Exp(B)

Liquor Law violation

-2.893

1.181

6.000

.014

0.055

0.005

0.561

Official Capacity

-0.949

0.363

6.842

.009

0.387

0.190

0.788

0.861

0.283

9.239

.002

2.366

1.358

4.123

DUI-related Traffic Accident
DUI Refused BAC Test

-0.775

0.308

6.313

.012

0.461

0.252

0.843

DUI Traffic Accident: Officer Denied Driving

-2.858

1.200

5.670

.017

0.057

0.005

0.603

0.892

0.310

8.283

.004

2.440

1.329

4.480

Job Loss
- 2 Log Likelihood

334.792

Model Chi-Square

41.842

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.105
.166

95% CI for AUC

.410

.705

UL
.639

.771

308
Table 48. Bivariate Associations of Job Loss in DUI Arrest Cases
Variable Label
Duty Status
Full-Time Sworn Personnel (categorical)
State
Part-Time Sworn Personnel (categorical)
Urban to Rural Continuum
Murder and Nonnegligent Manslaughter
Kidnapping / Abduction
Organizational vs. Against Citizenry
Internal Crime Against the Organization
Official Capacity
Violence-related
Officer was Reassigned
Discussion of Agency Scandal
Conviction
DUI-related Traffic Accident
Injuries in DUI-related Traffic Accident
DUI On-Duty in Police Vehicle
Off-Duty: Agency has 24/7 On-Call Policy
Off-Duty: In Police Uniform
DUI Traffic Accident: Driving in Wrong Direction
DUI Traffic Accident: Fatality
DUI in a Police Vehicle
Age (categorical)
Years of Service (categorical)
Metro vs. Non-Metro County
Geographic Region
Geographic Division
Victim Age (categorical)
Victim Age Difference

Variable
V6
V10
V11
V12
V13
V36
V39
V90
V91
V92
V96
V102
V107
V109
V110
V111
V112
V118
V120
V221
V222
DUIpveh
agecat
yrsservcat
codichot
geogreg
geogdiv
vicagecat
vicagediff

N
960
960
960
960
960
960
960
960
960
960
960
960
960
378
960
960
960
960
960
960
960
960
960
960
960
960
960
960
960

χ
6.657
29.056
107.344
24.362
31.714
6.222
4.346
4.555
7.764
17.856
14.195
5.850
9.773
6.222
6.759
9.618
6.912
8.080
4.346
16.158
12.230
10.788
32.467
33.388
9.812
14.578
38.273
21.306
80.423

df
1
10
50
7
8
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
3
1
10
10
1
3
8
8
54

p
.010
.001
.000
.001
.000
.013
.037
.033
.021
.000
.000
.016
.002
.013
.009
.002
.009
.004
.037
.000
.007
.001
.000
.000
.002
.002
.000
.006
.011

V
.083
.174
.334
.159
.182
.081
.067
.069
.090
.136
.122
.078
.101
.128
.084
.100
.085
.092
.067
.130
.113
.106
.184
.186
.101
.123
.200
.149
.289

309
Table 49. Police DUI Arrest Cases, 2005-2011: Logistic Regression Model Predicting Job Loss (N = 378)
95% CI for Exp(B)
B

Wald

Exp(B)

Full-Time Sworn Personnel (categorical)

-0.149

0.050

8.878

.003

0.862

0.781

0.950

Murder & Nonnegligent Manslaughter

1.254

0.634

3.909

.048

3.506

1.011

12.156

Official Capacity

1.076

0.332

10.525

.001

2.934

1.531

5.623

Violence-related

0.902

0.339

7.068

.008

2.465

1.268

4.794

Conviction

0.857

0.299

8.202

.004

2.357

1.311

4.238

-2.254

1.047

4.638

.031

0.105

0.013

0.817

Off-Duty: Employing Agency Requires 24/7 Availability
DUI Traffic Accident: Driving in Wrong Direction

2.149

1.081

3.951

.047

8.576

1.030

71.382

Years of Service (categorical)

0.043

0.021

4.278

.039

1.043

1.002

1.086

- 2 Log Likelihood

444.563

Model Chi-Square

69.892

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.169
.227

95% CI for AUC

.436

.718

UL
.666

.770

310
Table 50. Drug-related Police Crime Arrest Cases, 2005-2011: Arrested Officers and Employing Agencies (N = 739)
n
(%)
n
(%)
Sex
Male
Female

701
38

(94.9)
(5.1)

Age
19-23
24-27
28-31
32-35
36-39
40-43
44-47
48-51
52-55
56 or older
Missing

5
53
130
129
136
84
72
37
21
17
55

(0.7)
(7.2)
(17.6)
(17.5)
(18.4)
(11.4)
(9.7)
(5.0)
(2.8)
(2.3)
(7.4)

Years of Service
0-2
3-5
6-8
9-11
12-14
15-17
18-20
21-23
24-26
27 or more years
Missing

72
116
87
76
64
50
37
21
8
21
187

(9.7)
(15.7)
(11.8)
(10.3)
(8.7)
(6.8)
(5.0)
(2.8)
(1.1)
(2.8)
(25.3)

Arresting Agency
Employing Agency
Another Agency

212
527

(28.7)
(71.3)

Officer Duty Status
On-Duty
Off-Duty

444
295

(60.1)
(39.9)

Rank
Officer
Detective
Corporal
Sergeant
Lieutenant
Captain
Major
Colonel
Deputy Chief
Chief

559
70
7
45
22
5
1
0
3
27

(75.6)
(9.5)
(0.9)
(6.1)
(3.0)
(0.7)
(0.1)
(0.0)
(0.4)
(3.7)

Function
Patrol & Street Level
Line/Field Supervisor
Management

629
74
36

(85.1)
(10.0)
(4.9)

Region of United States
Northeastern States
Midwestern States
Southern States
Western States

154
145
346
94

(20.9)
(19.6)
(46.8)
(12.7)

Level of Rurality
Metropolitan County
Non-Metro County

611
128

(82.7)
(17.3)

(%)

Agency Type
Primary State Police
Sheriff's Office
County Police Dept.
Municipal Police Dept.
Special Police Dept.
Constable
Tribal Police Dept.
Regional Police Dept.

23
139
25
527
20
4
1
0

(3.1)
(18.8)
(3.4)
(71.3)
(2.7)
(0.5)
(0.2)
(0.0)

Full-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499
500-999
1,000 or more

1
8
24
33
73
84
84
58
80
58
236

(0.1)
(1.1)
(3.3)
(4.5)
(9.9)
(11.4)
(11.4)
(7.8)
(10.8)
(7.8)
(31.9)

Part-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499

551
28
47
48
49
11
4
1
0

(74.6)
(3.8)
(6.4)
(6.5)
(6.6)
(1.5)
(0.5)
(0.1)
(0.0)

311
Table 51. Most Serious Offense Charged in Drug-related Police Crime Arrest Cases, 2005-2011 (N = 739)
n
(%)
Drug / Narcotic violation
Robbery
Driving Under the Influence
All Other Larceny
Theft from a Building
Burglary / Breaking and Entering
All Other Offenses
Weapons Law violation
False Pretenses / Swindle
Extortion / Blackmail
Official Misconduct / Oppression /Violation of Oath
Aggravated Assault
Obstruction of Justice
False Statement / False Report
Embezzlement
Forcible Fondling
Civil Rights violation
Bribery
Counterfeiting / Forgery

308
60
38
33
28
26
24
22
19
15
15
14
13
11
10
10
10
9
9

(41.7)
(8.1)
(5.1)
(4.5)
(3.8)
(3.5)
(3.2)
(3.0)
(2.6)
(2.0)
(2.0)
(1.9)
(1.8)
(1.5)
(1.4)
(1.4)
(1.4)
(1.2)
(1.2)

Forcible Rape
Simple Assault
Intimidation
Stolen Property Offenses
Evidence: Destroying / Tampering
Arson
Murder and Nonnegligent Manslaughter
Kidnapping / Abduction
Impersonation
Theft from a Motor Vehicle
Destruction of Property / Vandalism
Sexual Assaul with an Object
Other Sex Crime
Statutory Rape
Online Solicitation of a Child
Disorderly Conduct
Hit & Run
Wiretapping, illegal

(%)

8
7
7
7
6
5
4
4
3
3
2
2
2
1
1
1
1
1

(1.1)
(0.9)
(0.9)
(0.9)
(0.8)
(0.7)
(0.6)
(0.6)
(0.4)
(0.4)
(0.3)
(0.3)
(0.3)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)

312
Table 52. Specific Drugs in Drug-related Police Crime Arrest Cases, 2005-2011 (N = 739)
n
(%)
Specific Drugs
Cocaine (Stimulant)
Marijuana (Cannabis)
Oxycodone (Narcotic)
Hydrocodone (Narcotic)
Crack (Stimulant)
Amphetamine / Methamphetamine (Stimulant)
Heroin (Narcotic)
Narcotics (other)
Anabolic Steroids (not testosterone)
Depressants (other)
Benzodiazapines (Depressant)
Testosterone (Anabolic Steroid)
MDMA & Analogs (Hallucinogen)
Morphine (Narcotic)
Phencyclidine & Analogs (Hallucinogen)
Hydromorphone (Narcotic)
Stimulants (other)
Hallucinogens (other)
Codeine (Narcotic)
Gamma Hydroxybutyric Acide (Depressant)

233
177
89
69
58
53
50
29
29
26
19
17
11
8
8
5
4
4
2
2

(31.5)
(24.0)
(12.0)
(9.3)
(7.8)
(7.2)
(6.8)
(3.9)
(3.9)
(3.5)
(2.6)
(2.3)
(1.5)
(1.1)
(1.1)
(0.7)
(0.5)
(0.5)
(0.3)
(0.3)

(%)

Drug Classes
Stimulants
Narcotics
Cannabis
Depressants
Anabolic Steroids
Hallucinogens

308
197
177
45
44
23

(41.7)
(26.7)
(24.0)
(6.1)
(6.0)
(3.1)

Cases with Multiple Types of Drug Classes Present
0 Drug Classes
1 Drug Class
2 Drug Classes
3 Drug Classes
4 Drug Classes

133
466
105
22
13

(18.0)
(63.1)
(14.2)
(3.0)
(1.8)

Cases with Multiple Specific Drugs Present
0 Specific Drugs
1 Specific Drug
2 Specific Drugs
3 Specific Drugs
4 Specific Drugs
5 Specific Drugs
6 Specific Drugs

131
415
136
35
10
11
1

(17.7)
(56.2)
(18.4)
(4.7)
(1.4)
(1.5)
(0.1)

Note . Categories are not mutually exclusive. Sum of (%) columns ≠ 100

313
Table 53. Victim Characteristics in Drug-related Police Crime Arrest Cases, 2005-2011 (N = 739)
n
(%) (Valid %)
Victim's Sex
Female
Male
Missing

52
57
630

(7.0)
(7.7)
(85.3)

(47.7)
(52.3)

Victim's Age
Birth-11
12-13
14-15
16-17
18-19
20-24
25-32
33-41
42 or older
Missing

1
4
7
1
4
8
5
3
5
701

(0.1)
(0.5)
(1.0)
(0.1)
(0.5)
(1.1)
(0.7)
(0.4)
(0.7)
(94.9)

(2.6)
(10.5)
(18.4)
(2.6)
(10.5)
(21.1)
(13.2)
(7.9)
(13.2)

(%)

(Valid %)

6
0
3
1
2
2
12
112
601

(0.8)
(0.0)
(0.4)
(0.1)
(0.3)
(0.3)
(1.6)
(15.2)
(81.3)

(4.4)
(0.0)
(2.2)
(0.7)
(1.4)
(1.4)
(8.7)
(81.2)

Victim's Law Enforcement Status
Victim is Not a Police Officer
Victim is a Police Officer
Missing

136
4
599

(18.4)
(0.5)
(81.1)

(97.1)
(2.9)

Victim Adult or Child
Adult
Child
Missing

129
14
596

(17.5)
(1.9)
(80.6)

(90.2)
(9.8)

314
Table 54. Patterns of Drug-related Corruption in Police Crime Arrest Cases, 2005-2011 (N = 739)
n
(%)
Drug Selling / Dealing / Trafficking
Drug Personal Use
Facilitation of the Drug Trade
Theft / Shakedown
Street-Level Dealer (n = 75, 10.1%)
Warrantless Searches (n = 69, 9.3%)
Car Stops & Drug Couriers (n = 58, 7.8%)
Off-Duty Robberies (n = 35, 4.7%)
Illegitimate Raids / Searches (n = 33, 4.5%)
Calls for Service / Radio Runs (n = 16, 2.2%)
Theft from Police Evidence Room
Falsification
Forged Prescription
Planting Evidence
Sexually-motivated Drug Corruption
Cases with Multiple Patterns of Drug Corruption
0 Drug Corruption Patterns Present
1 Drug Corruption Pattern Present
2 Drug Corruption Patterns Present
3 Drug Corruption Patterns Present
4 Drug Corruption Patterns Present
5 Drug Corruption Patterns Present
6 Drug Corruption Patterns Present
7 Drug Corruption Patterns Present

299
235
172
171

(40.5)
(31.8)
(23.3)
(23.1)

60
58
33
33
25

(8.1)
(7.8)
(4.5)
(4.5)
(3.4)

59
316
192
72
56
35
6
3

(8.0)
(42.8)
(26.0)
(9.7)
(7.6)
(4.7)
(0.8)
(0.4)

(%)

Other Types of Police Crime Present in Drug Cases
Profit-motivated Police Crime
Violence-related Police Crime
Alcohol-related Police Crime
Sex-related Police Crime

438
132
38
37

(59.3)
(17.9)
(5.1)
(5.0)

Drug Cases with Multiple Types of Police Crime
1 Type of Police Crime Present (just Drug-related)
2 Types of Police Crime Present
3 Types of Police Crime Present
4 Types of Police Crime Present

226
390
114
9

(30.6)
(52.8)
(15.4)
(1.2)

Note . Categories are not mutually exclusive. Sum of (%) column ≠ 100

315
Table 55. CART Analysis Patterns of Drug-related Police Corruption (N = 739)
95% CI
Splitting Variabl

Node 1 Variable(s)

Node 2 Variable(s)

AUC

Cocaine
Marijuana
Cocaine
Hydrocodone
Cocaine
Cocaine

Heroin/Marijuana
Cocaine
Hydrocodone/Marijuana
Oxycodone
Heroin/Marjiuana
Heroin/Marjiuana

Crack
Heroin/Crack
Amphetamine/Crack
Testosterone
Testosterone
Marijuana

.774
.769
.792
.701
.685
.737

.705
.714
.740
.660
.646
.697

.843
.824
.844
.724
.725
.777

Stimulants
Cannabis
Cannabis
Stimulants
Narcotics
Stimulants

Cannabis/Depressants
Narcotics/Stimulants
Narcotics/Stimulants
Cannabis/Anabolic Steroids*
Stimulants
Cannabis

Narcotics/Hallucinogens
Anabolic Steroids/Narcotics/Hallucinogens
Anabolic Steroids/Hallucinogens
Hallucinogens/Narcotics
Anabolic Steroids/Hallucinogens
Depressants

.753
.753
.755
.696
.675
.654

.685
.695
.697
.658
.632
.608

.821
.812
.814
.734
.717
.699

Specific Drugs
Theft/Shakedown - Car Stops & Drug Couriers
Theft/Shakedown - Warrantless Search/Seizures
Theft/Shakedown - Street-Level Dealer
Drug Use
Drug Trafficking
Facilitation of the Drug Trade
Drug Classes
Theft/Shakedown - Car Stops & Drug Couriers
Theft/Shakedown - Warrantless Search/Seizures
Theft/Shakedown - Street-Level Dealer
Drug Trafficking
Drug Use
Facilitation of the Drug Trade
*Anabolic steroids other than testosterone

316
Table 56. Bivariate Associations of Conviction in Drug-related Police Crime Arrest Cases, 2005-2011
Variable Label
State
Arresting Agency
Burglary / Breaking and Entering
Drug / Narcotic Violation
Extortion / Blackmail
Gambling: Betting / Wagering
Weapons Law Violation
Obstruction of Justice
Victim Relationship to the Arrested Officer
Profit-motivated
Police Sexual Violence
Arrested Officer was Reassigned
Officer was Suspended
DUI: Refused BAC Test
Family Violence
Other Depressants
Cocaine
OIDV: Weapon: Hands Fists
OIDV: Weapon: Other Body Parts
OIDV: Victim Injured, Nonfatal
Drugs: Personal Use / Using Drugs
Drugs: Selling / Dealing / Trafficking
Drugs: Shakedown from Car Stops
Job Lost binary
Drugs: Stimulants
Metropolitan vs. NonMetro County
Victim Age Categorical
Victim Age Difference

Variable
V11
V14
V20
V23
V26
V32
V63
V74
V85
V97
V98
V102
V104
V117
V126
V138
V139
V164
V165
V173
V196
V197
V206
joblossb
stimulant
codichot
vicagecat
vicagediff

N
562
562
562
562
562
562
562
562
129
562
562
562
562
562
562
562
562
562
562
562
562
562
562
562
562
562
562
562

χ
65.220
13.219
4.328
5.962
3.870
6.310
4.694
5.436
15.398
15.513
5.024
9.568
17.595
7.156
9.044
4.289
8.535
9.146
4.489
6.758
3.762
15.265
4.878
12.267
9.442
5.843
47.832
63.847

df
43
1
1
1
1
1
1
1
6
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
25

p
.016
.000
.037
.015
.049
.012
.030
.020
.017
.000
.025
.002
.000
.007
.003
.038
.003
.002
.034
.009
.052
.000
.027
.000
.002
.016
.000
.000

V
.341
.153
.088
.103
.083
.106
.091
.098
.345
.166
.095
.130
.177
.113
.127
.087
.123
.128
.089
.110
.082
.165
.093
.148
.130
.102
.292
.337

317
Table 57. Drug-related Police Crime Arrest Cases: Logistic Regression Model Predicting Conviction (N = 129)
95% CI for Exp(B)
B
Obstruction of Justice
Victim Relationship to Arrested Officer

Wald

Exp(B)

-1.703

0.751

5.141

.023

0.182

0.042

0.794

0.471

0.171

7.545

.006

1.601

1.144

2.240

-2.061

0.699

8.687

.003

0.127

0.032

0.501

1.521

0.606

6.302

.012

4.575

1.396

14.996

Victim Age Categorical

-0.096

0.040

5.795

.016

0.909

0.840

0.982

- 2 Log Likelihood

86.361

Model Chi-Square

34.589

Suspended from Job
Lost Job

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.235
.387

95% CI for AUC

.698

.849

UL
.761

.937

318
Table 58. Bivariate Associations of Job Loss in Drug-related Police Crime Arrest Cases, 2005-2011
Variable Label
Year of Arrest
Age
Duty Status
Full-Time Sworn Personnel
Arresting Agency
Counterfeiting / Forgery
Drug / Narcotic Violation
Theft from a Building
Theft / All Other Larceny
Pornography / Obscene Material
Weapons Law Violation
Driving Under the Influence
Drunkenness
Trespass of Real Property
Official Capacity
Alcohol-related
Profit-motivated
Reassigned
Suspended
Supervisor was Disciplined
Chief is Under Scrutiny
Officer was Convicted
DUI-related Traffic Accident
DUI On-Duty in a Pollice Vehicle
DUI in Police Vehicle Outside Jurisdiction
Off-Duty: Identified Self as an Officer
DUI in Possession of a Firearm
Heroin
Other Hullucinogens
28 USC §1441 Civil Action Removed
18 USC §242 Criminal Deprivation of Rights
Drugs: Selling / Dealing / Trafficking
Drugs: Forged Prescription
Drugs: faciltating Drug Trade
Drugs: Shakedowns from Car Stops
Drugs: Theft from Evidence Room
Drugs: Falsification
Drugs: Narcotics
DUI in a Police Vehicle
Metropolitan County vs. Nonmetro County
Victim Age Difference

Variable
V2
V3
V6
V10
V14
V21
V23
V43
V47
V49
V63
V67
V68
V78
V92
V94
V97
V102
V104
V105
V106
V109
V110
V112
V114
V119
V128
V129
V147
V179
V181
V197
V198
V201
V206
V208
V209
Narcotic
DUI in Pol
CoDichot
VicAgeDif

N
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739
739

χ
56.885
62.818
12.598
26.219
7.304
4.142
4.854
4.684
4.227
16.780
13.652
19.044
7.152
7.152
18.828
21.590
13.973
20.894
25.124
11.138
8.228
12.267
4.555
8.471
7.152
4.533
4.818
7.998
3.969
5.019
4.709
7.430
15.890
10.467
4.636
10.110
4.163
6.864
7.843
4.470
39.386

df
6
45
1
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
26

p
.000
.041
.000
.003
.007
.042
.028
.030
.040
.000
.000
.000
.007
.007
.000
.000
.000
.000
.000
.001
.004
.000
.033
.004
.007
.033
.028
.005
.046
.025
.030
.006
.000
.001
.031
.001
.041
.009
.005
.034
.045

V
.277
.303
.131
.188
.099
.075
.081
.080
.076
.151
.136
.161
.090
.098
.160
.171
.138
.168
.184
.123
.106
.148
.079
.107
.098
.078
.081
.104
.073
.082
.080
.100
.147
.119
.079
.117
.075
.096
.103
.078
.231

319
Table 59. Drug-related Police Crime Arrest Cases, 2005-2011: Logistic Regression Model Predicting Job Loss (N = 535)
95% CI for Exp(B)
B

Wald

Exp(B)

Year of Officer's Arrest

0.544

0.075

52.053

< .001

1.723

1.486

1.997

Counterfeiting / Forgery

-1.718

0.818

4.405

.036

0.179

0.036

0.893

Alcohol-related Police Crime

-1.341

0.455

8.668

.003

0.262

0.107

0.639

Officer's Chief is Under Scrutiny as a Result of Arrest

-0.989

0.413

5.738

.017

0.372

0.166

0.836

Conviction on Any Offense Charged
28 U.S.C. §1441 Civil Rights Case Removed

0.917

0.314

8.538

.003

2.503

1.353

4.631

1.895

0.532

12.678

< .001

6.654

2.344

18.886

Falsification in a Drug-related Criminal Case

-1.183

0.405

8.521

.004

0.306

0.138

0.678

Narcotics

1.452

0.390

13.882

< .001

4.272

1.990

9.169

Non-Metropolitan County

0.946

0.397

5.694

.017

2.576

1.184

5.603

- 2 Log Likelihood

440.566

Model Chi-Square

131.461

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.218
.332

95% CI for AUC

.634

.817

UL
.776

.859

320
Table 60. Violence-related Police Crime Arrest Cases, 2005-2011: Arrested Officers and Employing Agencies (N = 3,328)
n
(%)
n
(%)
Sex
Male
Female
Age
19-23
24-27
28-31
32-35
36-39
40-43
44-47
48-51
52-55
56 or older
Missing
Years of Service
0-2
3-5
6-8
9-11
12-14
15-17
18-20
21-23
24-26
27 or more years
Missing
Arresting Agency
Employing Agency
Another Agency

3,194
134

(96.0)
(4.0)

74
305
489
513
542
441
288
149
75
67
385

(2.2)
(9.2)
(14.7)
(15.4)
(16.3)
(13.3)
(8.7)
(4.5)
(2.3)
(2.0)
(11.6)

398
514
350
296
262
193
174
68
57
60
956

(12.0)
(15.4)
(10.5)
(8.9)
(7.9)
(5.8)
(5.2)
(2.0)
(1.7)
(1.8)
(28.7)

1,228
2,100

(36.9)
(63.1)

Officer Duty Status
On-Duty
Off-Duty

1,173
2,155

(35.2)
(64.8)

Rank
Officer
Detective
Corporal
Sergeant
Lieutenant
Captain
Major
Colonel
Deputy Chief
Chief

2,656
141
64
269
69
21
3
0
16
89

(79.8)
(4.2)
(1.9)
(8.1)
(2.1)
(0.6)
(0,1)
(0.0)
(0.5)
(2.7)

Function
Patrol & Street Level
Line/Field Supervisor
Management

2,797
402
129

(84.0)
(12.1)
(3.9)

Region of United States
Northeastern States
Midwestern States
Southern States
Western States

715
664
1,386
563

(21.5)
(20.0)
(41.6)
(16.9)

Level of Rurality
Metropolitan County
Non-Metro County

2,880
448

(86.5)
(13.5)

(%)

118
523
90
2,504
77
6
9
1

(3.5)
(15.7)
(2.7)
(75.2)
(2.3)
(0.2)
(0.3)
(0.0)

Full-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499
500-999
1,000 or more

24
31
130
190
311
338
317
399
329
272
987

(0.7)
(0.9)
(3.9)
(5.7)
(9.3)
(10.2)
(9.5)
(12.0)
(9.9)
(8.2)
(29.7)

Part-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499

2,489
90
282
207
165
63
26
5
1

(74.8)
(2.7)
(8.5)
(6.2)
(5.0)
(1.9)
(0.8)
(0.2)
(0.0)

Agency Type
Primary State Police
Sheriff's Office
County Police Dept.
Municipal Police Dept.
Special Police Dept.
Constable
Tribal Police Dept.
Regional Police Dept.

321
Table 61. Most Serious Offense Charged in Violence-related Police Crime Arrest Cases, 2005-2011 (N = 3,328)
n
(%)
Simple Assault
Aggravated Assault
Forcible Fondling
Forcible Rape
Intimidation
Murder / Nonnegligent Manslaughter
Unclassified / All Other Offenses
Forcible Sodomy
Robbery
Civil Rights violation (criminal)
Weapons Law violation
Disorderly Conduct
Unclassified / Other Sex Crime
Negligent Manslaughter
Burglary / Breaking & Entering
Official Misconduct / Official Oppression
Arson
Driving Under the Influence
Kidnapping / Abduction
Destruction of Property / Vandalism

870
570
352
322
200
104
99
94
92
61
57
55
47
43
38
38
32
32
30
24

(26.4)
(17.1)
(10.6)
(9.7)
(6.0)
(3.1)
(3.0)
(2.8)
(2.8)
(1.8)
(1.7)
(1.7)
(1.4)
(1.3)
(1.1)
(1.1)
(1.0)
(1.0)
(0.9)
(0.7)

Statutory Rape
Obstruction of Justice
Restraining Order violation
False Report / False Statement
Bribery
Vehicular Hit & Run
Sexual Assault with an Object
Drug / Narcotic violation
Unclassified / All Other Larceny
Extortion / Blackmail
False Pretenses / Swindle
Indecent Exposure
Family Offenses, nonviolent
Pornography / Obscene Material
Prostitution
Drunkennness
Trespass of Real Property
Incest
Impersonation
Online Solicitation of a Child

(%)

24
18
17
14
12
12
10
9
8
7
5
5
4
3
3
3
3
2
1
1

(0.7)
(0.5)
(0.5)
(0.4)
(0.4)
(0.4)
(0.3)
(0.3)
(0.2)
(0.2)
(0.2)
(0.2)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.0)
(0.0)

322
Table 62. Victim Characteristics in Violence-related Police Crime Arrest Cases, 2005-2011 (N = 3,328)
n
(%) (Valid %)
Victim's Sex
Female
Male
Missing

1,841
1,124
363

(55.3)
(33.8)
(10.9)

(62.1)
(37.9)

Victim's Age
Birth-11
12-13
14-15
16-17
18-19
20-24
25-32
33-41
42 or older
Missing

163
112
174
134
96
180
252
187
165
1,865

(4.9)
(3.4)
(5.2)
(4.0)
(2.9)
(5.4)
(7.6)
(5.6)
(5.0)
(56.0)

(11.1)
(7.7)
(11.9)
(9.1)
(6.6)
(12.3)
(17.2)
(12.8)
(11.3)

(%)

(Valid %)

336
59
195
118
159
84
451
1,668
258

(10.1)
(1.8)
(5.9)
(3.5)
(4.8)
(2.5)
(13.6)
(50.1)
(7.8)

(10.9)
(1.9)
(6.4)
(3.8)
(5.2)
(2.7)
(14.7)
(54.3)

Victim's Law Enforcement Status
Victim is Not a Police Officer
Victim is a Police Officer
Missing

2,912
194
3,106

(87.5)
(5.8)
(93.3)

(93.8)
(6.2)

Victim Adult or Child
Adult
Child
Missing

2,416
676
3,092

(72.6)
(20.3)
(7.1)

(78.1)
(21.9)

323
Table 63. Bivariate Associations of Conviction in Violence-related Arrest Cases
Variable Label
Year of Arrest
Gender
Rank
Full-Time Sworn Personnel (categorical)
State
Part-Time Sworn Personnel (categorical)
Aggravated Assault
Simple Assault
Bribery
Burglary
Drug / Narcotic violation
Extortion / Blackmail
Murder & Nonnegligent Manslaughter
Kidnapping / Abduction
Pornography / Obscene Material
Robbery
Forcible Rape
Forcible Sodomy
Forcible Fondling
Statutory Rape
Stolen Property Offenses
Weapons Law violation
Criminal Deprivation of Civil Rights
Driving Under the Influence
Family Offenses, nonviolent
Gender of Victim
Victim Age
Child Victim
Victim's Relationship to Arrested Officer
Internal Crime Against the Organization
Drug-related
Alcohol-related
Sex-related
Profit-motivated

Variable
V2
V5
V7
V10
V11
V12
V16
V17
V19
V20
V23
V26
V36
V39
V49
V52
V53
V54
V56
V58
V62
V63
V65
V67
V71
V81
V83
V84
V85
V91
V93
V94
V95
V97

N
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,798
962
1,864
1,855
1,984
1,984
1,984
1,984
1,984

χ2
28.951
3.830
21.647
31.265
98.550
15.057
11.233
69.186
8.886
7.994
14.321
13.069
5.960
3.587
13.069
20.997
16.238
23.453
36.773
5.342
4.077
6.206
6.702
9.695
7.876
6.885
96.014
23.214
77.089
14.666
20.845
4.524
85.756
33.007

df
6
1
8
10
49
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
73
1
7
2
1
1
1
1

p
.000
.050
.006
.001
.000
.035
.001
.000
.003
.005
.000
.000
.015
.058
.000
.000
.000
.000
.000
.021
.043
.013
.010
.002
.005
.009
.037
.000
.000
.001
.000
.033
.000
.000

V
.121
.044
.104
.126
.223
.087
.075
.187
.067
.063
.085
.081
.055
.043
.081
.103
.090
.109
.136
.052
.045
.056
.058
.070
.063
.062
.316
.112
.204
.086
.103
.048
.208
.129

324
Police Sexual Violence
Driving While Female Encounter
Suspension
DUI-related Traffic Accident
Injuries in a DUI-related Traffic Accident
DUI in a Privately-owned Vehicle
DUI Refused BAC Test
Off-Duty: Agency has 24/7 On-Call Policy
Off-Duty: Intervened in Existing Dispute
Family Violence
Hydrocodone
Cocaine
Marijuana
OIDV: Weapon: Hands / Fist
OIDV: Weapon: Miscellaneous Objects
OIDV: Weapon: Verbal Threats
OIDV: Confiscated Gun was Returned
OIDV: Protection Order Filed
OIDV: Victim Injured, nonfatal
OIDV: Victim Injured, fatal
42 U.S.C. §1981 Civil Defendant
42 U.S.C. §1997 Civil / Plaintiff is Prisoner
Drugs: Selling / Dealing / Trafficking
Drugs: Sexually-motivated Drug Crime
Drugs: Facilitating Drug Trade
Drugs: Shakedown from Street Dealers
Drugs: Shakedown from Warrantless Search
Drugs: Shakedown from Car Stops
Drugs: Shakedown from Off-Duty Robbery
Drugs: Theft from Police Evidence Room
Drugs: Shakedowns (aggregate)
Job Lost
Narcotics
Stimulants
Cannabis
Age (categorical)

V98
V99
V104
V110
V111
V115
V117
V118
V125
V126
V131
V139
V148
V164
V166
V167
V169
V171
V173
V174
V175
V178
V197
V199
V201
V202
V204
V206
V207
V208
V210
joblostbin
narcotics
stimulants
cannabis
agecat

1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984
1,984

38.540
11.554
9.249
8.644
7.891
8.452
4.589
4.485
3.944
38.595
4.077
21.445
16.049
50.787
3.739
4.901
10.465
4.372
55.748
7.641
3.673
4.268
17.235
6.931
6.642
16.055
14.022
18.611
9.980
3.944
25.915
254.985
4.935
21.792
16.049
33.356

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10

.000
.001
.002
.003
.005
.004
.032
.034
.047
.000
.043
.000
.000
.000
.053
.027
.001
.037
.000
.006
.055
.039
.000
.008
.010
.000
.000
.000
.002
.047
.000
.000
.026
.000
.000
.000

.139
.076
.068
.066
.063
.065
.048
.048
.045
.139
.045
.104
.090
.160
.043
.050
.073
.047
.168
.062
.043
.046
.093
.059
.058
.090
.084
.097
.071
.045
.114
.358
.050
.105
.090
.130

325
Years of Service (categorical)
Geographic Region
Geographic Division
Rank Function
Victim Age (categorical)

yrservcat
geogreg
geogdiv
rankfunc
vicagecat

1,984
1,984
1,984
1,984
1,984

40.562
8.678
20.397
9.786
27.714

10
3
8
2
9

.000
.034
.009
.007
.001

.143
.066
.101
.070
.118

326
Table 64. Violence-related Police Crime Arrest Cases: Logistic Regression Model Predicting Conviction (N = 954)
95% CI for Exp(B)
B
Full-Time Sworn Personnel (categorical)

Wald

Exp(B)

-0.086

0.031

7.915

.005

0.917

0.864

0.974

Burglary / Breaking & Entering

2.526

1.097

5.307

.021

12.506

1.458

107.281

Pornography / Obscene Material

2.206

1.048

4.433

.035

9.079

1.165

70.781

Forcible Sodomy

0.997

0.422

5.592

.018

2.711

1.186

6.197

Criminal Deprivation of Civil Rights

1.370

0.469

8.543

.003

3.934

1.570

9.858

Sex-related

0.626

0.181

.001

1.870

1.310

2.668

DUI in a Personally-Owned Vehichle

2.560

1.071

5.713

.017

12.936

1.585

105.554

OIDV Victim Injured, fatal

1.995

0.650

9.432

.002

7.351

2.058

26.257

Job Loss

1.726

0.165

108.819

< .001

5.619

4.063

7.772

Years of Service (categorical)

0.047

0.014

11.794

.001

1.049

1.021

1.077

- 2 Log Likelihood

948.456

Model Chi-Square

256.077

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.235
.328

95% CI for AUC

.482

.741

UL
.718

.764

327
Table 65. Bivariate Associations of Job Loss in Violence-related Arrest Cases
Variable Label
Year of Arrest
Age
Years of Service
Gender
Duty Status
Rank
Type of Agency
Full-Time Sworn Personnel (categorical)
State
Part-Time Sworn Personnel (categorical)
Urban to Rural Continuum
Aggravated Assault
Simple Assault
Bribery
Burglary
Drug / Narcotic violation
Impersonation
Kidnapping / Abduction
Theft from a Building
Pornography / Obscene Material
Robbery
Forcible Rape
Forcible Sodomy
Sexual Assault with an Object
Forcible Fondling
Statutory Rape
On-Line Solicitation of a Child
Unclassified Sex Crimes
Stolen Property Offenses
Criminal Deprivation of Civil Rights
Disorderly Conduct
Evidence: Destroying / Tampering
Official Misconduct
Victim Gender

Variable
V2
V3
V4
V5
V6
V7
V9
V10
V11
V12
V13
V16
V17
V19
V20
V23
V29
V39
V43
V49
V52
V53
V54
V55
V56
V58
V60
V61
V62
V65
V66
V69
V75
V81

N
3,328
2,943
2,372
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
2,965

χ2
146.477
67.607
55.941
8.369
91.844
18.818
25.826
55.100
141.569
39.679
42.904
10.964
128.940
35.502
4.699
14.424
4.526
25.636
5.433
7.899
19.475
62.366
17.201
7.626
137.226
13.894
4.786
54.740
7.249
20.843
34.355
4.641
21.145
12.413

df
6
50
38
1
1
8
7
10
49
8
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.000
.049
.030
.004
.000
.016
.001
.000
.000
.000
.000
.001
.000
.000
.030
.000
.033
.000
.020
.005
.000
.000
.000
.006
.000
.000
.029
.000
.007
.000
.000
.031
.000
.000

V
.210
.152
.154
.050
.166
.075
.088
.129
.206
.109
.114
.057
.197
.103
.038
.066
.037
.088
.040
.049
.076
.137
.072
.048
.203
.065
.038
.128
.047
.079
.102
.037
.080
.065

328
Victim is a Police Officer
Victim Age
Child Victim
Victim's Relationship to Arrested Officer
Organizational vs. Against Citizenry
Internal Crime Against the Organization
Official Capacity
Drug-related
Sex-related
Profit-motivated
Police Sexual Violence
Driving While Female Encounter
Officer was Reassigned
Officer was Demoted
Officer was Suspended
Officer's Supervisor was Disciplined
Officer's Chief is Under Scrutiny
Crime Detected by Citizen Complaint
Conviction
DUI: Officer Resisted Arrest
Off-Duty: Showed a Police Weapon
Family Violence
Heroin
Hydrocodone
Oxycodone
Cocaine
Marijuana
OIDV: Weapon: Hands / Fist
OIDV: Weapon: Other Body Parts
OIDV: Weapon: Misc. Objects
OIDV: Gun was Confiscated
OIDV: Confiscated Gun was Later Returned
OIDV: Protection Order Filed
OIDV: Victim Injured, nonfatal
OIDV: Victim Injured, fatal
42 U.S.C. §1981 Civil Defendant

V82
V83
V84
V85
V90
V91
V92
V93
V95
V97
V98
V99
V102
V103
V104
V105
V106
V108
V109
V127
V121
V126
V129
V131
V133
V139
V148
V164
V165
V166
V168
V169
V171
V173
V174
V175

3,106
1,463
3,092
3,070
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
1,984
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328

3.870
100.641
38.888
153.358
7.809
18.888
83.335
9.875
284.771
21.423
151.504
71.090
46.978
6.491
58.545
9.467
15.225
7.273
254.985
3.808
12.422
122.943
5.433
7.249
6.521
3.944
7.722
54.925
4.143
12.252
7.830
5.264
4.365
32.927
4.269
8.082

1
76
1
7
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

.049
.031
.000
.000
.005
.000
.000
.002
.000
.000
.000
.000
.000
.011
.000
.002
.000
.007
.000
.051
.000
.000
.020
.007
.011
.047
.005
.000
.042
.000
.005
.022
.037
.000
.039
.004

.035
.262
.112
.224
.048
.075
.158
.054
.293
.080
.213
.146
.119
.044
.133
.053
.068
.047
.358
.034
.061
.192
.040
.047
.044
.034
.048
.128
.035
.061
.049
.040
.036
.099
.036
.049

329
42 U.S.C. §1983 Civil Defendant
42 U.S.C. §1997 Civil Plaintiff is a Prisoner
28 U.S.C. §1441 Civil Case Removed
18 U.S.C. §242 Criminal Defendant
Drugs: Using / Personal Use
Drugs: Selling / Dealing / Trafficking
Drugs: Shakedown from Car Stops
Drugs: Shakedown from Off-Duty Robbery
Drugs: Shakedown (aggregate)
Narcotics
Depressants
Stimulants
Cannabis
Age (categorical)
Years of Service (categorical)
Metro versus Non-Metro County
Geographic Region
Geographic Division
Victim Age (categorical)
Victim Age Difference

V176
V178
V179
V181
V196
V197
V206
V207
V210
narcotic
depress
stimulant
cannabis
agecat
yrservcat
codichot
geogreg
geogdiv
vicagecat
vicagediff

3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328
3,328

15.066
5.053
28.434
46.552
9.336
17.818
9.421
8.384
5.489
11.855
4.526
6.935
7.722
91.126
90.546
13.177
48.162
53.265
49.257
140.586

1
1
1
1
1
1
1
1
1
1
1
1
1
10
10
1
3
8
9
99

.000
.025
.000
.000
.002
.000
.002
.004
.019
.001
.033
.008
.005
.000
.000
.000
.000
.000
.000
.004

.067
.039
.092
.118
.053
.073
.053
.050
.041
.060
.037
.046
.048
.165
.165
.063
.120
.127
.122
.206

330
Table 66. Violence-related Police Crime Arrest Cases: Logistic Regression Model Predicting Job Loss (N = 692)
95% CI for Exp(B)
B

Wald

Exp(B)

Year of Arrest

0.411

0.059

49.043

< .001

1.509

1.345

1.693

Victim Gender

-0.572

0.260

4.858

.028

0.564

0.339

0.939

0.219

0.062

12.561

< .001

1.244

1.103

1.404

Victim's Relationship to the Arrested Officer
Official Capacity

-0.672

0.253

7.029

.008

0.511

0.311

0.839

1.352

0.351

14.843

< .001

3.864

1.943

7.685

Officer was Reassigned to Another Position

-1.224

0.503

5.930

.015

0.294

0.110

0.787

Officer was Suspended

-1.462

0.291

25.230

< .001

0.232

0.131

0.410

1.943

0.219

78.621

< .001

6.978

4.542

10.721

Police Sexual Violence

Conviction
28 U.S.C. §1441 Civil Case Removed to Federal Court

1.215

0.383

10.079

.001

3.370

1.592

7.136

Cannabis

-2.841

0.852

11.105

.001

0.058

0.011

0.310

Age (categorical)

-0.105

0.052

4.046

.044

0.900

0.813

0.997

- 2 Log Likelihood

617.555

Model Chi-Square

250.874

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.304
.425

95% CI for AUC

.536

.768

UL
.744

.791

331
Table 67. Bivariate Associations of Conviction in Officer-involved Domestic Violence Arrest Cases
Variable Label
Year of Arrest
State
Simple Assault
Burglary
Property Destruction / Vandalism
Murder and Nonnegligent Manslaughter
Pornogaphy / Obscene Material
Forcible Rape
Forcible Sodomy
Forcible Fondling
Obstruction of Justice
Official Misconduct
Child Victim
Victim's Relationship to Arrested Officer
Sex-related
OIDV: Weapon: Personally-owned Gun
OIDV: Weapon: Hands / Fist
OIDV: Weapon: Other Body Parts
OIDV: Confiscated Gun was Returned
OIDV: Officer Violated Protection Order
OIDV: Victim Injured, nonfatal
OIDV: Victim Injured, fatal
Lost Job
Geographic Region
Geographic Division
Victim Age (categorical)

Variable
V2
V11
V17
V20
V22
V36
V49
V53
V54
V56
V74
V75
V84
V85
V95
V157
V164
V165
V169
V172
V173
V174
joblost
geogreg
geogdiv
vicagecat

N
497
497
497
497
497
497
497
497
497
497
497
497
486
480
497
497
497
497
497
497
497
497
497
497
497
497

χ
15.413
80.273
29.509
3.927
9.992
14.147
5.826
11.871
4.626
24.159
7.533
7.521
13.968
27.127
34.222
7.379
14.263
14.351
5.982
4.603
17.631
15.862
62.928
19.175
24.938
28.296

df
6
46
1
1
1
1
1
1
1
1
1
1
1
7
1
1
1
1
1
1
1
1
1
3
8
9

p
.017
.001
.000
.048
.002
.000
.016
.001
.031
.000
.006
.006
.000
.000
.000
.007
.000
.000
.014
.032
.000
.000
.000
.000
.002
.001

V
.176
.402
.244
.089
.142
.169
.108
.155
.096
.220
.123
.123
.170
.238
.262
.122
.169
.170
.110
.096
.188
.179
.356
.196
.224
.239

332
Table 68. Officer-involved Domestic Violence Arrest Cases: Logistic Regression Model Predicting Conviction (N = 480)
95% CI for Exp(B)
B

Wald

Exp(B)

Destruction of Property / Vandalism

2.176

0.682

10.178

.001

8.807

2.314

33.516

Obstruction of Justice

2.950

1.106

7.118

.008

19.115

2.188

167.003

Victim's Relationship to the Arrested Officer

0.133

0.058

5.141

.023

1.142

1.018

1.280

Sex-related

1.526

0.326

21.909

< .001

4.602

2.428

8.720

OIDV: Weapon: Personally-owned Gun

0.809

0.401

4.074

.044

2.246

1.024

4.927

OIDV: Officer Violated an Order of Protection

1.547

0.599

6.663

.010

4.699

1.451

15.213

OIDV: Victim Injured, nonfatal
OIDV: Victim Injured, fatal
Job Loss
Geographic Region

-0.587

0.228

6.620

.010

0.556

0.356

0.870

2.048

0.660

9.622

.002

7.752

2.125

28.278

1.385

0.226

37.730

< .001

3.996

2.568

6.216

-0.287

0.110

6.764

.009

0.751

0.605

0.932

- 2 Log Likelihood

497.342

Model Chi-Square

163.272

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.288
.386

95% CI for AUC

.640

.820

UL
.782

.858

333
Table 69. Bivariate Associations of Job Loss in Officer-involved Domestic Violence Arrest Cases
Variable Label
Year of Arrest
Duty Status
Full-Time Sworn Personnel (categorical)
State
Part-Time Sworn Personnel (categorical)
Urban to Rural County Continuum
Simple Assault
Murder & Nonnegligent Manslaughter
Kidnapping / Abduction
Pornography / Obscene Material
Forcible Rape
Forcible Fondling
Indecent Exposure
Unclassified Sex Crime
Stolen Property Offenses
Official Misconduct
Unclassified / All Other Offenses
Child Victim
Victim's Relationship to Arrested Officer
Official Capacity
Drug-related
Sex-related
Police Sexual Violence
Officer was Reassigned
Officer was Suspended
Officer was Convicted
Unclassified / Other Depressants
OIDV: Weapon: Other Body Parts
OIDV: Weapon: Verbal Threats
OIDV: Officer Violated Protection Order
OIDV: Victim Injured, fatal
28 U.S.C. §1441 Civil Case Removed
Drugs: Sexually-motivated Drug Crime
Depressants

Variable
V2
V6
V10
V11
V12
V13
V17
V36
V39
V49
V53
V56
V59
V61
V62
V75
V80
V84
V85
V92
V93
V95
V98
V102
V104
V109
V138
V165
V167
V172
V173
V179
V199
depress

N
961
961
961
961
961
961
961
961
961
961
961
961
961
961
961
961
961
929
910
961
961
961
961
961
961
497
961
961
961
961
961
961
961
961

χ2
41.162
4.876
31.600
81.166
15.748
19.372
25.197
12.668
8.129
7.008
22.061
39.122
4.567
10.256
6.706
7.309
11.681
18.080
26.984
4.583
5.677
51.384
6.706
11.172
14.446
62.928
5.024
25.743
5.311
4.389
15.351
4.908
5.024
5.024

df
6
1
10
49
8
8
1
1
1
1
1
1
1
1
1
1
1
1
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.000
.027
.000
.003
.046
.013
.000
.000
.004
.008
.000
.000
.033
.001
.010
.007
.001
.000
.000
.032
.017
.000
.010
.001
.000
.000
.025
.000
.021
.036
.000
.027
.025
.025

V
.207
.071
.181
.291
.128
.142
.162
.115
.092
.085
.152
.202
.069
.103
.084
.087
.110
.140
.172
.069
.077
.231
.084
.108
.123
.356
.072
.164
.074
.068
.126
.071
.072
.072

334
Age (categorical)
Metro vs. Non-Metro County
Geographic Region
Geographic Division
Victim Age (categorical)
Victim Age Difference

agecat
codichot
geogreg
geogdiv
vicagecat
vicagediff

961
961
961
961
961
961

31.396
7.658
12.289
30.704
38.094
85.555

10
1
3
8
9
65

.000
.006
.006
.000
.000
.045

.181
.089
.113
.179
.199
.298

335
Table 70. Officer-involved Domestic Violence Arrest Cases: Logistic Regression Model Predicting Job Loss (N = 486)
95% CI for Exp(B)
B

Wald

Exp(B)

Year of Arrest

0.420

0.067

38.770

< .001

1.521

1.333

1.736

Duty Status

1.848

0.714

6.704

.010

6.347

1.567

25.710

-0.018

0.007

5.742

.017

0.982

0.968

0.997

State
Rurality Continuum (categorical)

0.248

0.089

7.826

.005

1.282

1.077

1.525

Simple Assault

-0.711

0.228

9.712

.002

0.491

0.314

0.768

Suspended

-0.673

0.254

7.006

.008

0.510

0.310

0.840

Conviction

1.689

0.231

53.587

< .001

5.414

3.445

8.510

OIDV: Weapon: Other Body Parts (not hands or fist)

0.946

0.352

7.215

.007

2.576

1.291

5.138

28 U.S.C. §1441 Civil Case Removed to Federal Court

1.150

0.432

7.101

.008

3.159

1.356

7.360

- 2 Log Likelihood

525.053

Model Chi-Square

146.577

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.260
.348

95% CI for AUC

.600

.800

UL
.762

.839

336
Table 71. Profit-motivated Police Crime Arrest Cases, 2005-2011: Arrested Officers and Employing Agencies (N = 1,591)
n
(%)
n
(%)
Sex
Male
Female
Age
19-23
24-27
28-31
32-35
36-39
40-43
44-47
48-51
52-55
56 or older
Missing
Years of Service
0-2
3-5
6-8
9-11
12-14
15-17
18-20
21-23
24-26
27 or more years
Missing
Arresting Agency
Employing Agency
Another Agency

1,497
94

(94.1)
(5.9)

24
137
207
233
244
231
144
90
48
62
171

(1.5)
(8.6)
(13.0)
(14.6)
(15.3)
(14.5)
(9.1)
(5.7)
(3.0)
(3.9)
(10.8)

148
197
162
155
126
104
111
55
25
43
465

(9.3)
(12.4)
(10.2)
(9.7)
(7.9)
(6.5)
(7.0)
(3.5)
(1.6)
(2.7)
(29.2)

510
1,081

(32.1)
(67.9)

Officer Duty Status
On-Duty
Off-Duty

1,093
498

(68.7)
(31.3)

Rank
Officer
Detective
Corporal
Sergeant
Lieutenant
Captain
Major
Colonel
Deputy Chief
Chief

1,132
111
25
141
42
18
5
2
18
97

(71.2)
(7.0)
(1.6)
(8.9)
(2.6)
(1.1)
(0.3)
(0.1)
(1.1)
(6.1)

Function
Patrol & Street Level
Line/Field Supervisor
Management

1,243
208
140

(78.1)
(13.1)
(8.8)

362
329
726
174

(22.8)
(20.7)
(45.6)
(10.9)

1,312
279

(82.5)
(17.5)

Region of United States
Northeastern States
Midwestern States
Southern States
Western States
Level of Rurality
Metropolitan County
Non-Metro County

(%)

62
251
66
1,162
42
7
1
0

(3.9)
(15.8)
(4.2)
(73.0)
(2.6)
(0.4)
(0.1)
(0.0)

Full-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499
500-999
1,000 or more

7
16
67
77
176
152
167
169
161
104
495

(0.4)
(1.0)
(4.2)
(4.9)
(11.1)
(9.6)
(10.5)
(10.6)
(10.1)
(6.5)
(31.1)

Part-Time Sworn Officers
0
1
2-4
5-9
10-24
25-49
50-99
100-249
250-499

1,209
46
104
111
94
19
6
1
1

(76.)
(2.9)
(6.5)
(7.0)
(5.9)
(1.2)
(0.3)
(0.1)
(0.1)

Agency Type
Primary State Police
Sheriff's Office
County Police Dept.
Municipal Police Dept.
Special Police Dept.
Constable
Tribal Police Dept.
Regional Police Dept.

337
Table 72. Most Serious Offense Charged in Profit-motivated Police Crime Arrest Cases, 2005-2011 (N = 1,591)
n
(%)
Unclassified Theft / Larceny
False Pretenses / Swindle
Drug / Narcotic violation
Robbery
Theft from Building
Extortion / Blackmail
Embezzlement
Burglary / Breaking & Entering
Bribery
All Other Offenses
False Report / False Statement
Weapons Law violation
Counterfeiting / Forgery
Stolen Property Offenses
Shoplifting
Official Misconduct / Oppression / Violation of Oath
Civil Rights violation
Intimidation
Theft from Motor Vehicle
Arson
Impersonation
Obstruction of Justice
Aggravated Assault

255
199
189
103
92
85
77
72
56
49
46
40
39
38
31
29
26
21
21
16
14
11
10

(16.0)
(12.5)
(11.9)
(6.4)
(5.8)
(5.3)
(4.8)
(4.5)
(3.5)
(3.1)
(2.9)
(2.5)
(2.5)
(2.4)
(1.9)
(1.8)
(1.6)
(1.3)
(1.3)
(1.0)
(0.9)
(0.7)
(0.6)

Wire Fraud
Gambling: Operating / Promoting
Simple Assault
Motor Vehicle Theft
Credit Card Fraud / ATM Fraud
Murder & Nonnegligent Manslaughter
Assisting or Promoting Prostitution
Evidence: Destroying / Tampering
Gambling: Betting / Wagering
Theft of Motor Vehicle Parts / Accessories
Kidnapping / Abduction
Unclassified Sex Crime
Welfare Fraud
Pocket-Picking
Theft from a Coin-operated Machine
Prostitution
Forcible Sodomy
Forcible Fondling
Bad Checks
Disorderly Conduct
Family Offenses, nonviolent
Liquor Law violation
Wiretapping, illegal

(%)

9
9
7
7
6
5
4
4
3
3
2
2
1
1
1
1
1
1
1
1
1
1
1

(0.6)
(0.6)
(0.4)
(0.4)
(0.4)
(0.3)
(0.3)
(0.3)
(0.2)
(0.2)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)

338
Table 73. Victim Characteristics in Profit-motivated Police Crime Arrest Cases, 2005-2011 (N = 1,591)
n
(%) (Valid %)
Victim's Sex
Female
Male
Missing

71
195
1,325

(4.4)
(83.3)
(16.7)

(26.7)
(73.3)

Victim's Age
Birth-11
12-13
14-15
16-17
18-19
20-24
25-32
33-41
42 or older
Missing

0
1
0
2
1
9
11
9
13
1,545

(0.0)
(0.1)
(0.0)
(0.1)
(0.1)
(0.6)
(0.7)
(0.6)
(0.8)
(97.0)

(0.0)
(2.2)
(0.0)
(4.3)
(2.2)
(19.5)
(24.0)
(19.5)
(28.3)

(%)

(Valid %)

1
4
1
6
0
5
6
350
1,218

(0.1)
(0.3)
(0.1)
(0.4)
(0.0)
(0.3)
(0.4)
(21.9)
(76.5)

(0.3)
(1.1)
(0.3)
(1.6)
(0.0)
(1.3)
(1.6)
(93.8)

Victim's Law Enforcement Status
Victim is Not a Police Officer
Victim is a Police Officer
Missing

358
12
1,221

(22.5)
(0.8)
(76.7)

(96.8)
(3.2)

Victim Adult or Child
Adult
Child
Missing

374
7
1,210

(23.5)
(0.4)
(76.1)

(98.2)
(1.8)

339
Table 74. Bivariate Associations of Conviction in Profit-motivated Police Crime Arrest Cases
Variable Label
Year
Age
Years of Service
Type of Agency
State
Part-Time Sworn Personnel (categorical)
Urban to Rural Continuum Code
Arresting Agency
Burglary
Drug / Narcotic violation
Embezzlement
Extortion / Blackmail
Pocket-Picking
Shoplifting
Weapons Law violation
Civil Rights violation
Victim's Relationship to the Offender
Organizational vs. Against Citizenry
Drug-related Police Crime
Violence-related Police Crime
Officer was Reassigned
Officer was Suspended
Officer's Supervisor was Disciplined
Officer's Chief is Under Scrutiny
Family Violence
Oxycodone
Cocaine
Marijuana
OIDV: Weapon: Dept-issued Gun
OIDV: Weapon: Hands / Fist
OIDV: Weapon: Miscellaneous Objects
OIDV: Weapon: Verbal Threats
OIDV: Protection Order was Filed
OIDV: Victim Injured, nonfatal

Variable
V2
V3
V4
V9
V11
V12
V13
V14
V20
V23
V25
V26
V40
V42
V63
V65
V85
V90
V93
V96
V102
V104
V105
V106
V126
V133
V139
V148
V156
V164
V166
V167
V171
V173

N
1,105
1,010
799
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
266
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105

χ2
23.832
85.828
62.423
14.237
88.251
21.871
43.579
22.569
5.262
28.447
7.204
5.654
4.790
7.668
5.992
5.402
25.017
5.729
23.074
4.100
18.365
22.939
6.340
4.178
9.353
5.048
19.749
4.436
4.790
4.790
4.790
9.588
9.588
14.395

df
6
49
39
6
47
8
8
1
1
1
1
1
1
1
1
1
6
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.001
.001
.010
.027
.000
.005
.000
.000
.022
.000
.007
.017
.029
.006
.014
.020
.000
.017
.000
.043
.000
.000
.012
.041
.002
.025
.000
.035
.029
.029
.029
.002
.002
.000

V
.147
.292
.280
.114
.283
.141
.199
.143
.069
.160
.081
.072
.066
.083
.074
.070
.307
.072
.145
.061
.129
.144
.076
.061
.092
.068
.134
.063
.066
.066
.066
.093
.093
.114

340
18 U.S.C. §242 Criminal Defendant
Drugs: Trafficking
Drugs: Facilitation of Drug Trade
Drugs: Shakedown from Car Stops
Drugs: Shakedown from Off-Duty Robberies
Drugs: Shakedown (aggregate)
Lost Job
Narcotics
Stimulants
Cannabis
Age (categorical)
Geographic Region
Geographic Division
Victim Age (categorical)
Victim Age Difference

V181
V197
V201
V206
V207
V210
joblossb
narcotics
stimulant
cannabis
agecat
geogreg
geogdiv
vicagecat
vicagediff

1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105
1,105

6.282
20.696
11.065
6.252
3.988
7.766
82.094
6.347
18.068
4.436
44.496
12.647
16.063
21.614
39.147

1
1
1
1
1
1
1
1
1
1
10
3
8
6
21

.012
.000
.001
.012
.046
.005
.000
.012
.000
.035
.000
.005
.041
.001
.009

.075
.137
.100
.075
.060
.084
.273
.076
.128
.063
.201
.107
.121
.140
.188

341
Table 75. Profit-motivated Police Crime Arrest Cases: Logistic Regression Model Predicting Conviction (N = 197)
95% CI for Exp(B)
B
Type of Agency
State
Part-time Sworn Personnel (categorical)
Violence-related

Wald

Exp(B)

0.519

0.224

5.381

.020

1.681

1.084

2.606

0.070

0.020

12.566

< .001

1.073

1.032

1.115

-0.318

0.160

3.931

.047

0.728

0.532

0.996

1.666

0.732

5.177

.023

5.292

1.260

22.234

Officer's Chief is Under Scrutiny

-3.293

1.398

5.549

.018

0.037

0.002

0.575

Drugs: Shakedown / Theft from Off-duty Robbery

-4.517

2.035

4.927

.026

0.011

0.000

0.589

4.702

1.648

8.141

.004

110.177

4.358

2785.279

3.187

0.679

22.041

< .001

24.216

6.401

91.605

-0.357

0.123

8.439

.004

0.700

0.550

0.890

Drugs: Any Shakedown / Theft )
Job Lost
Victim Age Categorical
- 2 Log Likelihood

133.494

Model Chi-Square

82.984

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.344
.516

95% CI for AUC

.186

.593

UL
.547

.639

342
Table 76. Bivariate Associations of Job Loss in Profit-motivated Police Crime Arrest
Cases
Variable Label
Year
Years of Service
Gender
Rank
Type of Agency
Full-Time Sworn Personnel (categorical)
State
Urban to Rural Continuum
Arresting Agency
Arson
Drug / Narcotic violation
Credit Card / ATM Fraud
Wire Fraud
Gambling: Operating / Promoting
Robbery
Weapons Law violation
Civil Rights violation
Obstruction of Justice
Victim is a Police Officer
Internal versus Organizational
Official Capacity
Drug-related Police Crime
Officer was Reassigned
Officer was Suspended
Officer's Supervisor was Disciplined
Officer's Chief is Under Scrutiny
Discussion of Agency Scandal
Conviction
Heroin
Hydrocodone
Oxycodone
Other Narcotics
28 U.S.C. §1441 Civil Rights Case Removed

Variable
V2
V4
V5
V7
V9
V10
V11
V13
V14
V15
V23
V28
V31
V33
V52
V63
V65
V74
V82
V91
V92
V93
V102
V104
V105
V106
V107
V109
V129
V131
V133
V135
V179

N
1,591
1,126
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
370
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,105
1,591
1,591
1,591
1,591
1,591

χ2
155.340
56.457
7.232
23.219
13.075
33.267
126.335
21.745
4.878
5.204
17.469
5.860
6.423
6.619
4.630
7.182
15.304
6.705
4.516
14.997
7.175
16.389
23.675
87.439
15.083
4.366
9.010
82.094
8.764
5.765
5.827
4.499
3.922

df
6
39
1
9
6
10
47
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

p
.000
.035
.007
.006
.042
.000
.000
.005
.027
.023
.000
.015
.011
.010
.031
.007
.000
.010
.034
.000
.007
.000
.000
.000
.000
.037
.003
.000
.003
.016
.016
.034
.048

V
.312
.224
.067
.121
.091
.145
.282
.117
.055
.057
.105
.061
.064
.065
.054
.067
.098
.065
.110
.097
.067
.101
.122
.234
.097
.052
.075
.273
.074
.060
.061
.053
.050

343
18 U.S.C. §242 Criminal Defendant
Drugs: Using / Personal Use
Drugs: Selling / Trafficking
Drugs: Facilitation of Drug Trade
Drugs: Shakedowns from Car Stops
Drugs: Theft from Police Evidence Room
Narcotics
Depressants
Age Categorical
Years of Service Categorical
Metropolitan versus Nonmetro County
Geographic Region
Geographic Division
Rank by Function

V181
V196
V197
V201
V206
V208
narcotics
depress
agecat
yrsservcat
countydic
geogreg
geogdiv
rankfunc

1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591
1,591

20.538
6.163
11.652
9.870
4.095
5.085
18.086
4.761
49.034
28.444
8.468
17.057
33.167
15.232

1
1
1
1
1
1
1
1
10
10
1
3
8
2

.000
.013
.001
.002
.043
.024
.000
.029
.000
.002
.004
.001
.000
.000

.114
.062
.086
.079
.051
.057
.107
.055
.176
.134
.073
.104
.144
.098

344
Table 77. Profit-motivated Police Crime Arrest Cases: Logistic Regression Model Predicting Job Loss (N = 206)
95% CI for Exp(B)
B
Internal Crime Against the Organization
Official Capacity

Wald

Exp(B)

0.381

0.093

16.830

< .001

1.464

1.220

1.756

2.302

0.781

8.692

.003

9.995

2.163

46.179

-2.597

0.824

9.922

.002

0.075

0.015

0.375

Conviction

2.404

0.633

14.413

< .001

11.070

3.200

38.301

Drugs: Selling / Dealing / Trafficking

3.839

1.235

9.666

.002

46.459

4.132

522.381

Geographic Division

0.528

0.152

12.104

.001

1.695

1.259

2.283

Obstruction of Justice

-2.741

1.073

6.530

.011

0.065

0.008

0.528

- 2 Log Likelihood

99.126

Model Chi-Square

111.933

Discussion of an Agency Scandal or Cover Up

Cox & Snell R
Nagelkerke R
ROC R
AUC

<.001

.419
.654

95% CI for AUC

.336

.668

UL
.626

.711

345
Table 78. 200 Largest State & Local Law Enforcement Agencies in Study: Rates of Officers Arrested, 2005-2011 (Sorted by # Full-Time Sworn Personnel)
# of Full-Time
Per
Per 1,000
Per 100,000
Agency
County
State
Sworn Personnel
Agency
Officers
Population
New York City Police Department
Chicago Police Department
Los Angeles Police Department
Los Angeles County Sheriff's Office
California Highway Patrol
Philadelphia Police Department
Cook County Sheriff's Office
Houston Police Department
New York State Police
New York State Courts Officers
Pennsylvania State Police
Washington Metropolitan Police Department
Texas Department of Public Safety
Dallas Police Department
Phoenix Police Department
Miami-Dade (County) Police Department
New Jersey State Police
Baltimore Police Department
Las Vegas Metro Police Department
Nassau County Police Department
Suffolk County Police Department
Harris County Sheriff's Office
Massachusetts State Police
Detroit Police Department
Boston Police Department
Riverside County Sheriff's Office
Illinois State Police
San Antonio Police Department
Milwaukee Police Department
San Diego Police Department
San Francisco Police Department
Honolulu (City & County) Police Department

New York
Cook
Los Angeles
Los Angeles
Sacramento
Philadelphia
Cook
Harris
Albany
New York
Dauphin
DC
Travis
Dallas
Maricopa
Miami-Dade
Mercer
Baltimore City
Clark
Nassau
Suffolk
Harris
Middlesex
Wayne
Suffolk
Riverside
Sangamon
Bexar
Milwaukee
San Diego
San Francisco
Honolulu

NY
IL
CA
CA
CA
PA
IL
TX
NY
NY
PA
DC
TX
TX
AZ
FL
NJ
MD
NV
NY
NY
TX
MA
MI
MA
CA
IL
TX
WI
CA
CA
HI

36023
13354
9727
9461
7202
6624
5655
5053
4847
4500
4458
3742
3529
3389
3388
3093
3053
2990
2942
2732
2622
2558
2310
2250
2181
2147
2105
2020
1987
1951
1940
1934

196
83
30
14
13
66
8
35
5
1
26
29
6
39
17
25
13
55
8
6
6
3
10
23
14
11
10
30
73
12
7
26

5.44
6.22
3.08
1.48
1.81
9.96
1.41
6.93
1.03
0.22
5.83
7.75
1.70
11.51
5.02
8.08
4.26
18.39
2.72
2.20
2.29
1.17
4.33
10.22
6.42
5.12
4.75
14.85
36.74
6.15
3.61
13.44

2.40
1.60
0.31
0.14
0.03
4.33
0.15
0.86
0.03
0.01
0.20
4.82
0.02
1.65
0.45
1.00
0.15
8.86
0.41
0.45
0.40
0.07
0.15
1.26
1.94
0.50
0.08
1.75
7.70
0.39
0.87
2.73

346
Baltimore County Police Department
Columbus Police Department
Virginia State Police
North Carolina State Highway Patrol
San Bernardino County Sheriff's Office
Orange County Sheriff-Coroner Department
Michigan State Police
Atlanta Police Department
Charlotte - Mecklenburg Police Department
Port Authority of New York & New Jersey Police
Jacksonville Sheriff's Office
Broward County Sheriff's Office
Cleveland Police Department
Florida Highway Patrol
Indianapolis Police
Prince George's County Police Department
Ohio State Highway Patrol
Memphis Police Department
Denver Police Department
Austin Police Department
Fort Worth Police Department
Palm Beach County Sheriff's Office
Maryland State Police
New Orleans Police Department
Kansas City Police Department
Fairfax County Police Department
Sacramento County Sheriff's Office
Orange County Sheriff's Office
San Jose Police Department
St. Louis (city) Police Dept
San Diego County Sheriff's Office
Indiana State Police
Nashville Metro Police Department
Newark Police
Seattle Police Department
Arizona Department of Public Safety

Baltimore
Franklin
Chesterfield
Wake
San Bernardino
Orange
Ingham
Fulton
Mecklenburg
Hudson
Duval
Broward
Cuyahoga
Leon
Marion
Prince Georges
Franklin
Shelby
Denver
Travis
Tarrant
Palm Beach
Baltimore
Orleans
Jackson
Fairfax
Sacramento
Orange
Santa Clara
St. Louis City
San Diego
Marion
Davidson
Essex
King
Maricopa

MD
OH
VA
NC
CA
CA
MI
GA
NC
NJ
FL
FL
OH
FL
IN
MD
OH
TN
CO
TX
TX
FL
MD
LA
MO
VA
CA
FL
CA
MO
CA
IN
TN
NJ
WA
AZ

1910
1886
1873
1827
1797
1794
1732
1719
1672
1667
1662
1624
1616
1606
1582
1578
1560
1549
1525
1515
1489
1447
1440
1425
1421
1419
1409
1398
1382
1351
1322
1315
1315
1310
1283
1244

4
9
3
8
11
14
9
22
20
1
12
16
27
14
33
19
4
46
13
10
20
12
9
63
5
7
6
16
9
10
7
4
18
8
9
3

2.09
4.77
1.60
4.38
6.12
7.80
5.20
12.80
11.96
0.60
7.22
9.85
16.71
8.72
20.86
12.04
2.56
29.70
8.52
6.60
13.43
8.29
6.25
44.21
3.52
4.93
4.26
11.44
6.51
7.40
5.30
3.04
13.69
6.11
7.01
2.41

0.50
0.77
0.04
0.08
0.54
0.47
0.09
2.39
2.17
0.01
1.39
0.92
2.11
0.07
3.65
2.20
0.03
4.96
2.17
0.98
1.11
0.91
0.16
18.32
0.74
0.65
0.42
1.40
0.51
3.13
0.23
0.06
2.87
1.02
0.47
0.05

347
Connecticut State Police
Hillsborough County Sheriff's Office
Louisiana State Police
Montgomery County Police Department
Louisville Metro Police Department
Washington State Patrol
El Paso Police Department
Miami Police Department
Cincinnati Police Department
DeKalb County Police Department
Wayne County Sheriff's Office
Georgia Department of Public Safety
Oklahoma City Police Department
Tucson Police Department
Albuquerque Police Department
Tampa Police Department
Long Beach Police Department
South Carolina Highway Patrol
Tennessee Department of Safety
Alameda County Sheriff's Office
Portland Police Bureau
Minneapolis Police Department
Jersey City Police
Pittsburgh Police Department
Kentucky State Police
Pinellas County Sheriff's Office
Mesa Police Department
Fresno Police Department
Tulsa Police Department
Oklahoma Department of Public Safety
Jefferson Parish Sheriff's Office
Birmingham Police Department
Virginia Beach Police Department
Buffalo Police Department
St. Louis County Police Dept
Oakland Police Department

Middlesex
Hillsborough
E. Baton Rouge
Montgomery
Jefferson
Thurston
El Paso
Miami-Dade
Hamilton
DeKalb
Wayne
Fulton
Oklahoma
Pima
Bernalillo
Hillsborough
Los Angeles
Richland
Davidson
Alameda
Multnomah
Hennepin
Hudson
Allegheny
Franklin
Pinellas
Maricopa
Fresno
Tulsa
Oklahoma
Jefferson
Jefferson
VA Beach City
Erie
St. Louis
Alameda

CT
FL
LA
MD
KY
WA
TX
FL
OH
GA
MI
GA
OK
AZ
NM
FL
CA
SC
TN
CA
OR
MN
NJ
PA
KY
FL
AZ
CA
OK
OK
LA
AL
VA
NY
MO
CA

1227
1223
1215
1206
1197
1132
1132
1104
1082
1074
1062
1048
1046
1032
1020
980
968
967
942
928
928
902
900
891
882
863
831
828
826
825
825
816
813
793
781
773

4
10
2
16
16
2
15
14
10
13
1
1
12
6
13
3
4
10
4
2
10
18
12
21
3
8
2
7
11
5
12
8
9
4
2
3

3.26
8.18
1.65
13.27
13.37
1.77
13.25
12.68
9.24
12.10
0.94
0.95
11.47
5.81
12.75
3.06
4.13
10.34
4.25
2.16
10.78
19.96
13.33
23.57
3.40
9.27
2.41
8.45
13.32
6.06
14.55
9.80
11.07
5.04
2.56
3.88

0.11
0.81
0.04
1.65
2.16
0.03
1.87
0.56
1.25
1.88
0.05
0.01
1.67
0.61
1.96
0.24
0.04
0.22
0.06
0.13
1.36
1.56
1.89
1.72
0.07
0.87
0.05
0.75
1.82
0.13
2.77
1.21
2.05
0.44
0.20
0.20

348
Norfolk Police Department
Maricopa County Sheriff's Office
Alabama Department of Public Safety
Orlando Police Department
Ventura County Sheriff's Office
Richmond Police Department
Omaha Police Dept
Colorado State Patrol
Marion County Sheriff's Office
Denver County Sheriff's Office
King County Sheriff's Office
Rochester Police Department
Raleigh Police Department
Sacramento Police Department
New York State Metro Transportation Auth. Police
Gwinnett County Police Department
Contra Costa County Sheriff's Office
Iowa Department of Public Safety
Colorado Springs Police Department
Wichita Police Department
Delaware State Police
Yonkers Police Department
Toledo Police Department
Anne Arundel County Police Department
Baton Rouge Police Department
Collier County Sheriff's Office
Aurora Police Department
Florida Fish & Wildlife Conservation Commission
Lee County Sheriff's Office
Arlington Police Department
Polk County Sheriff's Office
St. Paul Police Department
Mississippi Highway Safety Patrol
Greensboro Police Department
Calcasieu Parish Sheriff's Office
Cobb County Police Department

Norfolk City
Maricopa
Montgomery
Orange
Ventura
Richmond City
Douglas
Jefferson
Marion
Denver
King
Monroe
Wake
Sacramento
New York
Gwinnett
Contra Costa
Polk
El Paso
Sedgwick
Kent
Westchester
Lucas
Anne Arundel
E. Baton Rouge
Collier
Arapahoe
Leon
Lee
Tarrant
Polk
Ramsey
Hinds
Guilford
Calcasieu
Cobb

VA
AZ
AL
FL
CA
VA
NE
CO
IN
CO
WA
NY
NC
CA
NY
GA
CA
IA
CO
KS
DE
NY
OH
MD
LA
FL
CO
FL
FL
TX
FL
MN
MS
NC
LA
GA

772
766
763
757
755
752
747
742
740
739
721
703
702
701
694
682
679
669
668
662
658
641
640
633
630
628
627
626
621
610
600
598
594
593
592
590

14
2
2
8
1
7
6
4
7
5
5
3
8
5
2
4
1
2
5
2
3
2
12
3
6
6
6
4
7
3
13
8
6
5
1
5

18.13
2.61
2.62
10.57
1.32
9.31
8.03
5.39
9.46
6.77
6.93
4.27
11.40
7.13
2.88
5.87
1.47
2.99
7.49
3.02
4.56
3.12
18.75
4.74
9.52
9.55
9.57
6.39
11.27
4.92
21.67
13.38
10.10
8.43
1.69
8.47

5.77
0.05
0.04
0.70
0.12
3.43
1.16
0.08
0.77
0.83
0.26
0.40
0.89
0.35
0.02
0.50
0.10
0.07
0.80
0.40
0.33
0.21
2.72
0.56
1.36
1.87
1.05
0.02
1.13
0.17
2.16
1.57
0.20
1.02
0.52
0.73

349
Pima County Sheriff's Dept.
Henrico County Division of Police
Prince William County Police Department
Savannah-Chatham Metropolitan Police Dept
Gwinnett County Sheriff's Office
Minnesota State Patrol
Passaic County Sheriff's Office
New Mexico State Police
Bexar County Sheriff's Office
Arkansas State Police
Milwaukee County Sheriff's Office
Little Rock Police Department
Fulton County Sheriff's Office
Shelby County Sheriff's Office
Mobile Police Department
Tulare County Sheriff's Office
Kern County Sheriff's Office
Richland County Sheriff's Office
Shreveport Police Department
St. Petersburg Police Department
Winston-Salem Police Department
Orleans Parish Sheriff's Office (Criminal Division)
Montgomery Police Department
Fairfax County Sheriff's Office
Brevard County Sheriff's Office
Paterson Police
Monmouth County Sheriff's Office
Durham Police Department
Nebraska State Patrol
Syracuse Police Department
Pasco County Sheriff's Office
District of Columbia Protective Services Police
Providence Police Department
Fort Lauderdale Police Department
Worcester Police Department
Jackson Police Department

Pima
Henrico
Prince William
Chatham
Gwinnett
Ramsey
Passaic
Santa Fe
Bexar
Pulaski
Milwaukee
Pulaski
Fulton
Shelby
Mobile
Tulare
Kern
Richland
Caddo
Pinellas
Forsyth
Orleans
Montgomery
Fairfax
Brevard
Passaic
Monmouth
Durham
Lancaster
Onondaga
Pasco
DC
Providence
Broward
Worcester
Hinds

AZ
VA
VA
GA
GA
MN
NJ
NM
TX
AR
WI
AR
GA
TN
AL
CA
CA
SC
LA
FL
NC
LA
AL
VA
FL
NJ
NJ
NC
NE
NY
FL
DC
RI
FL
MA
MS

554
554
546
534
531
530
530
528
526
525
524
520
516
516
515
513
512
512
511
510
508
505
500
499
497
497
494
494
491
489
485
484
483
482
482
480

1
4
3
6
3
4
6
13
5
2
7
2
4
8
4
3
11
3
12
6
3
4
6
3
2
3
1
9
1
3
4
1
7
6
5
9

1.81
7.22
5.49
11.24
5.65
7.55
11.32
24.62
9.51
3.81
13.36
3.85
7.75
15.50
7.77
5.85
21.48
5.86
23.48
11.76
5.91
7.92
12.00
6.01
4.02
6.04
2.02
18.22
2.04
6.13
8.25
2.07
14.49
12.45
10.37
18.75

0.10
1.30
0.75
2.26
0.37
0.08
1.20
0.63
0.29
0.07
0.74
0.52
0.43
0.86
0.97
0.68
1.31
0.78
4.71
0.65
0.86
1.16
2.62
0.28
0.37
0.60
0.16
3.36
0.05
0.64
0.86
0.17
1.12
0.34
0.63
3.67

350
Manatee County Sheriff's Office
Utah Department of Public Safety
Chesterfield County Police Department
Akron Police Department
North Las Vegas Police Department
Springfield Police Department
Fresno County Sheriff's Office
Knox County Sheriff's Office
Maryland Transportation Authority Police
Franklin County Sheriff's Office
El Paso County Sheriff's Office
Dane County Sheriff's Office
Santa Clara County Sheriff's Office
Volusia County Sheriff's Office
School District of Philadelphia Police
Richmond County Sheriff's Office
Dallas County Sheriff's Office
Loudoun County Sheriff's Office
Corpus Christi Police Department
Fort Wayne Police
Will County Sheriff's Office
Leon County Sheriff's Office
Washington Metro Area Transit Auth. Police
Madison Police Department

Manatee
Salt Lake
Chesterfield
Summit
Clark
Hampden
Fresno
Knox
Baltimore
Franklin
El Paso
Dane
Santa Clara
Volusia
Philadelphia
Richmond
Dallas
Loudoun
Nueces
Allen
Will
Leon
DC
Dane

FL
UT
VA
OH
NV
MA
CA
TN
MD
OH
CO
WI
CA
FL
PA
GA
TX
VA
TX
IN
IL
FL
DC
WI

476
475
475
472
471
464
461
456
456
455
454
454
450
450
450
449
449
448
448
447
445
443
442
437

4
2
1
8
2
9
1
1
1
2
1
2
1
4
1
5
1
2
3
4
1
3
2
1

8.40
4.21
2.11
16.95
4.25
19.40
2.17
2.19
2.19
4.40
2.20
4.41
2.22
8.89
2.22
11.14
2.23
4.46
6.70
8.95
2.25
6.77
4.52
2.29

1.24
0.07
0.32
1.48
0.10
1.94
0.11
0.23
0.02
0.17
0.16
0.41
0.06
0.81
0.07
2.49
0.04
0.64
0.88
1.13
0.15
1.09
0.33
0.20

351
Table 79. Nonmetropolitan State & Local Law Enforcement Agencies in Study: Rates of Officers Arrested, 2005-2011 (Sorted Alphabetically)
# of Full-Time
Per
Per 1,000
Agency
County
State Sworn Personnel
Agency
Officers
Accomack County Sheriff's Office
Ada Police Department
Alamogordo Department of Public Safety
Albert Lea Police Department
Algood Police Department
Alice Police Department
Allegan County Sheriff's Office
Allendale Police Department
Allenstown Police Department
Alma Police Department
Andalusia Police Dept.
Andrews Police Department
Appalachian State University Police Dept
Appling County Sheriff's Office
Arcade Police Department
Aspen Police Department
Assumption Parish Sheriff's Office
Athena Police Department
Atwater Police Department
Auburn Police Department
Austin Police Department
Bainbridge Police Department
Barre Police Department
Bartlesville Police Department
Baxter County Sheriff's Office
Beardstown Police Dept
Beeville Police Department
Belle Police Department
Belpre Police Department
Bennettsville Police Department
Benzie County Sheriff's Office
Berea Police Department

Accomack
Pontotoc
Otero
Freeborn
Putnam
Jim Wells
Allegan
Allendale
Merrimack
Bacon
Covington
Georgetown
Watauga
Appling
Jackson
Pitkin
Assumption
Umatilla
Kandiyohi
Cayuga
Mower
Decatur
Washington
Washington
Baxter
Cass
Bee
Maries
Washington
Marlboro
Benzie
Madison

VA
OK
NM
MN
TN
TX
MI
SC
NH
GA
AL
SC
NC
GA
GA
CO
LA
OR
MN
NY
MN
GA
VT
OK
AR
IL
TX
MO
OH
SC
MI
KY

27
42
71
29
11
37
59
10
10
10
30
9
25
14
9
23
50
2
1
70
31
40
18
54
32
9
23
3
9
36
13
30

1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
2
1
1
1
1
2
1
4
1
1
1
1
1
1
1
1

37.04
23.81
14.08
34.48
90.91
27.03
16.95
200.00
100.00
100.00
33.33
111.11
40.00
71.43
111.11
86.96
40.00
500.00
1000.00
14.29
32.26
50.00
55.56
74.07
31.25
111.11
43.48
333.33
111.11
27.78
76.92
33.33

Per 100,000
Population
3.02
2.67
1.57
3.20
1.38
2.45
0.90
19.20
0.68
9.01
2.65
1.66
1.96
5.48
1.65
11.66
8.54
1.32
2.37
1.25
2.55
7.18
1.68
7.85
2.41
7.33
3.14
10.90
1.62
3.46
5.71
1.21

352
Berlin Borough Police Department
Berlin Heights Police Department
Big Horn County Sheriff's Office
Big Spring Police Department
Bingen-White Salmon Police Department
Birchwood Police Department
Bishopville Police Department
Bismarck Police Department
Black Mountain Police Department
Black River Falls Police Department
Blackford County Sheriff's Office
Bloomville Police Department
Blountstown Police Dept.
Blue Lake Police Department
Bluefield Police Department
Bogalusa Police Department
Bolivar County Sheriff's Office
Bolivar Police Department
Boone Police Department
Boswell Police Department
Bowman Police Department
Braselton Police Department
Bridgeport Police Department
Brownwood Police Department
Bunkie Police Department
Burr Oak Police Department
Butler County Sheriff's Office
Butler Township Police Department
Butte - Silver Bow County Sheriff's Office
Byron Police Dept
BYU-Idaho Police Department
Caddo Police Department
Calhoun County Sheriff's Office
Calhoun Police Department
Campbell County Sheriff's Office
Canton Police Dept

Somerset
Erie
Big Horn
Howard
Klickitat
Washburn
Lee
St. Francois
Buncombe
Jackson
Blackford
Seneca
Calhoun
Humboldt
Mercer
Washington
Bolivar
Hardeman
Watauga
Choctaw
Orangeburg
Jackson
Jackson
Brown
Avoyelles
St. Joseph
Butler
Schuylkill
Silver Bow
Ogle
Madison
Bryan
Calhoun
Gordon
Campbell
Fulton

PA
OH
MT
TX
WA
WI
SC
MO
NC
WI
IN
OH
FL
CA
WV
LA
MS
TN
NC
OK
SC
GA
AL
TX
LA
MI
AL
PA
MT
IL
ID
OK
FL
GA
WY
IL

1
1
8
40
6
2
16
3
18
7
8
0
8
4
21
37
18
21
38
2
3
12
6
38
12
1
9
4
45
7
10
3
18
44
58
23

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1
2
1
1
3
1
2
1
1
1
1
1
1
1

1000.00
1000.00
125.00
25.00
166.67
500.00
62.50
333.33
55.56
142.86
125.00
125.00
250.00
47.62
27.03
55.56
47.62
26.32
500.00
1000.00
83.33
166.67
52.63
83.33
1000.00
333.33
250.00
44.44
142.86
100.00
333.33
55.56
22.73
17.24
43.48

1.29
1.30
7.77
2.86
4.92
6.28
5.20
1.53
2.22
4.89
7.83
1.76
6.84
0.74
1.61
2.12
2.93
3.67
1.96
6.58
3.24
1.65
1.88
5.25
2.38
1.63
14.32
0.67
5.85
1.87
2.66
2.36
6.84
1.81
2.17
2.70

353
Canton Village Police Department
Caribou Police Department
Carlisle Police Department
Carter County Sheriff's Office
Caruthersville Police Department
Central Michigan University Police Department
Chaffee Police Department
Chandler Police Department
Cherokee County Sheriff's Office
Cherokee Police Department
Chesterfield County Sheriff's Office
Chilhowie Police Department
Chillicothe Police Department
Clarksdale Police Department
Clay County Sheriff's Office
Clayton County Sheriff's Office
Clearlake Police Department
Clewiston Police Department
Clovis Police Department
Clyde Police Department
Cochran Police Department
Coeburn Police Department
Columbiana County Sheriff's Office
Columbus Police Department
Columbus Police Department
Columbus Police Department
Columbus Police Department
Commerce Police Department
Commerce Police Department
Concord Police Department
Conneaut Police Department
Cookeville Police Department
Cooter Police Department
Corbin Police Department
Corinth Police Department
Cortland Police Department

St. Lawrence
Aroostook
Nicholas
Carter
Pemiscot
Isabella
Scott
Henderson
Cherokee
Alfalfa
Chesterfield
Smyth
Ross
Coahoma
Clay
Clayton
Lake
Hendry
Curry
Sandusky
Bleckley
Wise
Columbiana
Lowndes
Colorado
Polk
Luna
Jackson
Ottawa
Merrimack
Ashtabula
Putnam
Pemiscot
Whitley
Alcorn
Cortland

NY
ME
KY
MO
MO
MI
MO
TX
SC
OK
SC
VA
OH
MS
AR
IA
CA
FL
NM
OH
GA
VA
OH
MS
TX
NC
NM
GA
OK
NH
OH
TN
MO
KY
MS
NY

8
14
5
3
21
21
5
8
48
2
41
6
47
35
7
12
21
20
53
14
14
7
22
62
10
6
4
24
5
77
19
70
1
19
39
44

1
1
1
2
2
1
1
1
2
1
1
4
1
1
1
1
1
2
3
1
3
1
2
1
1
1
1
1
1
1
1
2
1
1
1
1

125.00
71.43
200.00
666.67
95.24
47.62
200.00
125.00
41.67
500.00
24.39
666.67
21.28
28.57
142.86
83.33
47.62
100.00
56.60
71.43
214.29
142.86
90.91
16.13
100.00
166.67
250.00
41.67
200.00
12.99
52.63
28.57
1000.00
52.63
25.64
22.73

0.89
1.39
14.02
31.92
10.93
1.42
2.55
1.27
3.61
17.72
2.14
12.42
1.28
3.82
6.22
5.52
1.55
5.11
6.20
1.64
22.97
2.41
1.85
1.67
4.79
4.88
3.98
1.65
3.14
0.68
0.99
2.77
5.47
2.81
2.70
2.03

354
Craig Police Department
Crescent City Police Department
Creston Police Department
Cross County Sheriff's Office
Crossett Police Department
Crystal City Police Department
Cullman County Sheriff's Office
Custer County Sheriff's Office
Dale County Sheriff's Office
Danville (City) Sheriff's Office
Danville Police Department
Daviess County Sheriff's Office
DeKalb County Sheriff's Office
Del Norte County Sheriff's Office
Del Rio Police Department
Delavan Police Department
Delhi Police Department
Delhi Village Police Department
Demopolis Police Department
Denton Police Department
Deridder Police Department
Dewitt Police Department
Dillon Police Department
Dillon Police Department
Dixon Police Department
Dodge City Police Department
Douglas County Sheriff's Office
Dunn Police Department
Durand Police Department
Durango Police Department
Durant Police Department
Duval County Sheriff's Office
Earlville Police Dept
East Brewton Police Department
Eastman Police Department
Eatonton Police Department

Moffat
Putnam
Union
Cross
Ashley
Zavala
Cullman
Custer
Dale
Danville City
Pittsylvania
Daviess
DeKalb
Del Norte
Val Verde
Walworth
Richland
Delaware
Marengo
Caroline
Beauregard
Clinton
Dillon
Summit
Pulaski
Ford
Douglas
Harnett
Shiawassee
La Plata
Bryan
Duval
La Salle
Escambia
Dodge
Putnam

CO
FL
IA
AR
AR
TX
AL
OK
AL
VA
VA
IN
AL
CA
TX
WI
LA
NY
AL
MD
LA
IA
SC
CO
MO
KS
NV
NC
MI
CO
OK
TX
IL
AL
GA
GA

22
5
10
17
15
11
78
12
0
69
126
19
34
30
65
19
5
4
19
13
24
10
25
9
5
49
100
37
5
50
36
18
3
6
11
16

1
1
2
1
1
4
1
1
1
3
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

45.45
200.00
200.00
58.82
66.67
363.64
12.82
83.33
43.48
7.94
52.63
29.41
66.67
15.38
52.63
200.00
250.00
52.63
76.92
41.67
100.00
40.00
111.11
200.00
20.41
10.00
27.03
200.00
20.00
27.78
55.56
333.33
166.67
90.91
62.50

7.25
1.34
15.96
5.60
4.58
34.26
1.24
3.64
1.99
6.97
1.57
3.16
1.41
6.99
2.05
0.98
4.83
2.08
4.76
3.02
2.80
2.04
3.12
3.57
1.91
2.95
2.13
0.87
1.42
1.95
2.36
8.49
0.88
2.61
4.59
4.71

355
Edenton Police Department
Elgin Police Department
Elkin Police Department
Elko County Sheriff's Office
Elmore Police Department
Emery County Sheriff's Office
Emmet County Sheriff's Office
Ephrata Police Department
Errol Police Department
Estill County Sheriff's Office
Eunice Police Department
Eunice Police Department
Eureka Police Department
Fair Bluff Police Department
Fairfax Police Department
Fairland Police Department
Fairmont Police Department
Farmville Police Department
Fayette County Sheriff's Office
Fayette Police Department
Ferriday Police Department
Ferry County Sheriff's Office
Flemingsburg Police Department
Florala Police Department
Floyd County Sheriff's Office
Forest City Police Department
Forks Police Department
Fort Dodge Police Department
Fort Gibson Police Department
Fort Madison Police Department
Fort Payne Police Department
Foster Township Police Department
Frankfort Police Department
Franklin County Sheriff's Office
Franklin Police Dept
Freedom Police Department

Chowan
Grant
Surry
Elko
Ottawa
Emery
Emmet
Grant
Coos
Estill
St. Landry
Lea
Humboldt
Columbus
Allendale
Ottawa
Robeson
Prince Edward
Fayette
Fayette
Concordia
Ferry
Fleming
Covington
Floyd
Winnebago
Clallam
Webster
Muskogee
Lee
DeKalb
McKean
Franklin
Franklin
Franklin
Carroll

NC
ND
NC
NV
OH
UT
MI
WA
NH
KY
LA
NM
CA
NC
SC
OK
NC
VA
OH
AL
LA
WA
KY
AL
IA
IA
WA
IA
OK
IA
AL
PA
KY
IN
NE
NH

12
1
17
57
4
26
23
15
0
5
32
8
46
3
6
4
13
27
22
12
10
10
7
6
11
9
9
37
11
17
34
4
65
12
2
3

3
1
1
1
1
2
1
1
1
1
5
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

250.00
1000.00
58.82
17.54
250.00
76.92
43.48
66.67
200.00
156.25
125.00
21.74
333.33
166.67
250.00
76.92
37.04
136.36
83.33
100.00
100.00
142.86
166.67
90.91
111.11
111.11
27.03
90.91
58.82
29.41
250.00
15.38
83.33
500.00
333.33

20.28
41.77
1.36
2.05
2.41
18.22
3.06
1.12
3.03
6.82
6.00
1.54
0.74
1.72
9.60
3.14
0.75
4.28
10.33
5.80
4.80
13.24
6.97
2.65
6.13
9.20
1.40
2.63
1.41
2.79
1.41
2.30
2.03
4.33
31.01
2.09

356
Fremont County Sheriff's Office
Frisco Police Department
Fryeburg Police Department
Gaffney Police Department
Gage County Sheriff's Office
Galesburg Police Dept
Gallatin County Sheriff's Office
Gallup Police Department
Gatlinburg Police Department
Geneva Township Police Department
Georgetown County Sheriff's Office
Gillette Police Department
Glendive Police Department
Gold Beach Police Department
Graham County Sheriff's Office
Grambling State University Police Dept.
Grand Rapids Police Department
Grand Traverse County Sheriff's Office
Grant County Sheriff's Office
Grant County Sheriff's Office
Grants Police Department
Great Bend Police Department
Greenbrier County Sheriff's Office
Greene County Sheriff's Office
Greene County Sheriff's Office
Greensburg Police
Greenville Police Department
Griggs County Sheriff's Office
Guadalupe County Sheriff's Office
Halifax County Sheriff's Office
Hamburg Police Department
Hampton County Sheriff's Office
Hanceville Police Department
Hannahville Tribal Police Department
Hardeman County Sheriff's Office
Harrison County Sheriff's Office

Fremont
Summit
Oxford
Cherokee
Gage
Knox
Gallatin
McKinley
Sevier
Walworth
Georgetown
Campbell
Dawson
Curry
Graham
Lincoln
Wood
Grand Traverse
Grant
Grant
Cibola
Barton
Greenbrier
Greene
Greene
Decatur
Washington
Griggs
Guadalupe
Halifax
Fremont
Hampton
Cullman
Menominee
Hardeman
Harrison

IA
CO
ME
SC
NE
IL
IL
NM
TN
WI
SC
WY
MT
OR
AZ
LA
WI
MI
IN
OK
NM
KS
WV
AR
AL
IN
MS
ND
NM
VA
IA
SC
AL
MI
TN
TX

7
13
5
37
12
53
3
62
45
6
78
45
9
4
23
9
5
64
45
5
14
30
30
15
11
18
96
3
3
29
1
26
9
11
22
45

1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1

142.86
76.92
200.00
54.05
83.33
18.87
333.33
16.13
22.22
166.67
12.82
22.22
111.11
250.00
43.48
222.22
200.00
31.25
22.22
200.00
71.43
33.33
33.33
66.67
90.91
55.56
20.83
333.33
333.33
34.48
1000.00
38.46
111.11
90.91
45.45
22.22

13.44
3.57
1.73
3.61
4.48
1.89
17.89
1.40
1.11
0.98
1.66
2.17
11.15
4.47
2.69
4.28
1.34
2.30
1.43
22.09
3.67
3.61
2.82
2.38
11.06
3.89
3.91
41.32
21.34
2.76
13.44
4.74
1.24
4.16
3.67
1.52

357
Harrodsburg Police Department
Haskell Police Department
Haskell Police Department
Hastings Police Dept
Haynesville Police Dept
Haywood County Sheriff's Office
Hegins Township Police Department
Helen Police Department
Helena\/West Helena Police Department
Hemingway Police Department
Hempstead County Sheriff's Office
Hendry County Sheriff's Office
Hennessey Police Department
Henry County Sheriff's Office
Hermann Police Department
Hockley County Sheriff's Office
Holly Hill Police Department
Holmes County Sheriff's Office
Holt County Sheriff's Office
Homer City Borough Police Department
Hooksett Police Department
Hornell Police Department
Humboldt Police Department
Hunter Police Department
Huron Police Department
Indiana Borough Police Department
Inman Police Department
Ishpeming Police Department
Jackson County Sheriff's Office
Jackson County Sheriff's Office
Jaffrey Police Department
Jamestown Police Department
Jennings Police Department
Jim Wells County Sheriff's Office
Juneau County Sheriff's Office
Juneau Police Dept.

Mercer
Muskogee
Haskell
Adams
Claiborne
Haywood
Schuylkill
White
Phillips
Williamsburg
Hempstead
Hendry
Kingfisher
Henry
Gasconade
Hockley
Orangeburg
Holmes
Holt
Indiana
Merrimack
Steuben
Gibson
Greene
Beadle
Indiana
McPherson
Marquette
Jackson
Jackson
Cheshire
Chautauqua
Jefferson Davis
Jim Wells
Juneau
Juneau

KY
OK
TX
NE
LA
TN
PA
GA
AR
SC
AR
FL
OK
VA
MO
TX
SC
OH
NE
PA
NH
NY
TN
NY
SD
PA
KS
MI
FL
TN
NH
NY
LA
TX
WI
AK

16
5
3
36
7
22
2
10
30
4
13
67
3
112
7
11
7
33
5
2
28
22
26
2
24
22
2
10
61
14
10
62
24
27
45
44

1
1
1
2
1
1
1
1
4
1
1
1
1
14
1
2
1
1
1
1
1
1
2
1
1
1
1
1
1
3
1
2
1
1
1
1

62.50
200.00
333.33
55.56
142.86
45.45
500.00
100.00
133.33
250.00
76.92
14.93
333.33
125.00
142.86
181.82
142.86
30.30
200.00
500.00
35.71
45.45
76.92
500.00
41.67
45.45
500.00
100.00
16.39
214.29
100.00
32.26
41.67
37.04
22.22
22.73

4.69
1.41
16.95
6.38
5.82
5.32
0.67
3.68
18.38
2.91
4.42
2.55
6.65
25.85
6.57
8.72
1.08
2.36
9.58
1.13
0.68
1.01
4.03
2.03
5.75
1.13
3.43
1.49
2.01
25.78
1.30
1.48
3.17
2.45
3.75
3.20

358
Kahoka Police Department
Kalispell Police Department
Kauai (County) Police Department
Kaw Nation Tribal Police
Kendallville Police
Kenton Police Department
Keokuk County Sheriff's Office
Kermit Police Department
Kerrville Police Department
Ketchikan Police Dept.
Kingfisher County Sheriff's Office
Kings Mountain Police Department
Kingsland Police Dept
Kingsville Police Department
Kitty Hawk Police Department
Klamath Falls Police Department
La Salle County Sheriff's Office
La Salle County Sheriff's Office
LaGrange Police Department
Lake County Sheriff's Office
Lake County Sheriff's Office
Lake County Sheriff's Office
Lakeport Police Department
Lamesa Police Department
Lamoure Police Department
Lancaster Police Department
Lander University Public Safety
Las Vegas Police Department
Lawrence County Sheriff's Office
Lawrence Township Police Department
Lebanon Police Department
Lebanon Police Department
Lee County Sheriff's Office
Lee County Sheriff's Office
Leesville Police Department
Leland Police Department

Clark
Flathead
Kauai
Kay
Noble
Obion
Keokuk
Winkler
Kerr
Ketchikan Gateway
Kingfisher
Cleveland
Camden
Kleberg
Dare
Klamath
La Salle
La Salle
Troup
Lake
Lake
Lake
Lake
Dawson
LaMoure
Garrard
Greenwood
San Miguel
Lawrence
Clearfield
Grafton
Laclede
Lee
Lee
Vernon
Washington

MO
MT
HI
OK
IN
TN
IA
TX
TX
AK
OK
NC
GA
TX
NC
OR
IL
TX
GA
CA
CO
SD
CA
TX
ND
KY
SC
NM
IN
PA
NH
MO
NC
SC
LA
MS

3
36
125
6
18
5
4
10
51
22
8
30
43
45
17
39
36
10
83
61
9
5
12
16
1
10
10
27
25
9
35
30
47
28
28
18

1
1
3
1
1
1
1
1
1
1
1
2
1
1
1
1
1
3
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

333.33
27.78
24.00
166.67
55.56
200.00
250.00
100.00
19.61
45.45
125.00
66.67
23.26
22.22
58.82
25.64
27.78
300.00
12.05
32.79
222.22
200.00
83.33
62.50
1000.00
100.00
100.00
37.04
40.00
111.11
28.57
33.33
21.28
35.71
35.71
55.56

6.58
1.10
4.47
2.15
2.10
3.14
9.51
14.06
2.02
7.42
6.65
2.04
1.98
3.12
2.95
1.51
0.88
43.57
1.49
3.09
27.36
8.93
1.55
7.23
24.16
5.91
1.44
3.40
2.17
1.22
1.12
2.81
1.73
5.20
1.91
1.96

359
Lenawee County Sheriff's Office
Lenoir County Sheriff's Office
Level Plains Police Department
Lewis County Sheriff's Office
Lincoln County Sheriff's Office
Lincoln Parish Sheriff's Office
Lockhart Police Department
Lumberton Police Department
Luna County Sheriff's Office
Madill Police Department
Madison County Sheriff's Office
Mahanoy City Borough Police Department
Malakoff Police Department
Manitowoc Police Department
Marble Falls Police Department
Marble Head Police Department
Marion County Sheriff's Office
Marion Police Department
Marion Police Department
Marion Township Police Department
Marksville Police Department
Marlboro County Sheriff's Office
Marlow Police Department
Marshallville Police Dept.
Martin County Sheriff's Office
Marvell Police Department
Mason City Police Department
Mason County Sheriff's Office
McArthur Police Department
McDowell County Sheriff's Office
McDowell County Sheriff's Office
McIntosh County Sheriff's Office
McKinley County Sheriff's Office
Medina Police Department
Meigs Police Department
Meridian Police Department

Lenawee
Lenoir
Dale
Lewis
Lincoln
Lincoln
Covington
Robeson
Luna
Marshall
Madison
Schuylkill
Henderson
Manitowoc
Burnet
Ottawa
Marion
Marion
Smyth
Waushara
Avoyelles
Marlboro
Stephens
Macon
Martin
Phillips
Cerro Gordo
Mason
Vinton
McDowell
McDowell
McIntosh
McKinley
Gibson
Thomas
Lauderdale

MI
NC
AL
MO
OR
LA
AL
NC
NM
OK
MO
PA
TX
WI
TX
OH
WV
SC
VA
WI
LA
SC
OK
GA
NC
AR
IA
WV
OH
NC
WV
OK
NM
TN
GA
MS

44
58
5
5
65
46
1
73
30
12
8
4
4
64
27
4
27
24
18
1
20
25
10
4
34
3
46
21
4
40
15
17
35
11
3
99

1
2
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
1
1
1
1
1
3
1
1
1
2
1
1
1
3

22.73
34.48
200.00
200.00
15.38
21.74
1000.00
13.70
33.33
83.33
125.00
250.00
250.00
31.25
37.04
250.00
37.04
41.67
55.56
1000.00
50.00
80.00
100.00
250.00
29.41
333.33
21.74
142.86
250.00
25.00
66.67
117.65
28.57
90.91
333.33
30.30

1.00
3.36
1.99
9.79
2.17
2.14
2.65
0.75
3.98
6.31
8.18
0.67
1.27
2.46
2.34
2.41
1.77
3.02
3.10
4.08
2.38
6.91
2.22
6.78
4.08
4.60
2.26
10.98
7.44
2.22
4.52
9.88
1.40
2.01
2.24
3.74

360
Meyersdale Borough Police Department
Miami Police Department
Middlesex County Sheriff's Office
Middletown Police
Miller County Sheriff's Office
Millersburg Police Department
Mineral Wells Police Department
Minocqua Police Department
Missouri University of Science & Tech Police Dept
Moab Police Department
Moberly Police Department
Monroe County Sheriff's Office
Monroe County Sheriff's Office
Monroeville Police Department
Montague County Sheriff's Office
Montezuma Police Department
Monticello Police
Montpelier Police Department
Mora County Sheriff's Office
Morrow County Sheriff's Office
Mounds Police Dept
Mount Airy Police Department
Mount Pleasant Police Department
Murphy Police Department
Murray Police Department
Muscatine Police Department
Muskogee County Sheriff's Office
Muskogee Police Department
Nashville Police Department
Natchez Police Dept.
Natchitoches Parish Sheriff's Office
Navajo Nation Tribal Dept of Law Enforcement
Nebraska City Police Dept
New Athens Police Department
New Castle (city) Police Department
New Castle Police

Somerset
Ottawa
Middlesex
Henry
Miller
Holmes
Palo Pinto
Oneida
Phelps
Grand
Randolph
Monroe
Monroe
Huron
Montague
Macon
White
Bear Lake
Mora
Morrow
Pulaski
Surry
Titus
Cherokee
Calloway
Muscatine
Muskogee
Muskogee
Berrien
Adams
Natchitoches
Apache
Otoe
Harrison
Lawrence
Henry

PA
OK
VA
IN
MO
OH
TX
WI
MO
UT
MO
FL
WI
OH
TX
GA
IN
ID
NM
OR
IL
NC
TX
NC
KY
IA
OK
OK
GA
MS
LA
AZ
NE
OH
PA
IN

2
30
17
5
16
10
30
9
11
15
32
189
23
4
10
11
12
5
5
14
1
38
29
7
31
39
37
90
15
48
45
393
15
0
35
35

1
1
1
1
1
1
2
1
1
1
1
3
1
1
1
2
1
1
1
1
2
1
1
1
1
1
1
4
1
4
1
3
2
1
1
1

500.00
33.33
58.82
200.00
62.50
100.00
66.67
111.11
90.91
66.67
31.25
15.87
43.48
250.00
100.00
181.82
83.33
200.00
200.00
71.43
2000.00
26.32
34.48
142.86
32.26
25.64
27.03
44.44
66.67
83.33
22.22
7.63
133.33
28.57
28.57

1.29
3.14
9.12
2.02
4.04
2.36
7.11
2.78
2.21
10.84
3.93
4.10
2.24
1.68
5.07
13.57
4.06
16.71
20.49
8.95
32.46
1.36
3.09
3.64
2.69
2.34
1.41
5.63
5.19
12.39
2.53
4.19
12.71
6.30
1.10
2.02

361
New Castle Police Department
New Lisbon Police Department
New Martinsville Police Department
New Milford Police Department
New York City Dept of Env. Protection Police
Newaygo County Sheriff's Office
Newbury Police Department
Newport Police Department
Nicholas County Sheriff's Office
Nogales Police Department
North Kingsville Police Department
Norton Police Department
Norwich Police Department
Nye County Sheriff's Office
Oakdale Police Department
Oakwood Police Department
Ocean Shores Police Department
Ogdensburg Police Department
Oglesby Police Dept
Oglethorpe Police Department
Okanogan County Sheriff's Office
Olean Police Department
Olney Police Department
Onley Police Department
Ontario Police Department
Opelousas Police Department
Orangeburg County Sheriff's Office
Orangeburg Public Safety
Oregon County Sheriff's Office
Osceola County Sheriff's Office
Osceola Police Department
Otero County Sheriff's Office
Page County Sheriff's Office
Palatka Police Department
Paris Police Department
Pauls Valley Police Department

Garfield
Juneau
Wetzel
Litchfield
Westchester
Newaygo
Merrimack
Sullivan
Nicholas
Santa Cruz
Ashtabula
Norton
Chenango
Nye
Allen
Paulding
Grays Harbor
St. Lawrence
La Salle
Macon
Okanogan
Cattaraugus
Young
Accomack
Malheur
St. Landry
Orangeburg
Orangeburg
Oregon
Osceola
Clarke
Otero
Page
Putnam
Lamar
Garvin

CO
WI
WV
CT
NY
MI
NH
NH
KY
AZ
OH
KS
NY
NV
LA
OH
WA
NY
IL
GA
WA
NY
TX
VA
OR
LA
SC
SC
MO
MI
IA
NM
VA
FL
TX
OK

7
4
10
46
168
25
3
14
1
60
5
6
19
108
20
1
14
25
8
5
30
32
5
4
22
58
92
72
5
18
9
30
49
37
54
13

1
1
1
1
1
1
1
1
1
2
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
3
2
1
1
1
1
1
1
1

142.86
250.00
100.00
21.74
5.95
40.00
333.33
71.43
1000.00
33.33
200.00
166.67
52.63
27.78
50.00
1000.00
71.43
40.00
125.00
200.00
33.33
31.25
200.00
250.00
45.45
17.24
10.87
41.67
400.00
55.56
111.11
33.33
20.41
27.03
18.52
76.92

1.77
3.75
6.03
0.53
1.29
2.06
0.68
2.29
14.02
4.22
0.99
17.63
1.98
6.83
3.88
5.10
1.37
0.89
0.88
6.78
2.43
1.25
5.39
3.02
3.19
1.20
1.08
3.24
18.38
4.25
10.77
1.57
4.16
1.34
2.01
3.63

362
Pearson Police Department
Pelham Police Department
Perry County Sheriff's Office
Perryville Police Department
Petroleum County Sheriff's Office
Philadelphia Police Department
Phillips County Sheriff's Office
Pineland Police Department
Pineview Police Department
Pink Hill Police Department
Plainview Police Department
Platteville Police Department
Plattsburgh Police Department
Pocahontas County Sheriff's Office
Polk County Police Department
Polk County Sheriff's Office
Poplar Bluff Police Department
Port Barre Police Dept
Port Jefferson Police Department
Powell County Sheriff's Office
Princeton Police Department
Providence Police Department
Pulaski County Sheriff's Office
Putnam County Sheriff's Office
Quincy Police Department
Rainsville Police Department
Randolph County Sheriff's Office
Ranger Police Department
Ravalli County Sheriff's Office
Ravenna Police Dept
Red Springs Police Department
Richland Police Dept
Richmond Police Department
Ridgeville Police
Ridgeville Police Department
Rio Arriba County Sheriff's Office

Atkinson
Mitchell
Perry
Boyle
Petroleum
Neshoba
Phillips
Sabine
Wilcox
Lenoir
Hale
Grant
Clinton
Pocahontas
Polk
Polk
Butler
St. Landry
Shelby
Powell
Mercer
Webster
Pulaski
Putnam
Grant
DeKalb
Randolph
Eastland
Ravalli
Buffalo
Robeson
Pulaski
Madison
Randolph
Dorchester
Rio Arriba

GA
GA
IL
KY
MT
MS
AR
TX
GA
NC
TX
WI
NY
WV
GA
GA
MO
LA
OH
MT
WV
KY
IL
TN
WA
AL
IL
TX
MT
NE
NC
MO
KY
IN
SC
NM

5
14
12
1
1
27
16
2
1
2
34
20
47
5
35
27
43
7
0
10
17
7
11
58
12
11
13
5
29
3
16
5
65
2
2
22

1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
2
1
1
1
1
2
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1

200.00
71.43
83.33
1000.00
1000.00
37.04
62.50
500.00
1000.00
500.00
29.41
50.00
21.28
200.00
85.71
74.07
23.26
142.86
100.00
117.65
142.86
181.82
34.48
83.33
90.91
76.92
200.00
34.48
333.33
62.50
200.00
15.38
500.00
500.00
45.45

11.94
4.26
4.47
3.52
202.43
3.37
4.60
9.23
10.80
1.68
2.76
1.95
1.22
11.47
7.23
4.82
2.34
1.20
2.02
14.23
3.21
7.34
32.46
2.77
1.12
1.41
2.99
5.38
2.49
2.17
0.75
1.91
1.21
3.82
2.57
2.48

363
Ripley Police Department
Robbins Police Department
Robeson County Sheriff's Office
Rochelle Police Department
Rockmart Police Department
Roodhouse Police Dept
Roosevelt County Sheriff's Office
Roseboro Police Department
Rosebud Sioux Tribal Police
Rosedale Police Department
Roswell Police Department
Routt County Sheriff's Office
Royston Police Department
Ruidoso Police Department
Russellville Police Department
Rutland Police Department.
Saline County Sheriff's Office
Saline County Sheriff's Office
San Jacinto County Sheriff's Office
Sandusky Police Department
Sanford Police Department
Santa Clara Police Department
Santee Police Department
Sauk County Sheriff's Office
Sault Ste. Marie Police Department
Scotts Hill Police Department
Scottsbluff Police Dept
Scottsboro Police Department
Seadrift Police Department
Searcy Police Department
Seaside Police Department
Sedalia Police Dept
Selma Police Department
Selmer Police Department
Seneca County Sheriff's Office
Seneca Police Department

Jackson
Moore
Robeson
Wilcox
Polk
Greene
Roosevelt
Sampson
Todd
Bolivar
Chaves
Routt
Franklin
Lincoln
Franklin
Rutland
Saline
Saline
San Jacinto
Erie
Lee
Grant
Orangeburg
Sauk
Chippewa
Henderson
Scotts Bluff
Jackson
Calhoun
White
Clatsop
Pettis
Dallas
McNairy
Seneca
Oconee

WV
NC
NC
GA
GA
IL
MT
NC
SD
MS
NM
CO
GA
NM
AL
VT
KS
IL
TX
OH
NC
NM
SC
WI
MI
TN
NE
AL
TX
AR
OR
MO
AL
TN
NY
SC

9
5
128
4
18
4
11
5
27
5
80
22
15
26
25
41
44
42
21
54
81
5
6
118
25
3
30
44
2
41
18
44
50
18
23
34

1
1
3
1
1
1
1
2
1
1
2
1
1
1
2
1
1
1
1
2
1
1
2
1
1
1
1
1
1
1
1
1
2
1
6
1

111.11
200.00
23.44
250.00
55.56
250.00
90.91
400.00
37.04
200.00
25.00
45.45
66.67
38.46
80.00
24.39
22.73
23.81
47.62
37.04
12.35
200.00
333.33
8.47
40.00
333.33
33.33
22.73
500.00
24.39
55.56
22.73
40.00
55.56
260.87
29.41

3.42
1.13
2.24
10.80
2.41
7.20
9.59
3.15
10.40
2.93
3.05
4.25
4.53
4.88
6.31
1.62
1.80
4.01
3.79
2.59
1.73
3.39
2.16
1.61
2.60
3.60
2.70
1.88
4.68
1.30
2.70
2.37
4.56
3.84
17.02
1.35

364
Sevierville Police Department
Seymour Police
Shawnee Police Department
Shelby County Sheriff's Office
Shelby County Sheriff's Office
Shelby Police Department
Shelby Police Department
Shenandoah Borough Police Department
Shinnston Police Department
Simpson County Sheriff's Office
Sleepy Eye Police Department
Socorro County Sheriff's Office
Spencer Police Department
Springfield Police Department
Springfield Police Department
St. George Police Department
St. Landry Parish Sheriff's Office
St. Marys Police Department
St. Paul Police Department
Stanly County Sheriff's Office
Stark County Sheriff's Office
Starr County Sheriff's Office
Stephens County Sheriff's Office
Stephens County Sheriff's Office
Steuben County Sheriff's Office
Stillwater Police Department
Stover Police Department
Sturgis Police Department
Sugarcreek Borough Police Department
Sugarcreek Police Department
Sullivan County Sheriff's Office
Sweetwater Police Department
Tabor City Police Department
Tahlequah Police Department
Talbot County Sheriff's Office
Terrell County Sheriff's Office

Sevier
Jackson
Pottawatomie
Shelby
Shelby
Cleveland
Bolivar
Schuylkill
Harrison
Simpson
Redwood
Socorro
Clay
Windsor
Orangeburg
Dorchester
St. Landry
Auglaize
Wise
Stanly
Stark
Starr
Stephens
Stephens
Steuben
Payne
Morgan
St. Joseph
Venango
Tuscarawas
Sullivan
Monroe
Columbus
Cherokee
Talbot
Terrell

TN
IN
OK
OH
TX
NC
MS
PA
WV
KY
MN
NM
IA
VT
SC
SC
LA
OH
VA
NC
ND
TX
GA
OK
NY
OK
MO
MI
PA
OH
NY
TN
NC
OK
GA
TX

55
39
52
30
15
70
7
7
7
11
6
10
18
15
2
10
90
15
6
49
11
31
31
11
26
74
3
19
5
5
44
18
9
32
8
5

1
1
1
1
2
1
2
4
2
1
1
1
1
1
1
1
4
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
2
1
3
1

18.18
25.64
19.23
33.33
133.33
14.29
285.71
571.43
285.71
90.91
166.67
100.00
55.56
66.67
500.00
100.00
44.44
66.67
166.67
20.41
90.91
32.26
32.26
181.82
38.46
13.51
333.33
52.63
200.00
200.00
22.73
55.56
222.22
31.25
375.00
200.00

1.11
2.36
1.44
2.02
7.86
1.02
5.86
2.70
2.89
5.77
3.86
5.60
6.00
1.76
1.08
2.57
4.80
2.18
2.41
1.65
4.13
1.64
3.82
4.44
1.01
1.29
4.86
1.63
1.82
1.08
1.29
2.25
3.44
2.13
43.70
101.63

365
Thief River Falls Police Department
Thomas County Sheriff's Office
Tipton Police Department
Torrington Police Department
Towns County Sheriff's Office
Traverse City Police Department
Tucumcari Police Department
Tupelo Police Department
Turkey Creek Police Department
Tuscola County Sheriff's Office
Tuskegee Police Department
Tuskegee University Police Department
Tutwiler Police Department
Twin Falls Police Department
Union City Police Department
Union Police Department
University of West Alabama Police
Upper Sandusky Police Department
Vail Police Department
Vergennes Police Department
Vernal Police Department
Vernon Parish Sheriff's Office
Vidalia Police Department
Vinita Police Department
Virginia Marine Resources Commission
Wakeman Police Department
Wallace Police Department
Warren County Sheriff's Office
Washington Court House Police Dept.
Washington Police Department
Waukomis Police Department
Waynesburg Borough Police Department
West Yellowstone Police Department
Wheeler Police Department
White Cloud Police Department
White County Sheriff's Office

Pennington
Thomas
Tillman
Litchfield
Towns
Grand Traverse
Quay
Lee
Evangeline
Tuscola
Macon
Macon
Tallahatchie
Twin Falls
Obion
Newton
Sumter
Wyandot
Eagle
Addison
Uintah
Vernon
Concordia
Craig
Newport News City
Huron
Duplin
Warren
Fayette
Beaufort
Garfield
Greene
Gallatin
Dunn
Newaygo
White

MN
GA
OK
CT
GA
MI
NM
MS
LA
MI
AL
AL
MS
ID
TN
MS
AL
OH
CO
VT
UT
LA
LA
OK
VA
OH
NC
NC
OH
NC
OK
PA
MT
WI
MI
GA

15
47
1
79
18
32
13
115
1
25
23
33
4
64
36
7
6
13
28
5
22
64
20
15
60
1
12
30
22
36
3
8
5
0
2
40

1
2
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1

66.67
42.55
1000.00
12.66
55.56
31.25
76.92
8.70
1000.00
40.00
86.96
30.30
250.00
15.63
27.78
142.86
166.67
76.92
35.71
200.00
45.45
31.25
50.00
66.67
16.67
1000.00
83.33
33.33
45.45
27.78
333.33
125.00
200.00
500.00
25.00

7.18
4.47
12.51
0.53
9.55
1.15
11.06
1.21
2.94
1.79
9.32
4.66
6.50
1.29
3.14
4.60
7.27
4.42
1.92
2.72
3.07
3.82
4.80
6.65
3.02
1.68
1.71
4.77
3.44
2.09
1.65
2.58
1.12
2.28
2.06
3.68

366
White County Sheriff's Office
Whiteville Police Department
Whitley County Sheriff's Office
Wilkesboro Police Department
Williams Police Department
Williamsburg Police Department
Wilson Police Department
Wilson Police Department
Windber Borough Police Department
Winkler County Sheriff's Office
Winn Parish Sheriff's Office
Winnemucca Police Department
Winnfield Police Dept
Winnsboro Police Department
Winsted Police Department
Wolfeboro Police Department
Woodruff County Sheriff's Office
Woodstock Police Department
Yemassee Police Department
Zanesville Police Department
Zapata County Sheriff's Office
Zolfo Springs Police Department

White
Hardeman
Whitley
Wilkes
Colusa
Whitley
Wilson
Ellsworth
Somerset
Winkler
Winn
Humboldt
Winn
Franklin
Litchfield
Carroll
Woodruff
Grafton
Hampton
Muskingum
Zapata
Hardee

TN
TN
KY
NC
CA
KY
NC
KS
PA
TX
LA
NV
LA
LA
CT
NH
AR
NH
SC
OH
TX
FL

28
8
12
20
10
12
114
1
2
10
23
18
13
7
4
12
6
5
7
53
44
1

1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1

35.71
125.00
83.33
50.00
100.00
83.33
17.54
1000.00
500.00
100.00
43.48
55.56
76.92
142.86
250.00
83.33
166.67
200.00
142.86
56.60
22.73
1000.00

3.87
3.67
2.81
1.44
4.67
2.81
2.46
15.39
1.29
14.06
6.53
6.05
6.53
4.82
0.53
2.09
13.77
1.12
4.74
3.49
7.13
3.61

367
Table 80. Primary State Police Agencies in Study: Rates of Officers Arrested, 2005-2011 (Sorted Alphabetically)
# of Full-Time
Agency
County
State
Sworn Personnel
Alabama Department of Public Safety
Alaska State Troopers
Arizona Department of Public Safety
Arkansas Highway Police
Arkansas State Police
California Highway Patrol
Colorado State Patrol
Connecticut State Police
Delaware State Police
Florida Highway Patrol
Georgia Department of Public Safety
Idaho State Police
Illinois State Police
Indiana State Police
Iowa Department of Public Safety
Kentucky State Police
Louisiana State Police
Maine State Police
Maryland State Police
Massachusetts State Police
Michigan State Police
Minnesota State Patrol
Mississippi Highway Safety Patrol
Nebraska State Patrol
Nevada Highway Patrol
New Hampshire State Police
New Jersey State Police
New Mexico State Police
New York State Police
North Carolina State Highway Patrol
Ohio State Highway Patrol
Oklahoma Department of Public Safety

Montgomery
Anchorage
Maricopa
Pulaski
Pulaski
Sacramento
Jefferson
Middlesex
Kent
Leon
Fulton
Ada
Sangamon
Marion
Polk
Franklin
East Baton Rouge
Kennebec
Baltimore
Middlesex
Ingham
Ramsey
Hinds
Lancaster
Carson
Merrimack
Mercer
Santa Fe
Albany
Wake
Franklin
Oklahoma

AL
AK
AZ
AR
AR
CA
CO
CT
DE
FL
GA
ID
IL
IN
IA
KY
LA
ME
MD
MA
MI
MN
MS
NE
NV
NH
NJ
NM
NY
NC
OH
OK

Per
Agency

763
274
1244
149
525
7202
742
1227
658
1606
1048
264
2105
1315
669
882
1215
334
1440
2310
1732
530
594
491
417
350
3053
528
4847
1827
1560
825

2
1
3
2
2
13
4
4
3
14
1
1
10
4
2
3
2
1
9
10
9
4
6
1
3
3
13
13
5
8
4
5

Per 1,000
Officers
2.62
3.65
2.41
13.42
3.81
1.81
5.39
3.26
4.56
8.72
0.95
3.79
4.75
3.04
2.99
3.40
1.65
2.99
6.25
4.33
5.20
7.55
10.10
2.04
7.19
8.57
4.26
24.62
1.03
4.38
2.56
6.06

Per 100,000
Population
0.04
0.14
0.05
0.07
0.07
0.03
0.08
0.11
0.33
0.07
0.01
0.06
0.08
0.06
0.07
0.07
0.04
0.08
0.16
0.15
0.09
0.08
0.20
0.05
0.11
0.23
0.15
0.63
0.03
0.08
0.03
0.13

368
Pennsylvania State Police
Rhode Island State Police
South Carolina Highway Patrol
Tennessee Department of Safety
Texas Department of Public Safety
Utah Department of Public Safety
Vermont State Police
Virginia State Police
Washington State Patrol
Wyoming Highway Patrol

Dauphin
Providence
Richland
Davidson
Travis
Salt Lake
Washington
Chesterfield
Thurston
Laramie

PA
RI
SC
TN
TX
UT
VT
VA
WA
WY

4458
201
967
942
3529
475
307
1873
1132
204

26
2
10
4
6
2
1
3
2
1

5.83
9.95
10.34
4.25
1.70
4.21
3.26
1.60
1.77
4.90

0.20
0.19
0.22
0.06
0.02
0.07
0.16
0.04
0.03
0.18

369
Table 81. Sheriff's Offices in Study: Rates of Officers Arrested, 2005-2011 (Sorted Alphabetically)
# of Full-Time
Agency
County
State
Sworn Personnel
Acadia Parish Sheriff's Office
Accomack County Sheriff's Office
Adams County Sheriff's Office
Alachua County Sheriff's Office
Alameda County Sheriff's Office
Allegan County Sheriff's Office
Allegheny County Sheriff's Office
Anderson County Sheriff's Office
Anoka County Sheriff's Office
Arapahoe County Sheriff's Office
Assumption Parish Sheriff's Office
Atlantic County Sheriff's Office
Baker County Sheriff's Office
Baltimore (City) Sheriff's Office
Baltimore County Sheriff's Office
Barnstable County Sheriff's Office
Barrow County Sheriff's Office
Baxter County Sheriff's Office
Bay County Sheriff's Office
Beaufort County Sheriff's Office
Bedford County Sheriff's Office
Benton County Sheriff's Office
Benzie County Sheriff's Office
Bernalillo County Sheriff's Office
Bexar County Sheriff's Office
Bibb County Sheriff's Office
Bibb County Sheriff's Office
Big Horn County Sheriff's Office
Blackford County Sheriff's Office
Bolivar County Sheriff's Office
Boone County Sheriff's Office
Bossier Parish Sheriff's Office

Acadia
Accomack
Adams
Alachua
Alameda
Allegan
Allegheny
Anderson
Anoka
Arapahoe
Assumption
Atlantic
Baker
Baltimore City
Baltimore
Barnstable
Barrow
Baxter
Bay
Beaufort
Bedford
Benton
Benzie
Bernalillo
Bexar
Bibb
Bibb
Big Horn
Blackford
Bolivar
Boone
Bossier

LA
VA
CO
FL
CA
MI
PA
SC
MN
CO
LA
NJ
GA
MD
MD
MA
GA
AR
FL
SC
VA
AR
MI
NM
TX
AL
GA
MT
IN
MS
KY
LA

105
27
364
276
928
59
151
191
133
407
50
103
3
99
84
254
127
32
213
209
77
103
13
279
526
12
290
8
8
18
130
300

Per
Agency

Per 1,000
Officers
1
1
2
1
2
1
3
2
1
2
2
1
1
2
1
5
1
1
2
1
2
1
1
3
5
1
3
1
1
1
1
3

9.52
37.04
5.49
3.62
2.16
16.95
19.87
10.47
7.52
4.91
40.00
9.71
333.33
20.20
11.90
19.69
7.87
31.25
9.39
4.78
25.97
9.71
76.92
10.75
9.51
83.33
10.34
125.00
125.00
55.56
7.69
10.00

Per 100,000
Population
1.62
3.02
0.45
0.40
0.13
0.90
0.25
1.07
0.30
0.35
8.54
0.36
28.98
0.32
0.12
2.32
1.44
2.41
1.18
0.62
2.91
0.45
5.71
0.45
0.29
4.36
1.93
7.77
7.83
2.93
0.84
2.56

370
Boulder County Sheriff's Office
Bracken County Sheriff's Office
Brazoria County Sheriff's Office
Brevard County Sheriff's Office
Broward County Sheriff's Office
Brunswick County Sheriff's Office
Bucks County Sheriff's Office
Bullitt County Sheriff's Office
Burke County Sheriff's Office
Burleigh County Sheriff's Office
Butler County Sheriff's Office
Butte County Sheriff's Office
Calcasieu Parish Sheriff's Office
Calhoun County Sheriff's Office
Cameron County Sheriff's Office
Campbell County Sheriff's Office
Caroline County Sheriff's Office
Carter County Sheriff's Office
Carver County Sheriff's Office
Catawba County Sheriff's Office
Centre County Sheriff's Office
Chambers County Sheriff's Office
Charleston County Sheriff's Office
Charlotte County Sheriff's Office
Cherokee County Sheriff's Office
Chesapeake (City) Sheriff's Office
Chesterfield County Sheriff's Office
Chesterfield County Sheriff's Office
Clackamas County Sheriff's Office
Clark County Sheriff's Office
Clark County Sheriff's Office
Clarke County Sheriff's Office
Clay County Sheriff's Office
Clay County Sheriff's Office
Clay County Sheriff's Office
Clay County Sheriff's Office

Boulder
Bracken
Brazoria
Brevard
Broward
Brunswick
Bucks
Bullitt
Burke
Burleigh
Butler
Butte
Calcasieu
Calhoun
Cameron
Campbell
Caroline
Carter
Carver
Catawba
Centre
Chambers
Charleston
Charlotte
Cherokee
Chesapeake City
Chesterfield
Chesterfield
Clackamas
Clark
Clark
Clarke
Clay
Clay
Clay
Clay

CO
KY
TX
FL
FL
NC
PA
KY
NC
ND
AL
CA
LA
FL
TX
WY
VA
MO
MN
NC
PA
TX
SC
FL
SC
VA
SC
VA
OR
IN
OH
VA
AR
FL
IN
MO

174
4
127
497
1624
114
50
40
86
40
9
110
592
18
107
58
49
3
87
126
17
44
259
285
48
358
41
225
319
35
134
18
7
284
30
115

1
1
1
2
16
2
2
1
1
2
3
1
1
1
2
1
3
2
1
1
1
1
4
3
2
2
1
1
2
1
1
1
1
3
1
1

5.75
250.00
7.87
4.02
9.85
17.54
40.00
25.00
11.63
50.00
333.33
9.09
1.69
55.56
18.69
17.24
61.22
666.67
11.49
7.94
58.82
22.73
15.44
10.53
41.67
5.59
24.39
4.44
6.27
28.57
7.46
55.56
142.86
10.56
33.33
8.70

0.34
11.78
0.32
0.37
0.92
1.86
0.32
1.35
1.10
2.46
14.32
0.45
0.52
6.84
0.49
2.17
10.51
31.92
1.10
0.65
0.65
2.85
1.14
1.88
3.61
0.90
2.14
0.32
0.53
0.91
0.72
7.13
6.22
1.57
3.72
0.45

371
Clayton County Sheriff's Office
Cleveland County Sheriff's Office
Cobb County Sheriff's Office
Collier County Sheriff's Office
Columbia County Sheriff's Office
Columbia County Sheriff's Office
Columbiana County Sheriff's Office
Contra Costa County Sheriff's Office
Cook County Sheriff's Office
Coweta County Sheriff's Office
Crawford County Sheriff's Office
Cross County Sheriff's Office
Cullman County Sheriff's Office
Custer County Sheriff's Office
Cuyahoga County Sheriff's Office
Dade County Sheriff's Office
Dakota County Sheriff's Office
Dale County Sheriff's Office
Dallas County Sheriff's Office
Dallas County Sheriff's Office
Dane County Sheriff's Office
Darlington County Sheriff's Office
Dauphin County Sheriff's Office
Davie County Sheriff's Office
Daviess County Sheriff's Office
Dearborn County Sheriff's Office
DeKalb County Sheriff's Office
DeKalb County Sheriff's Office
Del Norte County Sheriff's Office
Delaware County Sheriff's Office
Delaware County Sheriff's Office
Denver County Sheriff's Office
Dewitt County Sheriff's Office
Douglas County Sheriff's Office
Duval County Sheriff's Office
East Baton Rouge Parish Sheriff's Office

Clayton
Cleveland
Cobb
Collier
Columbia
Columbia
Columbiana
Contra Costa
Cook
Coweta
Crawford
Cross
Cullman
Custer
Cuyahoga
Dade
Dakota
Dale
Dallas
Dallas
Dane
Darlington
Dauphin
Davie
Daviess
Dearborn
DeKalb
DeKalb
Del Norte
Delaware
Delaware
Denver
De Witt
Douglas
Duval
East Baton Rouge

IA
OK
GA
FL
GA
OR
OH
CA
IL
GA
AR
AR
AL
OK
OH
GA
MN
AL
IA
TX
WI
SC
PA
NC
IN
IN
AL
GA
CA
OH
PA
CO
IL
NV
TX
LA

12
70
435
628
206
41
22
679
5655
137
27
17
78
12
141
25
77
0
23
449
454
64
36
45
19
29
34
325
30
86
58
739
16
100
18
359

1
1
1
6
2
1
2
1
8
1
1
1
1
1
3
1
1
1
2
1
2
1
2
3
1
2
1
2
2
1
2
5
1
1
1
3

83.33
14.29
2.30
9.55
9.71
24.39
90.91
1.47
1.41
7.30
37.04
58.82
12.82
83.33
21.28
40.00
12.99
86.96
2.23
4.41
15.63
55.56
66.67
52.63
68.97
29.41
6.15
66.67
11.63
34.48
6.77
62.50
10.00
55.56
8.36

5.52
0.39
0.15
1.87
1.61
2.03
1.85
0.10
0.15
0.79
1.61
5.60
1.24
3.64
0.23
0.04
0.25
1.99
3.02
0.04
0.41
1.46
0.75
7.27
3.16
4.00
1.41
0.29
6.99
0.57
0.36
0.83
6.04
2.13
8.49
0.68

372
Eaton County Sheriff's Office
El Paso County Sheriff's Office
El Paso County Sheriff's Office
Elko County Sheriff's Office
Emery County Sheriff's Office
Emmet County Sheriff's Office
Escambia County Sheriff's Office
Estill County Sheriff's Office
Fairfax County Sheriff's Office
Fayette County Sheriff's Office
Ferry County Sheriff's Office
Flagler County Sheriff's Office
Floyd County Sheriff's Office
Forsyth County Sheriff's Office
Forsyth County Sheriff's Office
Franklin County Sheriff's Office
Franklin County Sheriff's Office
Franklin County Sheriff's Office
Frederick County Sheriff's Office
Fremont County Sheriff's Office
Fresno County Sheriff's Office
Fulton County Sheriff's Office
Gage County Sheriff's Office
Gallatin County Sheriff's Office
Galveston County Sheriff's Office
Graham County Sheriff's Office
Grand Traverse County Sheriff's Office
Grant County Sheriff's Office
Grant County Sheriff's Office
Greenbrier County Sheriff's Office
Greene County Sheriff's Office
Greene County Sheriff's Office
Greenville County Sheriff's Office
Griggs County Sheriff's Office
Guadalupe County Sheriff's Office
Gwinnett County Sheriff's Office

Eaton
El Paso
El Paso
Elko
Emery
Emmet
Escambia
Estill
Fairfax
Fayette
Ferry
Flagler
Floyd
Forsyth
Forsyth
Franklin
Franklin
Franklin
Frederick
Fremont
Fresno
Fulton
Gage
Gallatin
Galveston
Graham
Grand Traverse
Grant
Grant
Greenbrier
Greene
Greene
Greenville
Griggs
Guadalupe
Gwinnett

MI
CO
TX
NV
UT
MI
FL
KY
VA
OH
WA
FL
IA
GA
NC
IN
OH
VA
MD
IA
CA
GA
NE
IL
TX
AZ
MI
IN
OK
WV
AL
AR
SC
ND
NM
GA

77
454
248
57
26
23
388
5
499
22
10
130
11
253
217
12
455
78
177
7
461
516
12
3
240
23
64
45
5
30
11
15
397
3
3
531

1
1
2
1
2
1
3
1
3
3
1
2
1
5
2
1
2
1
1
1
1
4
1
1
1
1
2
1
1
1
1
1
2
1
1
3

12.99
2.20
8.06
17.54
76.92
43.48
7.73
200.00
6.01
136.36
100.00
15.38
90.91
19.76
9.22
83.33
4.40
12.82
5.65
142.86
2.17
7.75
83.33
333.33
4.17
43.48
31.25
22.22
200.00
33.33
90.91
66.67
5.04
333.33
333.33
5.65

0.93
0.16
0.25
2.05
18.22
3.06
1.01
6.82
0.28
10.33
13.24
2.09
6.13
2.85
0.57
4.33
0.17
1.78
0.43
13.44
0.11
0.43
4.48
17.89
0.34
2.69
2.30
1.43
22.09
2.82
11.06
2.38
0.44
41.32
21.34
0.37

373
Halifax County Sheriff's Office
Hall County Sheriff's Office
Hamilton County Sheriff's Office
Hamilton County Sheriff's Office
Hamilton County Sheriff's Office
Hampton County Sheriff's Office
Hancock County Sheriff's Office
Hardeman County Sheriff's Office
Harford County Sheriff's Office
Harris County Sheriff's Office
Harrison County Sheriff's Office
Harrison County Sheriff's Office
Harrison County Sheriff's Office
Hawkins County Sheriff's Office
Haywood County Sheriff's Office
Hempstead County Sheriff's Office
Hendry County Sheriff's Office
Henry County Sheriff's Office
Henry County Sheriff's Office
Hernando County Sheriff's Office
Hidalgo County Sheriff's Office
Highlands County Sheriff's Office
Hillsborough County Sheriff's Office
Hockley County Sheriff's Office
Hoke County Sheriff's Office
Holmes County Sheriff's Office
Holt County Sheriff's Office
Horry County Sheriff's Office
Hudson County Sheriff's Office
Hunterdon County Sheriff's Office
Iberia Parish Sheriff's Office
Indian River County Sheriff's Office
Isle of Wight County Sheriff's Office
Jackson County Sheriff's Office
Jackson County Sheriff's Office
Jackson County Sheriff's Office

Halifax
Hall
Hamilton
Hamilton
Hamilton
Hampton
Hancock
Hardeman
Harford
Harris
Harrison
Harrison
Harrison
Hawkins
Haywood
Hempstead
Hendry
Henry
Henry
Hernando
Hidalgo
Highlands
Hillsborough
Hockley
Hoke
Holmes
Holt
Horry
Hudson
Hunterdon
Iberia
Indian River
Isle of Wight
Jackson
Jackson
Jackson

VA
GA
IN
OH
TN
SC
IN
TN
MD
TX
IN
MS
TX
TN
TN
AR
FL
AL
VA
FL
TX
FL
FL
TX
NC
OH
NE
SC
NJ
NJ
LA
FL
VA
FL
KS
TN

29
257
64
330
146
26
40
22
280
2558
22
90
45
42
22
13
67
8
112
249
262
130
1223
11
50
33
5
55
221
17
242
226
40
61
15
14

1
2
1
1
2
1
3
1
1
3
1
1
1
1
1
1
1
1
14
1
5
1
10
2
1
1
1
1
3
1
6
3
1
1
1
3

34.48
7.78
15.63
3.03
13.70
38.46
75.00
45.45
3.57
1.17
45.45
11.11
22.22
23.81
45.45
76.92
14.93
125.00
125.00
4.02
19.08
7.69
8.18
181.82
20.00
30.30
200.00
18.18
13.57
58.82
24.79
13.27
25.00
16.39
66.67
214.29

2.76
1.11
0.36
0.12
0.59
4.74
4.29
3.67
0.41
0.07
2.54
0.53
1.52
1.76
5.32
4.42
2.55
5.78
25.85
0.58
0.65
1.01
0.81
8.72
2.13
2.36
9.58
0.37
0.47
0.78
8.19
2.17
2.84
2.01
7.43
25.78

374
Jefferson Parish Sheriff's Office
Jim Wells County Sheriff's Office
Johnston County Sheriff's Office
Juneau County Sheriff's Office
Kane County Sheriff's Office
Kenosha County Sheriff's Office
Kent County Sheriff's Office
Keokuk County Sheriff's Office
Kern County Sheriff's Office
Kershaw County Sheriff's Office
King County Sheriff's Office
Kingfisher County Sheriff's Office
Kitsap County Sheriff's Office
Knox County Sheriff's Office
La Crosse County Sheriff's Office
La Porte County Sheriff's Office
La Salle County Sheriff's Office
La Salle County Sheriff's Office
Lafourche Parish Sheriff's Office
Lake County Sheriff's Office
Lake County Sheriff's Office
Lake County Sheriff's Office
Lake County Sheriff's Office
Lake County Sheriff's Office
Lake County Sheriff's Office
Lancaster County Sheriff's Office
Laurens County Sheriff's Office
Lawrence County Sheriff's Office
Lee County Sheriff's Office
Lee County Sheriff's Office
Lee County Sheriff's Office
Lenawee County Sheriff's Office
Lenoir County Sheriff's Office
Leon County Sheriff's Office
Lewis County Sheriff's Office
Liberty County Sheriff's Office

Jefferson
Jim Wells
Johnston
Juneau
Kane
Kenosha
Kent
Keokuk
Kern
Kershaw
King
Kingfisher
Kitsap
Knox
La Crosse
La Porte
La Salle
La Salle
Lafourche
Lake
Lake
Lake
Lake
Lake
Lake
Lancaster
Laurens
Lawrence
Lee
Lee
Lee
Lenawee
Lenoir
Leon
Lewis
Liberty

LA
TX
NC
WI
IL
WI
DE
IA
CA
SC
WA
OK
WA
TN
WI
IN
IL
TX
LA
CA
CO
FL
IL
IN
SD
SC
SC
IN
FL
NC
SC
MI
NC
FL
MO
TX

825
27
97
45
92
122
5
4
512
61
721
8
121
456
43
58
36
10
287
61
9
286
188
170
5
93
65
25
621
47
28
44
58
443
5
37

12
1
1
1
1
1
1
1
11
1
5
1
1
1
1
1
1
3
3
2
2
3
1
5
1
1
1
1
7
1
1
1
2
3
1
2

14.55
37.04
10.31
22.22
10.87
8.20
200.00
250.00
21.48
16.39
6.93
125.00
8.26
2.19
23.26
17.24
27.78
300.00
10.45
32.79
222.22
10.49
5.32
29.41
200.00
10.75
15.38
40.00
11.27
21.28
35.71
22.73
34.48
6.77
200.00
54.05

2.77
2.45
0.59
3.75
0.19
0.60
0.62
9.51
1.31
1.62
0.26
6.65
0.40
0.23
0.87
0.90
0.88
43.57
3.11
3.09
27.36
1.01
0.14
1.01
8.93
1.30
1.50
2.17
1.13
1.73
5.20
1.00
3.36
1.09
9.79
2.64

375
Lincoln County Sheriff's Office
Lincoln County Sheriff's Office
Lincoln County Sheriff's Office
Lincoln County Sheriff's Office
Lincoln Parish Sheriff's Office
Livingston County Sheriff's Office
Livingston Parish Sheriff's Office
Long County Sheriff's Office
Los Angeles County Sheriff's Office
Loudoun County Sheriff's Office
Lowndes County Sheriff's Office
Lowndes County Sheriff's Office
Lucas County Sheriff's Office
Luna County Sheriff's Office
Luzerne County Sheriff's Office
Macomb County Sheriff's Office
Madera County Sheriff's Office
Madison County Sheriff's Office
Madison County Sheriff's Office
Madison County Sheriff's Office
Manatee County Sheriff's Office
Marathon County Sheriff's Office
Maricopa County Sheriff's Office
Marin County Sheriff's Office
Marion County Sheriff's Office
Marion County Sheriff's Office
Marion County Sheriff's Office
Marion County Sheriff's Office
Marlboro County Sheriff's Office
Martin County Sheriff's Office
Martin County Sheriff's Office
Mason County Sheriff's Office
McDowell County Sheriff's Office
McDowell County Sheriff's Office
McHenry County Sheriff's Office
McIntosh County Sheriff's Office

Lincoln
Lincoln
Lincoln
Lincoln
Lincoln
Livingston
Livingston
Long
Los Angeles
Loudoun
Lowndes
Lowndes
Lucas
Luna
Luzerne
Macomb
Madera
Madison
Madison
Madison
Manatee
Marathon
Maricopa
Marin
Marion
Marion
Marion
Marion
Marlboro
Martin
Martin
Mason
McDowell
McDowell
McHenry
McIntosh

GA
NC
OR
SD
LA
MI
LA
GA
CA
VA
AL
GA
OH
NM
PA
MI
CA
AL
GA
MO
FL
WI
AZ
CA
FL
IN
OR
WV
SC
FL
NC
WV
NC
WV
IL
OK

12
98
65
17
46
65
125
15
9461
448
14
145
289
30
38
245
78
107
34
8
476
67
766
202
349
740
255
27
25
414
34
21
40
15
134
17

1
4
1
1
1
2
1
1
14
2
1
1
4
1
2
1
1
1
1
1
4
1
2
1
3
7
1
1
2
1
1
3
1
1
2
2

83.33
40.82
15.38
58.82
21.74
30.77
8.00
66.67
1.48
4.46
71.43
6.90
13.84
33.33
52.63
4.08
12.82
9.35
29.41
125.00
8.40
14.93
2.61
4.95
8.60
9.46
3.92
37.04
80.00
2.42
29.41
142.86
25.00
66.67
14.93
117.65

12.51
5.11
2.17
2.23
2.14
1.11
0.78
6.91
0.14
0.64
8.85
0.92
0.91
3.98
0.62
0.12
0.66
0.30
3.56
8.18
1.24
0.75
0.05
0.40
0.91
0.77
0.32
1.77
6.91
0.68
4.08
10.98
2.22
4.52
0.65
9.88

376
McKinley County Sheriff's Office
Mesilla Marshal's Office
Middlesex County Sheriff's Department
Middlesex County Sheriff's Office
Miller County Sheriff's Office
Milwaukee County Sheriff's Office
Minnehaha County Sheriff's Office
Monmouth County Sheriff's Office
Monroe County Sheriff's Office
Monroe County Sheriff's Office
Monroe County Sheriff's Office
Montague County Sheriff's Office
Montgomery County Sheriff's Office
Montgomery County Sheriff's Office
Mora County Sheriff's Office
Morgan County Sheriff's Office
Morgan County Sheriff's Office
Morris County Sheriff's Office
Morrow County Sheriff's Office
Muskogee County Sheriff's Office
Nassau County Sheriff's Office
New Hanover County Sheriff's Office
New York City Sheriff's Office
Newaygo County Sheriff's Office
Newton County Sheriff's Office
Nez Perce County Sheriff's Office
Niagara County Sheriff's Office
Nicholas County Sheriff's Office
Norfolk (City) Sheriff's Office
Nye County Sheriff's Office
Oglethorpe County Sheriff's Office
Okaloosa County Sheriff's Office
Okanogan County Sheriff's Office
Onondaga County Sheriff's Office
Orange County Sheriff-Coroner Department
Orange County Sheriff's Office

McKinley
Dona Ana
Middlesex
Middlesex
Miller
Milwaukee
Minnehaha
Monmouth
Monroe
Monroe
Monroe
Montague
Montgomery
Montgomery
Mora
Morgan
Morgan
Morris
Morrow
Muskogee
Nassau
New Hanover
New York
Newaygo
Newton
Nez Perce
Niagara
Nicholas
Norfolk City
Nye
Oglethorpe
Okaloosa
Okanogan
Onondaga
Orange
Orange

NM
NM
NJ
VA
MO
WI
SD
NJ
FL
NY
WI
TX
OH
TN
NM
AL
IN
NJ
OR
OK
FL
NC
NY
MI
GA
ID
NY
KY
VA
NV
GA
FL
WA
NY
CA
FL

35
5
170
17
16
524
69
494
189
273
23
10
222
287
5
55
26
262
14
37
109
315
120
25
152
25
110
1
414
108
19
258
30
242
1794
1398

1
1
4
1
1
7
1
1
3
3
1
1
1
1
1
1
2
1
1
1
2
3
2
1
1
2
1
1
1
3
1
3
1
1
14
16

28.57
200.00
23.53
58.82
62.50
13.36
14.49
2.02
15.87
10.99
43.48
100.00
4.50
3.48
200.00
18.18
76.92
3.82
71.43
27.03
18.35
9.52
16.67
40.00
6.58
80.00
9.09
1000.00
2.42
27.78
52.63
11.63
33.33
4.13
7.80
11.44

1.40
0.48
0.49
9.12
4.04
0.74
0.59
0.16
4.10
0.40
2.24
5.07
0.19
0.58
20.49
0.84
2.90
0.20
8.95
1.41
2.73
1.48
2.44
2.06
1.00
5.09
0.46
14.02
0.41
6.83
6.71
1.66
2.43
0.21
0.47
1.40

377
Orange County Sheriff's Office
Orange County Sheriff's Office
Orangeburg County Sheriff's Office
Oregon County Sheriff's Office
Orleans County Sheriff's Office
Orleans Parish Sheriff's Office (Criminal Division)
Osceola County Sheriff's Office
Osceola County Sheriff's Office
Otero County Sheriff's Office
Ouachita Parish Sheriff's Office
Outagamie County Sheriff's Office
Ozaukee County Sheriff's Office
Page County Sheriff's Office
Palm Beach County Sheriff's Office
Pasco County Sheriff's Office
Passaic County Sheriff's Office
Paulding County Sheriff's Office
Pawnee County Sheriff's Office
Pender County Sheriff's Office
Pennington County Sheriff's Office
Perry County Sheriff's Office
Petroleum County Sheriff's Office
Phillips County Sheriff's Office
Pierce County Sheriff's Office
Pima County Sheriff's Dept.
Pinal County Sheriff's Office
Pinellas County Sheriff's Office
Pitt County Sheriff's Office
Placer County Sheriff's Office
Plaquemines Parish Sheriff's Office
Pocahontas County Sheriff's Office
Polk County Sheriff's Office
Polk County Sheriff's Office
Polk County Sheriff's Office
Portage County Sheriff's Office
Porter County Sheriff's Office

Orange
Orange
Orangeburg
Oregon
Orleans
Orleans
Osceola
Osceola
Otero
Ouachita
Outagamie
Ozaukee
Page
Palm Beach
Pasco
Passaic
Paulding
Pawnee
Pender
Pennington
Perry
Petroleum
Phillips
Pierce
Pima
Pinal
Pinellas
Pitt
Placer
Plaquemines
Pocahontas
Polk
Polk
Polk
Portage
Porter

NY
NC
SC
MO
NY
LA
FL
MI
NM
LA
WI
WI
VA
FL
FL
NJ
GA
OK
NC
SD
IL
MT
AR
WA
AZ
AZ
FL
NC
CA
LA
WV
FL
GA
IA
OH
IN

99
115
92
5
29
505
388
18
30
431
74
83
49
1447
485
530
175
19
57
65
12
1
16
292
554
218
863
125
228
145
5
600
27
143
66
61

1
1
1
2
1
4
2
1
1
1
1
1
1
12
4
6
1
1
1
1
1
1
1
2
1
2
8
3
1
1
1
13
2
3
1
2

10.10
8.70
10.87
400.00
34.48
7.92
5.15
55.56
33.33
2.32
13.51
12.05
20.41
8.29
8.25
11.32
5.71
52.63
17.54
15.38
83.33
1000.00
62.50
6.85
1.81
9.17
9.27
24.00
4.39
6.90
200.00
21.67
74.07
20.98
15.15
32.79

0.27
0.75
1.08
18.38
2.33
1.16
0.74
4.25
1.57
0.65
0.57
1.16
4.16
0.91
0.86
1.20
0.70
6.03
1.92
0.99
4.47
202.43
4.60
0.25
0.10
0.53
0.87
1.78
0.29
4.34
11.47
2.16
4.82
0.70
0.62
1.22

378
Pottawattamie County Sheriff's Office
Potter County Sheriff's Office
Powell County Sheriff's Office
Prince George's County Sheriff's Office
Prince William County Sheriff's Office
Pulaski County Sheriff's Office
Putnam County Sheriff's Office
Queen Anne's County Sheriff's Office
Ramsey County Sheriff's Office
Randolph County Sheriff's Office
Rapides Parish Sheriff's Office
Ravalli County Sheriff's Office
Richland County Sheriff's Office
Richmond (City) Sheriff's Office
Richmond County Sheriff's Office
Rio Arriba County Sheriff's Office
Riverside County Sheriff's Office
Robeson County Sheriff's Office
Rock County Sheriff's Office
Rockingham County Sheriff's Office
Roosevelt County Sheriff's Office
Routt County Sheriff's Office
Russell County Sheriff's Office
Rutherford County Sheriff's Office
Sacramento County Sheriff's Office
Saginaw County Sheriff's Office
Salem County Sheriff's Office
Saline County Sheriff's Office
Saline County Sheriff's Office
San Benito County Sheriff's Office
San Bernardino County Sheriff's Office
San Diego County Sheriff's Office
San Jacinto County Sheriff's Office
San Juan County Sheriff's Office
San Luis Obispo County Sheriff's Office
Sandoval County Sheriff's Office

Pottawattomie
Potter
Powell
Prince Georges
Prince William
Pulaski
Putnam
Queen Annes
Ramsey
Randolph
Rapides
Ravalli
Richland
Richmond City
Richmond
Rio Arriba
Riverside
Robeson
Rock
Rockingham
Roosevelt
Routt
Russell
Rutherford
Sacramento
Saginaw
Salem
Saline
Saline
San Benito
San Bernardino
San Diego
San Jacinto
San Juan
San Luis Obispo
Sandoval

IA
TX
MT
MD
VA
IL
TN
MD
MN
IL
LA
MT
SC
VA
GA
NM
CA
NC
WI
VA
MT
CO
AL
TN
CA
MI
NJ
IL
KS
CA
CA
CA
TX
NM
CA
NM

50
93
10
233
75
11
58
50
235
13
212
29
512
424
449
22
2147
128
94
46
11
22
32
189
1409
71
157
42
44
29
1797
1322
21
94
156
45

1
2
1
3
1
2
2
1
2
1
1
1
3
2
5
1
11
3
2
1
1
1
2
2
6
1
1
1
1
1
11
7
1
2
2
1

20.00
21.51
100.00
12.88
13.33
181.82
34.48
20.00
8.51
76.92
4.72
34.48
5.86
4.72
11.14
45.45
5.12
23.44
21.28
21.74
90.91
45.45
62.50
10.58
4.26
14.08
6.37
23.81
22.73
34.48
6.12
5.30
47.62
21.28
12.82
22.22

1.07
1.65
14.23
0.35
0.25
32.46
2.77
2.09
0.39
2.99
0.76
2.49
0.78
0.98
2.49
2.48
0.50
2.24
1.25
1.31
9.59
4.25
3.78
0.76
0.42
0.50
1.51
4.01
1.80
1.81
0.54
0.23
3.79
1.54
0.74
0.76

379
Santa Barbara County Sheriff's Office
Santa Clara County Sheriff's Office
Santa Fe County Sheriff's Office
Santa Rosa County Sheriff's Office
Sarasota County Sheriff's Office
Sauk County Sheriff's Office
Seminole County Sheriff's Office
Seneca County Sheriff's Office
Shelby County Sheriff's Office
Shelby County Sheriff's Office
Simpson County Sheriff's Office
Smith County Sheriff's Office
Snohomish County Sheriff's Office
Socorro County Sheriff's Office
Solano County Sheriff's Office
Spartanburg County Sheriff's Office
Spokane County Sheriff's Office
St. Bernard Parish Sheriff's Office
St. Charles County Sheriff's Office
St. Charles Parish Sheriff's Office
St. Croix County Sheriff's Office
St. Helena Parish Sheriff's Office
St. John The Baptist Parish Sheriff's Office
St. Joseph County Sheriff's Office
St. Louis County Sheriff's Office
St. Lucie County Sheriff's Office
St. Martin Parish Sheriff's Office
St. Mary's County Sheriff's Office
St. Tammany Parish Sheriff's Office
Stanislaus County Sheriff's Office
Stanly County Sheriff's Office
Stark County Sheriff's Office
Starr County Sheriff's Office
Stephens County Sheriff's Office
Stephens County Sheriff's Office
Steuben County Sheriff's Office

Santa Barbara
Santa Clara
Santa Fe
Santa Rosa
Sarasota
Sauk
Seminole
Seneca
Shelby
Shelby
Simpson
Smith
Snohomish
Socorro
Solano
Spartanburg
Spokane
St. Bernard
St. Charles
St. Charles
St. Croix
St. Helena
St. John the Baptist
St. Joseph
St. Louis
St. Lucie
St. Martin
St. Marys
St. Tammany
Stanislaus
Stanly
Stark
Starr
Stephens
Stephens
Steuben

CA
CA
NM
FL
FL
WI
FL
NY
OH
TN
KY
TX
WA
NM
CA
SC
WA
LA
MO
LA
WI
LA
LA
IN
MO
FL
LA
MD
LA
CA
NC
ND
TX
GA
OK
NY

294
450
75
190
409
118
355
23
30
516
11
88
287
10
113
297
244
189
153
291
44
22
150
116
200
259
45
120
409
230
49
11
31
31
11
26

1
1
1
1
1
1
2
6
1
8
1
1
2
1
1
2
2
1
2
1
1
1
3
3
4
2
1
1
2
1
1
1
1
1
2
1

3.40
2.22
13.33
5.26
2.44
8.47
5.63
260.87
33.33
15.50
90.91
11.36
6.97
100.00
8.85
6.73
8.20
5.29
13.07
3.44
22.73
45.45
20.00
25.86
20.00
7.72
22.22
8.33
4.89
4.35
20.41
90.91
32.26
32.26
181.82
38.46

0.24
0.06
0.69
0.66
0.26
1.61
0.47
17.02
2.02
0.86
5.77
0.48
0.28
5.60
0.24
0.70
0.42
2.79
0.55
1.89
1.19
8.93
6.53
1.12
0.40
0.72
1.92
0.95
0.86
0.19
1.65
4.13
1.64
3.82
4.44
1.01

380
Sullivan County Sheriff's Office
Sullivan County Sheriff's Office
Summit County Sheriff's Office
Sutter County Sheriff's Office
Talbot County Sheriff's Office
Terrebonne Parish Sheriff's Office
Terrell County Sheriff's Office
Thomas County Sheriff's Office
Towns County Sheriff's Office
Tulare County Sheriff's Office
Tuscola County Sheriff's Office
Ulster County Sheriff's Office
Union County Sheriff's Office
Union County Sheriff's Office
Upshur County Sheriff's Office
Valencia County Sheriff's Office
Ventura County Sheriff's Office
Vernon Parish Sheriff's Office
Vigo County Sheriff's Office
Volusia County Sheriff's Office
Wake County Sheriff's Office
Walker County Sheriff's Office
Walton County Sheriff's Office
Warren County Sheriff's Office
Washington County Sheriff's Office
Washington County Sheriff's Office
Washoe County Sheriff's Office
Washtenaw County Sheriff's Office
Waukesha County Sheriff's Office
Wayne County Sheriff's Office
Wayne County Sheriff's Office
Weld County Sheriff's Office
Westmoreland County Sheriff's Office
White County Sheriff's Office
White County Sheriff's Office
Whitfield County Sheriff's Office

Sullivan
Sullivan
Summit
Sutter
Talbot
Terrebonne
Terrell
Thomas
Towns
Tulare
Tuscola
Ulster
Union
Union
Upshur
Valencia
Ventura
Vernon
Vigo
Volusia
Wake
Walker
Walton
Warren
Washington
Washington
Washoe
Washtenaw
Waukesha
Wayne
Wayne
Weld
Westmoreland
White
White
Whitfield

NY
TN
OH
CA
GA
LA
TX
GA
GA
CA
MI
NY
IN
NC
TX
NM
CA
LA
IN
FL
NC
GA
FL
NC
MD
TN
NV
MI
WI
MI
NC
CO
PA
GA
TN
GA

44
183
393
105
8
300
5
47
18
513
25
57
6
170
45
37
755
64
38
450
354
80
165
30
94
86
414
133
150
1062
85
121
0
40
28
115

1
1
1
1
3
2
1
2
1
3
1
1
1
1
1
1
1
2
1
4
4
2
4
1
1
1
4
4
1
1
2
1
1
1
1
1

22.73
5.46
2.54
9.52
375.00
6.67
200.00
42.55
55.56
5.85
40.00
17.54
166.67
5.88
22.22
27.03
1.32
31.25
26.32
8.89
11.30
25.00
24.24
33.33
10.64
11.63
9.66
30.08
6.67
0.94
23.53
8.26
25.00
35.71
8.70

1.29
0.64
0.18
1.06
43.70
1.79
101.63
4.47
9.55
0.68
1.79
0.55
13.30
0.50
2.54
1.31
0.12
3.82
0.93
0.81
0.44
2.91
7.27
4.77
0.68
0.81
0.95
1.16
0.26
0.05
1.63
0.40
0.27
3.68
3.87
0.97

381
Whitley County Sheriff's Office
Wicomico County Sheriff's Office
Will County Sheriff's Office
Williamson County Sheriff's Office
Williamson County Sheriff's Office
Williamson County Sheriff's Office
Wilson County Sheriff's Office
Winkler County Sheriff's Office
Winn Parish Sheriff's Office
Winnebago County Sheriff's Office
Wood County Sheriff's Office
Woodford County Sheriff's Office
Woodruff County Sheriff's Office
Yakima County Sheriff's Office
Yamhill County Sheriff's Office
Yellowstone County Sheriff's Office
Zapata County Sheriff's Office

Whitley
Wicomico
Will
Williamson
Williamson
Williamson
Wilson
Winkler
Winn
Winnebago
Wood
Woodford
Woodruff
Yakima
Yamhill
Yellowstone
Zapata

KY
MD
IL
IL
TN
TX
TN
TX
LA
IL
WV
IL
AR
WA
OR
MT
TX

12
0
445
29
99
206
202
10
23
142
40
37
6
69
36
55
44

1
1
1
2
2
2
1
1
1
1
1
1
1
2
1
1
1

83.33
2.25
68.97
20.20
9.71
4.95
100.00
43.48
7.04
25.00
27.03
166.67
28.99
27.78
18.18
22.73

2.81
1.01
0.15
3.01
1.09
0.47
0.88
14.06
6.53
0.34
1.15
2.59
13.77
0.82
1.01
0.68
7.13

382
Table 82. County Police Departments in Study: Rates of Officers Arrested, 2005-2011 (Sorted
Alphabetically)
# of Full-Time
Agency
County
State
Sworn Personnel
Allegheny County Police Department.
Anne Arundel County Police Department
Arlington County Police Department
Athens-Clarke County Police Dept
Baltimore County Police Department
Charlotte - Mecklenburg Police Department
Chesterfield County Police Department
Clayton County Police Department
Cobb County Police Department
DeKalb County Police Department
Dougherty County Police Dept.
Fairfax County Police Department
Floyd County Police Department
Fulton County Police Department
Gaston County Police Department
Gwinnett County Police Department
Henrico County Division of Police
Henry County Police Department
Honolulu (City and County) Police Department
Horry County Police Department
Howard County Police Department
Indianapolis Police
James City County Police Dept.
Kauai (County) Police Department
Maui (County) Police Department
Miami-Dade (County) Police Department
Montgomery County Police Department
Nassau County Police Department
New Castle County Police Department
Oldham County Police Department
Polk County Police Department

Allegheny
Anne Arundel
Arlington
Clarke
Baltimore
Mecklenburg
Chesterfield
Clayton
Cobb
DeKalb
Dougherty
Fairfax
Floyd
Fulton
Gaston
Gwinnett
Henrico
Henry
Honolulu
Horry
Howard
Marion
James City
Kauai
Maui
Miami-Dade
Montgomery
Nassau
New Castle
Oldham
Polk

PA
MD
VA
GA
MD
NC
VA
GA
GA
GA
GA
VA
GA
GA
NC
GA
VA
GA
HI
SC
MD
IN
VA
HI
HI
FL
MD
NY
DE
KY
GA

202
633
364
213
1910
1672
475
336
590
1074
47
1419
71
129
133
682
554
225
1934
243
424
1582
94
125
329
3093
1206
2732
358
31
35

Per
Agency
2
3
1
2
4
20
1
3
5
13
2
7
1
2
2
4
4
1
26
9
3
33
1
3
5
25
16
6
2
2
3

Per 1,000
Officers
9.90
4.74
2.75
9.39
2.09
11.96
2.11
8.93
8.47
12.10
42.55
4.93
14.08
15.50
15.04
5.87
7.22
4.44
13.44
37.04
7.08
20.86
10.64
24.00
15.20
8.08
13.27
2.20
5.59
64.52
85.71

Per 100,000
Population
0.16
0.56
0.48
1.71
0.50
2.17
0.32
1.16
0.73
1.88
2.11
0.65
1.04
0.22
0.97
0.50
1.30
0.49
2.73
3.34
1.04
3.65
1.49
4.47
3.23
1.00
1.65
0.45
0.37
3.32
7.23

383
Prince George's County Police Department
Prince William County Police Department
Riley County Police Department
Roanoke County Police Department
Savannah-Chatham Metropolitan Police Department
St. Louis County Police Dept
Suffolk County Police Department
Westchester County Department of Public Safety

Prince Georges
Prince William
Riley
Roanoke
Chatham
St. Louis
Suffolk
Westchester

MD
VA
KS
VA
GA
MO
NY
NY

1578
546
101
135
534
781
2622
270

19
3
1
2
6
2
6
3

12.04
5.49
9.90
14.81
11.24
2.56
2.29
11.11

2.20
0.75
1.41
2.17
2.26
0.20
0.40
0.32

384
Table 83. 500 Largest Municipal Police Departments in Study: Rates of Officers Arrested, 2005-2011 (Sorted Alphabetically by Agency
Name)
# of Full-Time
Per
Per 1,000
Agency
County
State
Sworn Personnel
Agency
Officers
Akron Police Department
Alameda Police Department
Albany Police Department
Albany Police Department
Albuquerque Police Department
Alexandria Police Department
Alhambra Police Department
Allen Police Department
Allentown Police Department
Altamonte Springs Police Dept
Anaheim Police Department
Anchorage Police Department
Anderson Police
Anderson Police Department
Ann Arbor Police Department
Apopka Police Department
Arlington Police Department
Atlanta Police Department
Aurora Police Department
Austin Police Department
Bakersfield Police Department
Baltimore Police Department
Barnstable Police Department
Bartlett Police Department
Baton Rouge Police Department
Baytown Police Department
Beaumont Police Department
Beaverton Police Department
Belleville Police Dept
Bellevue Police Dept
Bend Police Department

Summit
Alameda
Dougherty
Albany
Bernalillo
Alexandria City
Los Angeles
Collin
Lehigh
Seminole
Orange
Anchorage
Madison
Anderson
Washtenaw
Orange
Tarrant
Fulton
Arapahoe
Travis
Kern
Baltimore City
Barnstable
Shelby
East Baton Rouge
Harris
Jefferson
Washington
St. Clair
Sarpy
Deschutes

OH
CA
GA
NY
NM
VA
CA
TX
PA
FL
CA
AK
IN
SC
MI
FL
TX
GA
CO
TX
CA
MD
MA
TN
LA
TX
TX
OR
IL
NE
OR

472
94
184
328
1020
315
83
106
200
124
398
372
117
91
160
85
610
1719
627
1515
348
2990
110
105
630
136
246
124
81
92
86

8
2
4
7
13
3
1
2
1
1
3
4
4
3
4
1
3
22
6
10
8
55
2
1
6
1
2
1
1
1
2

16.95
21.28
21.74
21.34
12.75
9.52
12.05
18.87
5.00
8.06
7.54
10.75
34.19
32.97
25.00
11.76
4.92
12.80
9.57
6.60
22.99
18.39
18.18
9.52
9.52
7.35
8.13
8.06
12.35
10.87
23.26

Per 100,000
Population
1.48
0.13
4.23
2.30
1.96
2.14
0.01
0.26
0.29
0.24
0.10
1.37
3.04
1.60
1.16
0.09
0.17
2.39
1.05
0.98
0.95
8.86
0.93
0.11
1.36
0.02
0.79
0.19
0.37
0.63
1.27

385
Bensalem Township Police Dept
Berkeley Police Department
Berwyn Police Dept
Bethlehem Police Department
Billings Police Department
Biloxi Police Department
Binghamton Police Department
Birmingham Police Department
Bloomington Police Dept
Boise Police Department
Bolingbrook Police Dept
Bossier City Police Dept
Boston Police Department
Boulder Police Department
Boynton Beach Police Department
Brick Township Police
Bridgeport Police Department
Bristol Police Department
Brockton Police Department
Broken Arrow Police Department
Brookline Police Department
Broomfield Police Department
Brownsville Police Department
Buffalo Police Department
Burlington Police Department
Calumet City Police Dept
Cambridge Police Department
Camden Police
Canton Police Department
Cape Coral Police Department
Carmel Police
Cedar Rapids Police Department
Champaign Police Dept
Chandler Police Department
Charleston Police Department
Charleston Police Department

Bucks
Alameda
Cook
Northampton
Yellowstone
Harrison
Broome
Jefferson
McLean
Ada
Will
Bossier
Suffolk
Boulder
Palm Beach
Ocean
Fairfield
Hartford
Plymouth
Tulsa
Norfolk
Broomfield
Cameron
Erie
Alamance
Cook
Middlesex
Camden
Stark
Lee
Hamilton
Linn
Champaign
Maricopa
Kanawha
Charleston

PA
CA
IL
PA
MT
MS
NY
AL
IL
ID
IL
LA
MA
CO
FL
NJ
CT
CT
MA
OK
MA
CO
TX
NY
NC
IL
MA
NJ
OH
FL
IN
IA
IL
AZ
WV
SC

104
186
105
154
138
133
139
816
122
306
122
178
2181
165
165
127
422
119
197
124
129
105
230
793
105
91
272
397
172
220
104
197
122
333
182
382

1
1
3
4
2
2
1
8
2
2
3
3
14
1
7
1
3
1
3
2
3
1
1
4
3
1
1
10
1
2
3
2
1
2
9
2

9.62
5.38
28.57
25.97
14.49
15.04
7.19
9.80
16.39
6.54
24.59
16.85
6.42
6.06
42.42
7.87
7.11
8.40
15.23
16.13
23.26
9.52
4.35
5.04
28.57
10.99
3.68
25.19
5.81
9.09
28.85
10.15
8.20
6.01
49.45
5.24

0.16
0.07
0.06
1.34
1.35
1.07
0.50
1.21
1.18
0.51
0.44
2.56
1.94
0.34
0.53
0.17
0.33
0.11
0.61
0.33
0.45
1.79
0.25
0.44
1.99
0.02
0.07
1.95
0.27
0.32
1.09
0.95
0.50
0.05
4.66
0.57

386
Charlottesville Police Dept.
Chattanooga Police Department
Chelsea Police Department
Cherry Hill Police
Chesapeake Police Department
Chicago Police Dept
Chico Police Department
Chicopee Police Department
Chula Vista Police Department
Cicero Police Dept
Cincinnati Police Department
Citrus Heights Police Department
Clarksville Police Department
Clearwater Police Department
Cleveland Police Department
Cleveland Police Department
Clifton Police
Coconut Creek Police Department
College Park Police Department
Colonie Town Police Department
Colorado Springs Police Department
Columbia Police Department
Columbia Police Department
Columbus Police Department
Columbus Police Department
Concord Police Department
Concord Police Department
Conroe Police Department
Coral Gables Police Department
Corpus Christi Police Department
Costa Mesa Police Department
Council Bluffs Police Department
Covington Police Department
Cranston Police Department
Cuyahoga Falls Police Department
Dallas Police Department

Charlottesville City
Hamilton
Suffolk
Camden
Chesapeake City
Cook
Butte
Hampden
San Diego
Cook
Hamilton
Sacramento
Montgomery
Pinellas
Bradley
Cuyahoga
Passaic
Broward
Fulton
Albany
El Paso
Richland
Boone
Muscogee
Franklin
Cabarrus
Contra Costa
Montgomery
Miami-Dade
Nueces
Orange
Pottawattomie
Kenton
Providence
Summit
Dallas

VA
TN
MA
NJ
VA
IL
CA
MA
CA
IL
OH
CA
TN
FL
TN
OH
NJ
FL
GA
NY
CO
SC
MO
GA
OH
NC
CA
TX
FL
TX
CA
IA
KY
RI
OH
TX

115
434
92
145
376
13354
88
133
244
146
1082
83
238
255
89
1616
158
89
93
109
668
351
155
400
1886
153
161
99
184
448
158
105
112
148
86
3389

1
14
1
2
3
83
2
1
1
4
10
1
5
1
4
27
1
3
1
2
5
8
1
6
9
1
1
2
2
3
1
1
1
3
2
39

8.70
32.26
10.87
13.79
7.98
6.22
22.73
7.52
4.10
27.40
9.24
12.05
21.01
3.92
44.94
16.71
6.33
33.71
10.75
18.35
7.49
22.79
6.45
15.00
4.77
6.54
6.21
20.20
10.87
6.70
6.33
9.52
8.93
20.27
23.26
11.51

2.30
4.16
0.14
0.39
1.35
1.60
0.91
0.22
0.03
0.08
1.25
0.07
7.54
0.11
4.04
2.11
0.20
0.17
0.11
0.66
0.80
2.08
0.61
3.16
0.77
0.56
0.10
0.44
0.08
0.88
0.03
1.07
0.63
1.81
0.37
1.65

387
Daly City Police Department
Danbury Police Department
Danville Police Department
Davie Police Department
Dayton Police Department
Daytona Beach Police Department
Dearborn Police Department
Decatur Police Department
Decatur Police Dept
Delray Beach Police Department
Denton Police Department
Denver Police Department
Des Moines Police Department
Des Plaines Police Dept
Detroit Police Department
Dothan Police Department
Dover Police Department
Downers Grove Police Dept
Dubuque Police Department
Durham Police Department
East Chicago Police
East Orange Police
East Point Police Department
East Providence Police Department
Eau Claire Police Department
Edinburg Police Department
Edison Police
Edmond Police Department
Egg Harbor Township Police
El Cajon Police Department
El Paso Police Department
Elgin Police Dept
Elizabeth Police
Elk Grove Police Department
Elkhart Police
Elyria Police Department

San Mateo
Fairfield
Pittsylvania
Broward
Montgomery
Volusia
Wayne
Morgan
Macon
Palm Beach
Denton
Denver
Polk
Cook
Wayne
Houston
Kent
Du Page
Dubuque
Durham
Lake
Essex
Fulton
Providence
Eau Claire
Hidalgo
Middlesex
Oklahoma
Atlantic
San Diego
El Paso
Kane
Union
Sacramento
Elkhart
Lorain

CA
CT
VA
FL
OH
FL
MI
AL
IL
FL
TX
CO
IA
IL
MI
AL
DE
IL
IA
NC
IN
NJ
GA
RI
WI
TX
NJ
OK
NJ
CA
TX
IL
NJ
CA
IN
OH

113
153
126
164
421
250
198
133
166
152
155
1525
385
102
2250
148
90
81
100
494
110
283
116
98
96
110
189
110
95
120
1132
181
348
126
124
86

1
1
1
2
1
6
4
1
1
1
1
13
6
1
23
2
2
1
3
9
2
3
2
1
1
5
7
2
2
1
15
5
1
1
4
2

8.85
6.54
7.94
12.20
2.38
24.00
20.20
7.52
6.02
6.58
6.45
8.52
15.58
9.80
10.22
13.51
22.22
12.35
30.00
18.22
18.18
10.60
17.24
10.20
10.42
45.45
37.04
18.18
21.05
8.33
13.25
27.62
2.87
7.94
32.26
23.26

0.14
0.11
1.57
0.11
0.19
1.21
0.22
0.84
0.90
0.08
0.15
2.17
1.39
0.02
1.26
1.97
1.23
0.11
3.20
3.36
0.40
0.38
0.22
0.16
1.01
0.65
0.86
0.28
0.73
0.03
1.87
0.97
0.19
0.38
2.02
0.66

388
Enfield Police Department
Erie Police Department
Euclid Police Department
Euless Police Department
Evansville Police
Everett Police Department
Fall River Police Department
Farmington Police Department
Fayetteville Police Department
Fayetteville Police Department
Federal Way Police Department
Flagstaff Police Department
Flint Police Department
Florence Police Department
Florence Police Department
Folsom Police Department
Fort Collins Police Department
Fort Lauderdale Police Department
Fort Myers Police Department
Fort Pierce Police Department
Fort Smith Police Department
Fort Wayne Police
Fort Worth Police Department
Framingham Police Department
Frederick Police Department
Fremont Police Department
Fresno Police Department
Fullerton Police Department
Gainesville Police Department
Galveston Police Department
Garden Grove Police Department
Gary Police
Gastonia Police Department
Glendale Police Department
Gloucester Township Police
Goldsboro Police Department

Hartford
Erie
Cuyahoga
Tarrant
Vanderburgh
Snohomish
Bristol
San Juan
Washington
Cumberland
King
Coconino
Genesee
Florence
Lauderdale
Sacramento
Larimer
Broward
Lee
St. Lucie
Sebastian
Allen
Tarrant
Middlesex
Frederick
Alameda
Fresno
Orange
Alachua
Galveston
Orange
Lake
Gaston
Los Angeles
Camden
Wayne

CT
PA
OH
TX
IN
WA
MA
NM
AR
NC
WA
AZ
MI
SC
AL
CA
CO
FL
FL
FL
AR
IN
TX
MA
MD
CA
CA
CA
FL
TX
CA
IN
NC
CA
NJ
NC

95
167
100
83
277
198
237
129
117
341
133
113
204
109
94
88
162
482
170
126
158
447
1489
118
138
182
828
159
275
161
166
243
166
264
111
101

4
1
1
1
3
1
1
5
2
2
1
1
6
2
1
2
3
6
4
4
3
4
20
1
1
1
7
3
4
1
2
10
2
1
2
1

42.11
5.99
10.00
12.05
10.83
5.05
4.22
38.76
17.09
5.87
7.52
8.85
29.41
18.35
10.64
22.73
18.52
12.45
23.53
31.75
18.99
8.95
13.43
8.47
7.25
5.49
8.45
18.87
14.55
6.21
12.05
41.15
12.05
3.79
18.02
9.90

0.45
0.36
0.08
0.06
1.67
0.14
0.18
3.84
0.98
0.63
0.05
0.74
1.41
1.46
1.08
0.14
1.00
0.34
0.65
1.44
2.39
1.13
1.11
0.07
0.43
0.07
0.75
0.10
1.62
0.34
0.07
2.02
0.97
0.01
0.39
0.82

389
Grand Junction Police Department
Grand Rapids Police Department
Grapevine Police Department
Greece Town Police Department
Greeley Police Department
Greensboro Police Department
Greenville Police Department
Greenville Police Department
Gresham Police Department
Gulfport Police Department
Hackensack Police
Hagerstown Police Department
Hamden Police Department
Hamilton Police Department
Hampton Police Department
Harlingen Police Department
Harrisburg Police Department
Hartford Police Department
Hattiesburg Police Department
Haverhill Police Department
Hayward Police Department
Hempstead Village Police Department
Henderson Police Department
Hialeah Police Department
Hickory Police Department
Hoboken Police
Hollywood Police Department
Holyoke Police Department
Homestead Police Department
Houston Police Department
Huntington Beach Police Department
Huntsville Police Department
Idaho Falls Police Department
Independence Police Department
Inglewood Police Department
Irvine Police Department

Mesa
Kent
Tarrant
Monroe
Weld
Guilford
Greenville
Washington
Multnomah
Harrison
Bergen
Washington
New Haven
Butler
Hampton City
Cameron
Dauphin
Hartford
Forrest
Essex
Alameda
Nassau
Clark
Miami-Dade
Catawba
Hudson
Broward
Hampden
Miami-Dade
Harris
Orange
Madison
Bonneville
Jackson
Los Angeles
Orange

CO
MI
TX
NY
CO
NC
SC
MS
OR
MS
NJ
MD
CT
OH
VA
TX
PA
CT
MS
MA
CA
NY
NV
FL
NC
NJ
FL
MA
FL
TX
CA
AL
ID
MO
CA
CA

108
319
90
94
146
593
178
96
129
193
117
105
105
135
232
123
155
408
126
93
185
112
336
338
114
156
316
123
106
5053
223
405
89
206
187
197

2
1
1
4
2
5
4
2
1
1
9
1
2
1
2
1
1
10
2
1
1
1
2
1
1
3
8
1
3
35
1
3
1
1
1
1

18.52
3.13
11.11
42.55
13.70
8.43
22.47
20.83
7.75
5.18
76.92
9.52
19.05
7.41
8.62
8.13
6.45
24.51
15.87
10.75
5.41
8.93
5.95
2.96
8.77
19.23
25.32
8.13
28.30
6.93
4.48
7.41
11.24
4.85
5.35
5.08

1.36
0.17
0.06
0.54
0.79
1.02
0.89
3.91
0.14
0.53
0.99
0.68
0.23
0.27
1.46
0.25
0.37
1.12
2.67
0.13
0.07
0.07
0.10
0.04
0.65
0.47
0.46
0.22
0.12
0.86
0.03
0.90
0.96
0.15
0.01
0.03

390
Irving Police Department
Irvington Police
Jackson Police Department
Jackson Police Department
Jacksonville Sheriff's Office
Jefferson City Police Department
Jersey City Police
Johnson City Police Department
Joliet Police Dept
Joplin Police Department
Jupiter Police Department
Kalamazoo Dept of Public Safety
Kansas City Police Department
Kansas City Police Department
Kenner Police Department
Kennewick Police Department
Killeen Police Department
Kingsport Police Department
Kissimmee Police Department
Kokomo Police
Lafayette Police
Lafayette Police Department
LaGrange Police Department
Lake Charles Police Department
Lakeland Police Department
Lakewood Police Department
Lancaster Police Department
Lansing Police Department
Laredo Police Department
Largo Police Department
Las Cruces Police Department
Las Vegas Metro Police Department
Lauderhill Police Department
Lawrence Police Department
Lawton Police Department
Lincoln Police Dept

Dallas
Essex
Madison
Hinds
Duval
Cole
Hudson
Washington
Will
Jasper
Palm Beach
Kalamazoo
Wyandotte
Jackson
Jefferson
Benton
Bell
Sullivan
Osceola
Howard
Tippecanoe
Lafayette
Troup
Calcasieu
Polk
Pierce
Lancaster
Ingham
Webb
Pinellas
Dona Ana
Clark
Broward
Essex
Comanche
Lancaster

TX
NJ
TN
MS
FL
MO
NJ
TN
IL
MO
FL
MI
KS
MO
LA
WA
TX
TN
FL
IN
IN
LA
GA
LA
FL
WA
PA
MI
TX
FL
NM
NV
FL
MA
OK
NE

344
190
204
480
1662
88
900
146
302
92
102
244
354
1421
162
90
190
111
136
100
128
243
83
153
226
103
161
240
430
140
167
2942
115
151
165
308

2
2
4
9
12
1
12
1
3
1
1
1
10
5
1
1
3
3
3
1
1
1
1
2
5
1
2
3
4
1
1
8
1
4
1
1

5.81
10.53
19.61
18.75
7.22
11.36
13.33
6.85
9.93
10.87
9.80
4.10
28.25
3.52
6.17
11.11
15.79
27.03
22.06
10.00
7.81
4.12
12.05
13.07
22.12
9.71
12.42
12.50
9.30
7.14
5.99
2.72
8.70
26.49
6.06
3.25

0.08
0.26
4.07
3.67
1.39
1.32
1.89
0.81
0.44
0.85
0.08
0.40
6.35
0.74
0.23
0.57
0.97
1.91
1.12
1.21
0.58
0.45
1.49
1.04
0.83
0.13
0.39
1.07
1.60
0.11
0.48
0.41
0.06
0.54
0.81
0.35

391
Little Rock Police Department
Long Beach Police Department
Longmont Police Department
Lorain Police Department
Los Angeles Police Department
Louisville Metro Police Department
Lowell Police Department
Lubbock Police Department
Lynn Police Department
Macon Police Department
Madison Police Department
Manchester Police Department
Manchester Police Department
Marietta Police Department
McAllen Police Department
Medford Police Department
Medford Police Department
Melbourne Police Department
Memphis Police Department
Mentor Police Department
Merced Police Department
Meriden Police Department
Meridian Police Department
Mesa Police Department
Mesquite Police Department
Miami Beach Police Department
Miami Police Department
Michigan City Police
Middletown Police Department
Midland Police Department
Milford Police Department
Milwaukee Police Department
Minneapolis Police Department
Miramar Police Department
Mission Police Department
Missoula Police Department

Pulaski
Los Angeles
Boulder
Lorain
Los Angeles
Jefferson
Middlesex
Lubbock
Essex
Bibb
Dane
Hartford
Hillsborough
Cobb
Hidalgo
Jackson
Middlesex
Brevard
Shelby
Lake
Merced
New Haven
Lauderdale
Maricopa
Dallas
Miami-Dade
Miami-Dade
La Porte
Middlesex
Midland
New Haven
Milwaukee
Hennepin
Broward
Hidalgo
Missoula

AR
CA
CO
OH
CA
KY
MA
TX
MA
GA
WI
CT
NH
GA
TX
OR
MA
FL
TN
OH
CA
CT
MS
AZ
TX
FL
FL
IN
CT
TX
CT
WI
MN
FL
TX
MT

520
968
136
100
9727
1197
239
376
178
270
437
116
218
133
273
103
110
163
1549
82
105
113
99
831
226
374
1104
86
99
153
110
1987
902
171
125
100

2
4
1
5
30
16
2
5
2
9
1
2
1
1
3
2
1
1
46
1
1
1
3
2
1
3
14
1
2
1
1
73
18
2
2
1

3.85
4.13
7.35
50.00
3.08
13.37
8.37
13.30
11.24
33.33
2.29
17.24
4.59
7.52
10.99
19.42
9.09
6.13
29.70
12.20
9.52
8.85
30.30
2.41
4.42
8.02
12.68
11.63
20.20
6.54
9.09
36.74
19.96
11.70
16.00
10.00

0.52
0.04
0.34
1.66
0.31
2.16
0.13
1.79
0.27
5.79
0.20
0.22
0.25
0.15
0.39
0.98
0.07
0.18
4.96
0.43
0.39
0.12
3.74
0.05
0.04
0.12
0.56
0.90
1.21
0.73
0.12
7.70
1.56
0.11
0.26
0.91

392
Mobile Police Department
Modesto Police Department
Moline Police Department
Monroe Police Department
Montgomery Police Department
Mount Pleasant Police Department
Mount Vernon Police Department
Muncie Police
Murfreesboro Police Department
Muskogee Police Department
Myrtle Beach Police Department
Naperville Police Dept
Nashua Police Department
Nashville Metro Police Department
New Bedford Police Department
New Bern Police Department
New Braunfels Police Department
New Britain Police Department
New Brunswick Police
New Haven Police Department
New London Police Department
New Orleans Police Department
New Rochelle Police Department
New York City Police Department
Newark Police
Newburgh City Police Department
Newport Beach Police Department
Newport News Police Department
Newton Police Department
Niagara Falls Police Department
Norfolk Police Department
North Bergen Police
North Brunswick Police
North Charleston Police Department
North Las Vegas Police Department
Norwalk Police Department

Mobile
Stanislaus
Rock Island
Ouachita
Montgomery
Charleston
Westchester
Delaware
Rutherford
Muskogee
Horry
Du Page
Hillsborough
Davidson
Bristol
Craven
Comal
Hartford
Middlesex
New Haven
New London
Orleans
Westchester
New York
Essex
Orange
Orange
Newport News City
Middlesex
Niagara
Norfolk City
Hudson
Middlesex
Charleston
Clark
Fairfield

AL
CA
IL
LA
AL
SC
NY
IN
TN
OK
SC
IL
NH
TN
MA
NC
TX
CT
NJ
CT
CT
LA
NY
NY
NJ
NY
CA
VA
MA
NY
VA
NJ
NJ
SC
NV
CT

515
262
85
188
500
137
205
102
213
90
179
184
172
1315
288
90
92
160
138
436
90
1425
185
36023
1310
105
140
415
136
145
772
123
83
325
471
167

4
5
1
1
6
1
1
3
5
4
5
1
1
18
2
1
2
2
2
8
2
63
2
196
8
1
1
10
1
3
14
1
1
5
2
5

7.77
19.08
11.76
5.32
12.00
7.30
4.88
29.41
23.47
44.44
27.93
5.43
5.81
13.69
6.94
11.11
21.74
12.50
14.49
18.35
22.22
44.21
10.81
5.44
6.11
9.52
7.14
24.10
7.35
20.69
18.13
8.13
12.05
15.38
4.25
29.94

0.97
0.97
0.68
0.65
2.62
0.29
0.11
2.55
1.90
5.63
1.86
0.11
0.25
2.87
0.36
0.97
1.84
0.22
0.25
0.93
0.73
18.32
0.21
2.40
1.02
0.27
0.03
5.53
0.07
1.39
5.77
0.16
0.12
1.43
0.10
0.55

393
Norwich Police Department
Oak Lawn Police Dept
Oak Park Police Dept
Oakland Police Department
Ocala Police Department
Ocean City Police Department
Oceanside Police Department
O'Fallon Police Department
Ogden Police Department
Oklahoma City Police Department
Olathe Police Department
Omaha Police Dept
Orangetown Town Police Department
Orem Department of Public Safety
Orlando Police Department
Oro Valley Police Department
Ottawa County Sheriff's Office
Oxnard Police Department
Palm Bay Police Department
Palm Beach Gardens Police Department
Palm Springs Police Department
Palo Alto Police Department
Panama City Police Department
Parma Police Department
Pasadena Police Department
Paterson Police
Pawtucket Police Department
Pembroke Pines Police Department
Pensacola Police Department
Peoria Police Department
Peoria Police Dept
Petersburg Police Department
Pharr Police Department
Philadelphia Police Department
Phoenix Police Department
Pine Bluff Police Department

New London
Cook
Cook
Alameda
Marion
Worcester
San Diego
St. Charles
Weber
Oklahoma
Johnson
Douglas
Rockland
Utah
Orange
Pima
Ottawa
Ventura
Brevard
Palm Beach
Riverside
Santa Clara
Bay
Cuyahoga
Harris
Passaic
Providence
Broward
Escambia
Maricopa
Peoria
Petersburg City
Hidalgo
Philadelphia
Maricopa
Jefferson

CT
IL
IL
CA
FL
MD
CA
MO
UT
OK
KS
NE
NY
UT
FL
AZ
MI
CA
FL
FL
CA
CA
FL
OH
TX
NJ
RI
FL
FL
AZ
IL
VA
TX
PA
AZ
AR

81
106
115
773
155
107
210
107
137
1046
161
747
90
90
757
103
115
228
159
114
93
93
86
92
260
497
153
238
146
189
246
82
96
6624
3388
140

5
1
1
3
2
1
4
1
2
12
1
6
1
1
8
1
1
3
1
2
1
1
1
3
5
3
2
1
1
1
1
2
2
66
17
1

61.73
9.43
8.70
3.88
12.90
9.35
19.05
9.35
14.60
11.47
6.21
8.03
11.11
11.11
10.57
9.71
8.70
13.16
6.29
17.54
10.75
10.75
11.63
32.61
19.23
6.04
13.07
4.20
6.85
5.29
4.07
24.39
20.83
9.96
5.02
7.14

1.82
0.02
0.02
0.20
0.60
1.94
0.13
0.28
0.86
1.67
0.18
1.16
0.32
0.19
0.70
0.10
0.38
0.36
0.18
0.15
0.05
0.06
0.59
0.23
0.12
0.60
0.32
0.06
0.34
0.03
0.54
6.17
0.26
4.33
0.45
1.29

394
Pinellas Park Police Department
Piscataway Township Police
Pittsburgh Police Department
Plainfield Police Department
Plano Police Department
Pleasanton Police Department
Port Orange Police Department
Port St. Lucie Police Department
Portland Police Bureau
Portland Police Department
Portsmouth Police Department
Poughkeepsie Police Department
Providence Police Department
Provo Police Department
Pueblo Police Dept
Quincy Police Department
Racine Police Department
Raleigh Police Department
Ramapo Town Police Department
Rapid City Police Department
Redding Police Department
Redondo Beach Police Department
Reno Police Department
Revere Police Department
Richmond Police Department
Richmond Police Department
Riverside Police Department
Riviera Beach Police Department
Roanoke City Police Department
Rochester Police Department
Rochester Police Department
Rock Hill Police Department
Rockford Police Dept
Rocky Mount Police Department
Rogers Police Department
Round Rock Police Department

Pinellas
Middlesex
Allegheny
Union
Collin
Alameda
Volusia
St. Lucie
Multnomah
Cumberland
Portsmouth City
Dutchess
Providence
Utah
Pueblo
Norfolk
Racine
Wake
Rockland
Pennington
Shasta
Los Angeles
Washoe
Suffolk
Contra Costa
Richmond City
Riverside
Palm Beach
Roanoke City
Olmsted
Monroe
York
Winnebago
Nash
Benton
Williamson

FL
NJ
PA
NJ
TX
CA
FL
FL
OR
ME
VA
NY
RI
UT
CO
MA
WI
NC
NY
SD
CA
CA
NV
MA
CA
VA
CA
FL
VA
MN
NY
SC
IL
NC
AR
TX

99
90
891
151
343
85
83
245
928
159
235
105
483
99
195
205
195
702
120
107
118
99
362
86
165
752
385
108
264
125
703
121
300
143
99
133

2
1
21
5
1
1
2
1
10
3
10
1
7
3
4
1
1
8
1
2
1
1
3
1
3
7
4
6
2
1
3
1
1
1
1
1

20.20
11.11
23.57
33.11
2.92
11.76
24.10
4.08
10.78
18.87
42.55
9.52
14.49
30.30
20.51
4.88
5.13
11.40
8.33
18.69
8.47
10.10
8.29
11.63
18.18
9.31
10.39
55.56
7.58
8.00
4.27
8.26
3.33
6.99
10.10
7.52

0.22
0.12
1.72
0.93
0.13
0.07
0.40
0.36
1.36
1.07
10.47
0.34
1.12
0.58
2.51
0.15
0.51
0.89
0.32
1.98
0.56
0.01
0.71
0.14
0.29
3.43
0.18
0.45
2.06
0.69
0.40
0.44
0.34
1.77
0.45
0.24

395
Royal Oak Police Department
Sacramento Police Department
Saginaw Police Department
Salem Police Department
Salem Police Department
Salinas Police Department
Salisbury Police Department
Salisbury Police Department
Salt Lake City Police Department
Salt Lake County Sheriff's Office
San Antonio Police Department
San Bernardino Police Department
San Diego Police Department
San Francisco Police Department
San Jose Police Department
San Leandro Police Department
San Marcos Police Department
Sandy Police Department
Sandy Springs Police Department
Sanford Police Department
Sanford Police Department
Santa Barbara Police Department
Santa Clara Police Department
Santa Fe Police Department
Schenectady Police Department
Scottsdale Police Dept
Scranton Police Department
Seattle Police Department
Shreveport Police Department
Sioux Falls Police Department
Somerville Police Department
South Bend Police
Southampton Town Police Department
Spokane Police Department
Springfield Police Department
Springfield Police Department

Oakland
Sacramento
Saginaw
Essex
Marion
Monterey
Rowan
Wicomico
Salt Lake
Salt Lake
Bexar
San Bernardino
San Diego
San Francisco
Santa Clara
Alameda
Hays
Salt Lake
Fulton
Lee
Seminole
Santa Barbara
Santa Clara
Santa Fe
Schenectady
Maricopa
Lackawanna
King
Caddo
Minnehaha
Middlesex
St. Joseph
Suffolk
Spokane
Hampden
Clark

MI
CA
MI
MA
OR
CA
NC
MD
UT
UT
TX
CA
CA
CA
CA
CA
TX
UT
GA
NC
FL
CA
CA
NM
NY
AZ
PA
WA
LA
SD
MA
IN
NY
WA
MA
OH

85
701
92
83
187
177
86
88
433
342
2020
345
1951
1940
1382
95
95
110
120
81
131
136
141
150
166
417
155
1283
511
221
130
255
102
295
464
127

1
5
1
2
1
4
1
1
1
1
30
2
12
7
9
2
1
1
3
1
1
1
2
2
8
1
4
9
12
1
2
8
1
6
9
1

11.76
7.13
10.87
24.10
5.35
22.60
11.63
11.36
2.31
2.92
14.85
5.80
6.15
3.61
6.51
21.05
10.53
9.09
25.00
12.35
7.63
7.35
14.18
13.33
48.19
2.40
25.81
7.01
23.48
4.52
15.38
31.37
9.80
20.34
19.40
7.87

0.08
0.35
0.50
0.27
0.32
0.96
0.72
1.01
0.10
0.10
1.75
0.10
0.39
0.87
0.51
0.13
0.64
0.10
0.33
1.73
0.24
0.24
0.11
1.39
5.17
0.03
1.87
0.47
4.71
0.59
0.13
3.00
0.07
1.27
1.94
0.72

396
Springfield Police Dept
Springfield Police Dept
St. Charles Police Dept
St. Joseph Police Dept
St. Louis (city) Police Dept
St. Paul Police Department
St. Petersburg Police Department
Stamford Police Department
Stockton Police Department
Stratford Police Department
Suffolk Police Department
Sugar Land Police Department
Sumter Police Department
Sunrise Police Department
Surprise Police Department
Syracuse Police Department
Tacoma Police Department
Tallahassee Police Department
Tampa Police Department
Tempe Police Department
Texarkana Police Department
Texas City Police Department
Tinley Park Police Dept
Titusville Police Department
Toledo Police Department
Topeka Police Department
Trenton Police
Troy Police Department
Tucson Police Department
Tulsa Police Department
Tupelo Police Department
Tuscaloosa Police Department
Union City Police
Utica Police Department
Vacaville Police Department
Valdosta Police Department

Greene
Sangamon
St. Charles
Buchanan
St. Louis City
Ramsey
Pinellas
Fairfield
San Joaquin
Fairfield
Suffolk
Fort Bend
Sumter
Broward
Maricopa
Onondaga
Pierce
Leon
Hillsborough
Maricopa
Bowie
Galveston
Cook
Brevard
Lucas
Shawnee
Mercer
Rensselaer
Pima
Tulsa
Lee
Tuscaloosa
Hudson
Oneida
Solano
Lowndes

MO
IL
MO
MO
MO
MN
FL
CT
CA
CT
VA
TX
SC
FL
AZ
NY
WA
FL
FL
AZ
TX
TX
IL
FL
OH
KS
NJ
NY
AZ
OK
MS
AL
NJ
NY
CA
GA

306
273
112
114
1351
598
510
292
415
111
171
130
107
175
127
489
371
364
980
357
94
85
81
88
640
283
361
121
1032
826
115
263
165
179
111
133

3
1
1
1
10
8
6
3
2
3
1
2
3
1
1
3
3
1
3
1
1
2
2
1
12
4
6
1
6
11
1
1
3
2
1
1

9.80
3.66
8.93
8.77
7.40
13.38
11.76
10.27
4.82
27.03
5.85
15.38
28.04
5.71
7.87
6.13
8.09
2.75
3.06
2.80
10.64
23.53
24.69
11.36
18.75
14.13
16.62
8.26
5.81
13.32
8.70
3.80
18.18
11.17
9.01
7.52

1.09
0.51
0.28
1.12
3.13
1.57
0.65
0.33
0.29
0.33
1.18
0.34
2.79
0.06
0.03
0.64
0.38
0.36
0.24
0.03
1.08
0.69
0.04
0.18
2.72
2.25
1.64
0.63
0.61
1.82
1.21
0.51
0.47
0.85
0.24
0.92

397
Victoria Police Department
Vineland Police
Virginia Beach Police Department
Visalia Department of Public Safety
Waco Police Department
Warren Police Department
Washington Metropolitan Police Dept
Washington Township Police
Waterbury Police Department
Waterloo Police Department
Waukegan Police Dept
Wayne Township Police
West Bloomfield Township Police
West Chester Police Department
West Haven Police Department
West Jordan Police Department
West New York Police
West Palm Beach Police Department
West Valley City Police Department
Westminster Police Department
Westminster Police Dept
Weymouth Police Department
Wheeling Police Department
White Plains Police Department
Wichita Falls Police Department
Wichita Police Department
Wilkes Barre City Police Department
Wilmington Police Department
Wilmington Police Department
Wilson Police Department
Winston-Salem Police Department
Woodbridge Police
Woonsocket Police Department
Worcester Police Department
Yakima Police Department
Yonkers Police Department

Victoria
Cumberland
Virginia Beach City
Tulare
McLennan
Macomb
District of Columbia
Gloucester
New Haven
Black Hawk
Lake
Passaic
Oakland
Butler
New Haven
Salt Lake
Hudson
Palm Beach
Salt Lake
Orange
Adams
Norfolk
Ohio
Westchester
Wichita
Sedgwick
Luzerne
New Hanover
New Castle
Wilson
Forsyth
Middlesex
Providence
Worcester
Yakima
Westchester

TX
NJ
VA
CA
TX
MI
DC
NJ
CT
IA
IL
NJ
MI
OH
CT
UT
NJ
FL
UT
CA
CO
MA
WV
NY
TX
KS
PA
NC
DE
NC
NC
NJ
RI
MA
WA
NY

106
157
813
136
246
230
3742
86
256
120
155
117
81
90
122
102
119
310
186
100
178
96
84
210
177
662
92
266
306
114
508
206
99
482
134
641

2
2
9
1
8
1
29
1
4
4
2
1
1
1
2
2
1
3
1
2
4
1
2
1
2
2
1
2
2
2
3
1
4
5
1
2

18.87
12.74
11.07
7.35
32.52
4.35
7.75
11.63
15.63
33.33
12.90
8.55
12.35
11.11
16.39
19.61
8.40
9.68
5.38
20.00
22.47
10.42
23.81
4.76
11.30
3.02
10.87
7.52
6.54
17.54
5.91
4.85
40.40
10.37
7.46
3.12

2.30
1.27
2.05
0.23
3.41
0.12
4.82
0.35
0.46
3.05
0.28
0.20
0.08
0.27
0.23
0.19
0.16
0.23
0.10
0.07
0.91
0.15
4.50
0.11
1.52
0.40
0.31
0.99
0.37
2.46
0.86
0.12
0.64
0.63
0.41
0.21

398
York Police Department
Yuma Police Department

York
Yuma

PA
AZ

110
167

1
2

9.09
11.98

0.23
1.02

399
Table 84. Special State & Local Law Enforcement Agencies in Study: Rates of Officers Arrested, 2005-2011 (Sorted
Alphabetically)
# of Full-Time
Per
Agency
County
State Sworn Personnel
Agency
Allegheny Port Authority Transit Police
Anchorage International Airport Police
Appalachian State University Police Dept
Arkansas State University Police Dept.
Austin I.S.D. Police Department
Baltimore City School Police Dept.
BART Police Department
Beaumont I.S.D. Police Dept.
Boston School Police
Bowie State University Dept. of Public Safety
Buffalo State College Police
BYU-Idaho Police Department
California Bureau of Narcotics Enforcement
California Dept. of Justice
California Exposition And State Fair Police
Cameron Co. District Attorney Investigations Div
Central Michigan University Police Department
College of Lake County Police Dept
Connally I.S.D. Police Dept.
Dallas I.S.D. Police Dept.
Dayton International Airport Police Dept
Delaware River & Bay Authority Police
Delaware River Port Authority - Transit Police
District of Columbia Protective Services Police
Duke University Police Department
Ennis I.S.D. Police Department
Florida Atlantic University Police
Florida Fish & Wildlife Conservation Commission
Florida International University Police
George Mason University Police Dept.
Georgia Public Safety Training Center

Allegheny
Anchorage
Watauga
Craighead
Travis
Baltimore City
Alameda
Jefferson
Suffolk
Prince Georges
Erie
Madison
Sacramento
Sacramento
Sacramento
Cameron
Isabella
Lake
McLennan
Dallas
Montgomery
New Castle
Camden
District of Columbia
Durham
Ellis
Palm Beach
Leon
Miami-Dade
Fairfax
Monroe

PA
AK
NC
AR
TX
MD
CA
TX
MA
MD
NY
ID
CA
CA
CA
TX
MI
IL
TX
TX
OH
DE
NJ
DC
NC
TX
FL
FL
FL
VA
GA

42
65
25
17
70
142
192
22
80
14
30
10
73
419
6
30
21
13
3
88
29
50
144
484
60
5
39
626
39
52
19

Per 1,000
Officers
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
4
1
1
1

23.81
15.38
40.00
58.82
14.29
7.04
5.21
45.45
12.50
71.43
33.33
100.00
27.40
2.39
166.67
33.33
47.62
76.92
333.33
11.36
34.48
20.00
6.94
2.07
16.67
200.00
25.64
6.39
25.64
19.23
52.63

Per 100,000
Population
0.08
0.34
1.96
1.04
0.10
0.16
0.07
0.40
0.14
0.12
0.11
2.66
0.02
0.10
0.07
0.25
1.42
0.14
0.43
0.04
0.19
0.51
0.19
0.17
0.37
0.67
0.08
0.44
0.04
0.09
3.78

400
Georgia State University Police
Grambling State University Police Dept.
Greenville Technical College Public Safety
Houston Baptist University Police Department
Houston Community College System
Humble I.S.D. Police Dept.
Indiana State Excise Police
Indiana University Purdue U. Fort Wayne U. Police
Kansas State Law Enforcement Training Center
Kentucky Alcoholic Beverage Control
Lafayette College Office of Public Safety
Lancaster I.S.D. Police Dept.
Lander University Public Safety
Lehigh-Northampton Airport Auth. Police Dept
Los Angeles School Police Department
Louisiana State Univ. Health Sciences Center Police Dept
Louisiana State University Police Department
Maryland Department of General Services Police
Maryland National Capital Park Police - Montgomery Co
Maryland Natural Resources Police
Maryland Transit Administration Police
Maryland Transportation Authority Police
Massachusetts Dept of Mental Health Police Dept
MBTA Transit Police
McAllen I.S.D. Police Dept.
Medical College of Georgia
Medical University of South Carolina Public Safety
Metropolitan Washington Airports Authority Police
Middlesex County Prosecutor's Office
Midland I.S.D. Police Dept.
Mississippi Department of Wildlife, Fisheries & Parks
Missouri Department of Corrections
Missouri Univ. of Science & Technology Police Dept
MIT Police Department
Morrisville State College Police
New Jersey Department of Environmental Protection

Fulton
Lincoln
Greenville
Harris
Harris
Harris
Marion
Allen
Reno
Franklin
Northampton
Dallas
Greenwood
Lehigh
Los Angeles
Caddo
East Baton Rouge
Baltimore City
Montgomery
Anne Arundel
Baltimore City
Baltimore
Suffolk
Suffolk
Hidalgo
Richmond
Charleston
Arlington
Middlesex
Midland
Hinds
Cole
Phelps
Middlesex
Madison
Burlington

GA
LA
SC
TX
TX
TX
IN
IN
KS
KY
PA
TX
SC
PA
CA
LA
LA
MD
MD
MD
MD
MD
MA
MA
TX
GA
SC
VA
NJ
TX
MS
MO
MO
MA
NY
NJ

68
9
9
30
48
24
88
18
39
30
18
6
10
9
340
49
62
68
86
224
140
456
40
256
43
32
62
206
80
11
230
13
11
59
11
90

1
2
1
1
1
1
1
1
1
2
1
1
1
1
2
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1

14.71
222.22
111.11
33.33
20.83
41.67
11.36
55.56
25.64
66.67
55.56
166.67
100.00
111.11
5.88
20.41
16.13
14.71
11.63
4.46
7.14
2.19
50.00
7.81
23.26
31.25
16.13
4.85
12.50
90.91
4.35
76.92
90.91
16.95
90.91
11.11

0.11
4.28
0.22
0.02
0.02
0.02
0.11
0.28
0.20
0.27
0.34
0.04
1.44
0.29
0.02
0.39
0.23
0.16
0.41
0.19
0.16
0.16
0.28
0.28
0.13
0.50
0.29
0.09
0.12
0.73
1.05
1.32
2.21
0.07
1.36
0.11

401
New Jersey Transit Police
New Mexico Department Of Game & Fish
New York City Dept of Environmental Protection Police
New York State Metro Transportation Auth. Police
New York State Park Police
North Forest I.S.D. Police Dept.
Northern Illinois University Police
Northside I.S.D. Police Dept.
Ohio Dept of Natural Resources - Ofc. of Law Enf.
Ohio Department of Taxation - Enforcement Division
Ohio State University Police Department
Palm Beach County School District Police
Pennsylvania Dept of Conservation & Natural Resources
Port Authority of New York & New Jersey Police Dept
San Antonio Park Rangers
Santa Ana Unified School District Police Dept.
Santa Rosa I.S.D. Police Department
School District of Philadelphia Police
Shippensburg University of Pennsylvania Police
Socorro I.S.D. Police Dept.
South Carolina Dept of Mental Health-Public Safety
South Carolina Department of Natural Resources
Southern University and A & M College Police
St. Edward's University Police Department
St. Joseph County Airport Police
St. Mary's University Police Department
State University at Albany Police
Tennessee State University
Texas Alcoholic Beverage Commission
Tuskegee University Police Department
United I.S.D. Police Dept.
University of Alabama - Birmingham Police Dept
University of Arkansas Medical Sciences Dept of Pub
University of California - Los Angeles Police
University of Colorado - Colorado Springs Police Dept.
University of Florida Police

Essex
Santa Fe
Westchester
New York
Albany
Harris
DeKalb
Bexar
Franklin
Franklin
Franklin
Palm Beach
Dauphin
Hudson
Bexar
Orange
Cameron
Philadelphia
Cumberland
El Paso
Richland
Richland
East Baton Rouge
Travis
St. Joseph
Bexar
Albany
Davidson
Travis
Macon
Webb
Jefferson
Pulaski
Los Angeles
El Paso
Alachua

NJ
NM
NY
NY
NY
TX
IL
TX
OH
OH
OH
FL
PA
NJ
TX
CA
TX
PA
PA
TX
SC
SC
LA
TX
IN
TX
NY
TN
TX
AL
TX
AL
AR
CA
CO
FL

201
106
168
694
305
21
59
83
394
30
51
176
136
1667
112
21
3
450
17
30
120
238
43
14
17
20
41
27
277
33
51
79
36
57
14
85

3
1
1
2
1
1
1
2
2
1
1
4
1
1
3
2
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1

14.93
9.43
5.95
2.88
3.28
47.62
16.95
24.10
5.08
33.33
19.61
22.73
7.35
0.60
26.79
95.24
333.33
2.22
58.82
33.33
8.33
4.20
23.26
71.43
58.82
50.00
24.39
74.07
3.61
30.30
19.61
12.66
27.78
17.54
71.43
11.76

0.47
0.69
1.29
0.13
0.21
0.02
0.95
0.12
3.24
0.18
0.09
0.30
0.79
0.16
0.17
0.07
0.25
0.07
0.42
0.12
0.26
0.26
0.23
0.10
0.37
0.06
0.33
0.32
0.10
4.66
0.40
0.15
0.26
0.01
0.16
0.40

402
University of Illinois Police Dept
University of Maryland Eastern Shore Public Safety
University of North Texas Police Department
University of Pittsburgh - Main Campus Police
University of Pittsburgh at Johnstown Police
University of South Carolina - Upstate Police Dept
University of Tennessee at Knoxville Police
University of Texas - Austin Police
University of West Alabama Police
Vanderbilt University Police Department
Ventura College
Virginia Commonwealth University Police Dept.
Virginia Marine Resources Commission
Volusia County Beach Patrol
Washington Metropolitan Area Transit Auth. Police
Wayne State University Dept of Public Safety
Wisconsin Dept of Justice - Criminal Investigation Div.

Champaign
Somerset
Denton
Allegheny
Cambria
Spartanburg
Knox
Travis
Sumter
Davidson
Ventura
Richmond City
Newport News City
Volusia
District of Columbia
Wayne
Dane

IL
MD
TX
PA
PA
SC
TN
TX
AL
TN
CA
VA
VA
FL
DC
MI
WI

54
6
40
73
13
12
52
62
6
91
5
74
60
60
442
49
92

1
1
1
1
1
1
2
1
1
1
1
2
1
2
2
1
1

18.52
166.67
25.00
13.70
76.92
83.33
38.46
16.13
166.67
10.99
200.00
27.03
16.67
33.33
4.52
20.41
10.87

0.50
3.78
0.15
0.08
0.70
0.35
10.47
0.10
7.27
0.16
0.12
0.98
3.02
0.40
0.33
0.05
0.20

403

Figure 1. Police Crime Arrest Cases: CART Model Predicting Conviction

404

Figure 2. Police Crime Arrest Cases: CART Model Predicting Job Loss

405

Figure 3. Police Crime: CART Model Predicting Sex-related Arrest Cases

406

Figure 4. Police Crime: CART Model Predicting Alcohol-related Arrest Cases

407

Figure 5. Police Crime: CART Model Predicting Drug-related Arrest Cases

408

Figure 6. Police Crime: CART Model Predicting Violence-related Arrest Cases

409

Figure 7. Police Crime: CART Model Predicting Profit-motivated Arrest Cases

410

Figure 8. CART Model Predicting Being Named as a Party Defendant in a Section 1983 Civil Action at Some Point during Career

411

Figure 9. Sex-related Arrest Cases: CART Model Predicting Conviction

412

Figure 10. Sex-related Arrest Cases: CART Model Predicting Job Loss

413

Figure 11. Sex-related Arrest Cases: CART Model Predicting Child Victims

414

Figure 12. Police Sexual Violence Arrest Cases: CHAID Model Predicting Conviction

415

Figure 13. Police Sexual Violence Arrest Cases: CART Model Predicting Job Loss

416

Figure 14. Driving While Female Encounters Arrest Cases: CHAID Model Predicting Conviction

417

Figure 15. Driving While Female Encounters Arrest Cases: CHAID Model Predicting Job Loss

418

Figure 16. Alcohol-related Arrest Cases: CART Model Predicting Conviction

419

Figure 17. Alcohol-related Arrest Cases: CART Model Predicting Job Loss

420

Figure 18. DUI Arrest Cases: CART Model Predicting Conviction

421

Figure 19. DUI Arrest Cases: CART Model Predicting Job Loss

422

Figure 20. Drug-related Arrest Cases: CART Model Predicting Conviction

423

Figure 21. Drug-related Arrest Cases: CART Model Predicting Job Loss

424

Figure 22. Violence-related Arrest Cases: CART Model Predicting Conviction

425

Figure 23. Violence-related Arrest Cases: CART Model Predicting Job Loss

426

Figure 24. Officer-involved Domestic Violence Arrest Cases: CART Model Predicting Conviction

427

Figure 25. Officer-involved Domestic Violence Arrest Cases: CART Model Predicting Job Loss

428

Figure 26. Profit-motivated Arrest Cases: CART Model Predicting Conviction

429

Figure 27. Profit-motivated Arrest Cases: CART Model Predicting Job Loss

430
Appendix A-1. Nonmetropolitan State & Local Law Enforcement Agencies in Study: Rates of Officers Arrested, 2005-2011 (Sorted by # FT
Sworn)
# of Full-Time
Per
Per 1,000
Agency
County
State Sworn Personnel
Agency
Officers
Navajo Nation Tribal Dept of Law Enforcement
Monroe County Sheriff's Office
New York City Dept of Env. Protection Police
Robeson County Sheriff's Office
Danville Police Department
Kauai (County) Police Department
Sauk County Sheriff's Office
Tupelo Police Department
Wilson Police Department
Henry County Sheriff's Office
Nye County Sheriff's Office
Douglas County Sheriff's Office
Meridian Police Department
Greenville Police Department
Orangeburg County Sheriff's Office
Muskogee Police Department
St. Landry Parish Sheriff's Office
LaGrange Police Department
Sanford Police Department
Roswell Police Department
Torrington Police Department
Cullman County Sheriff's Office
Georgetown County Sheriff's Office
Concord Police Department
Stillwater Police Department
Lumberton Police Department
Orangeburg Public Safety
Alamogordo Department of Public Safety
Auburn Police Department
Cookeville Police Department
Shelby Police Department

Apache
Monroe
Westchester
Robeson
Pittsylvania
Kauai
Sauk
Lee
Wilson
Henry
Nye
Douglas
Lauderdale
Washington
Orangeburg
Muskogee
St. Landry
Troup
Lee
Chaves
Litchfield
Cullman
Georgetown
Merrimack
Payne
Robeson
Orangeburg
Otero
Cayuga
Putnam
Cleveland

AZ
FL
NY
NC
VA
HI
WI
MS
NC
VA
NV
NV
MS
MS
SC
OK
LA
GA
NC
NM
CT
AL
SC
NH
OK
NC
SC
NM
NY
TN
NC

393
189
168
128
126
125
118
115
114
112
108
100
99
96
92
90
90
83
81
80
79
78
78
77
74
73
72
71
70
70
70

3
3
1
3
1
3
1
1
2
14
3
1
3
2
1
4
4
1
1
2
1
1
1
1
1
1
3
1
1
2
1

7.63
15.87
5.95
23.44
7.94
24.00
8.47
8.70
17.54
125.00
27.78
10.00
30.30
20.83
10.87
44.44
44.44
12.05
12.35
25.00
12.66
12.82
12.82
12.99
13.51
13.70
41.67
14.08
14.29
28.57
14.29

Per 100,000
Population
4.19
4.10
1.29
2.24
1.57
4.47
1.61
1.21
2.46
25.85
6.83
2.13
3.74
3.91
1.08
5.63
4.80
1.49
1.73
3.05
0.53
1.24
1.66
0.68
1.29
0.75
3.24
1.57
1.25
2.77
1.02

431
Danville (City) Sheriff's Office
Hendry County Sheriff's Office
Del Rio Police Department
Frankfort Police Department
Lincoln County Sheriff's Office
Richmond Police Department
Grand Traverse County Sheriff's Office
Manitowoc Police Department
Twin Falls Police Department
Vernon Parish Sheriff's Office
Columbus Police Department
Gallup Police Department
Jamestown Police Department
Jackson County Sheriff's Office
Lake County Sheriff's Office
Nogales Police Department
Virginia Marine Resources Commission
Allegan County Sheriff's Office
Campbell County Sheriff's Office
Lenoir County Sheriff's Office
Opelousas Police Department
Putnam County Sheriff's Office
Elko County Sheriff's Office
Sevierville Police Department
Bartlesville Police Department
Paris Police Department
Sandusky Police Department
Clovis Police Department
Galesburg Police Dept
Zanesville Police Department
Shawnee Police Department
Kerrville Police Department
Assumption Parish Sheriff's Office
Durango Police Department
Selma Police Department
Dodge City Police Department

Danville City
Hendry
Val Verde
Franklin
Lincoln
Madison
Grand Traverse
Manitowoc
Twin Falls
Vernon
Lowndes
McKinley
Chautauqua
Jackson
Lake
Santa Cruz
Newport News City
Allegan
Campbell
Lenoir
St. Landry
Putnam
Elko
Sevier
Washington
Lamar
Erie
Curry
Knox
Muskingum
Pottawatomie
Kerr
Assumption
La Plata
Dallas
Ford

VA
FL
TX
KY
OR
KY
MI
WI
ID
LA
MS
NM
NY
FL
CA
AZ
VA
MI
WY
NC
LA
TN
NV
TN
OK
TX
OH
NM
IL
OH
OK
TX
LA
CO
AL
KS

69
67
65
65
65
65
64
64
64
64
62
62
62
61
61
60
60
59
58
58
58
58
57
55
54
54
54
53
53
53
52
51
50
50
50
49

3
1
1
1
1
1
2
2
1
2
1
1
2
1
2
2
1
1
1
2
1
2
1
1
4
1
2
3
1
3
1
1
2
1
2
1

43.48
14.93
15.38
15.38
15.38
15.38
31.25
31.25
15.63
31.25
16.13
16.13
32.26
16.39
32.79
33.33
16.67
16.95
17.24
34.48
17.24
34.48
17.54
18.18
74.07
18.52
37.04
56.60
18.87
56.60
19.23
19.61
40.00
20.00
40.00
20.41

6.97
2.55
2.05
2.03
2.17
1.21
2.30
2.46
1.29
3.82
1.67
1.40
1.48
2.01
3.09
4.22
3.02
0.90
2.17
3.36
1.20
2.77
2.05
1.11
7.85
2.01
2.59
6.20
1.89
3.49
1.44
2.02
8.54
1.95
4.56
2.95

432
Page County Sheriff's Office
Stanly County Sheriff's Office
Cherokee County Sheriff's Office
Natchez Police Dept.
Chillicothe Police Department
Lee County Sheriff's Office
Plattsburgh Police Department
Thomas County Sheriff's Office
Eureka Police Department
Lincoln Parish Sheriff's Office
Mason City Police Department
New Milford Police Department
Butte - Silver Bow County Sheriff's Office
Gatlinburg Police Department
Gillette Police Department
Grant County Sheriff's Office
Harrison County Sheriff's Office
Juneau County Sheriff's Office
Kingsville Police Department
Natchitoches Parish Sheriff's Office
Calhoun Police Department
Cortland Police Department
Juneau Police Dept.
Lenawee County Sheriff's Office
Saline County Sheriff's Office
Scottsboro Police Department
Sedalia Police Dept
Sullivan County Sheriff's Office
Zapata County Sheriff's Office
Kingsland Police Dept
Poplar Bluff Police Department
Ada Police Department
Saline County Sheriff's Office
Chesterfield County Sheriff's Office
Rutland Police Department.
Searcy Police Department

Page
Stanly
Cherokee
Adams
Ross
Lee
Clinton
Thomas
Humboldt
Lincoln
Cerro Gordo
Litchfield
Silver Bow
Sevier
Campbell
Grant
Harrison
Juneau
Kleberg
Natchitoches
Gordon
Cortland
Juneau
Lenawee
Saline
Jackson
Pettis
Sullivan
Zapata
Camden
Butler
Pontotoc
Saline
Chesterfield
Rutland
White

VA
NC
SC
MS
OH
NC
NY
GA
CA
LA
IA
CT
MT
TN
WY
IN
TX
WI
TX
LA
GA
NY
AK
MI
KS
AL
MO
NY
TX
GA
MO
OK
IL
SC
VT
AR

49
49
48
48
47
47
47
47
46
46
46
46
45
45
45
45
45
45
45
45
44
44
44
44
44
44
44
44
44
43
43
42
42
41
41
41

1
1
2
4
1
1
1
2
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

20.41
20.41
41.67
83.33
21.28
21.28
21.28
42.55
21.74
21.74
21.74
21.74
44.44
22.22
22.22
22.22
22.22
22.22
22.22
22.22
22.73
22.73
22.73
22.73
22.73
22.73
22.73
22.73
22.73
23.26
23.26
23.81
23.81
24.39
24.39
24.39

4.16
1.65
3.61
12.39
1.28
1.73
1.22
4.47
0.74
2.14
2.26
0.53
5.85
1.11
2.17
1.43
1.52
3.75
3.12
2.53
1.81
2.03
3.20
1.00
1.80
1.88
2.37
1.29
7.13
1.98
2.34
2.67
4.01
2.14
1.62
1.30

433
Bainbridge Police Department
Big Spring Police Department
McDowell County Sheriff's Office
White County Sheriff's Office
Corinth Police Department
Klamath Falls Police Department
Muscatine Police Department
Seymour Police
Boone Police Department
Brownwood Police Department
Mount Airy Police Department
Alice Police Department
Bogalusa Police Department
Dunn Police Department
Fort Dodge Police Department
Gaffney Police Department
Muskogee County Sheriff's Office
Palatka Police Department
Bennettsville Police Department
Durant Police Department
Hastings Police Dept
Kalispell Police Department
La Salle County Sheriff's Office
Union City Police Department
Washington Police Department
Clarksdale Police Department
Lebanon Police Department
McKinley County Sheriff's Office
New Castle (city) Police Department
New Castle Police
Polk County Police Department
DeKalb County Sheriff's Office
Fort Payne Police Department
Martin County Sheriff's Office
Plainview Police Department
Seneca Police Department

Decatur
Howard
McDowell
White
Alcorn
Klamath
Muscatine
Jackson
Watauga
Brown
Surry
Jim Wells
Washington
Harnett
Webster
Cherokee
Muskogee
Putnam
Marlboro
Bryan
Adams
Flathead
La Salle
Obion
Beaufort
Coahoma
Grafton
McKinley
Lawrence
Henry
Polk
DeKalb
DeKalb
Martin
Hale
Oconee

GA
TX
NC
GA
MS
OR
IA
IN
NC
TX
NC
TX
LA
NC
IA
SC
OK
FL
SC
OK
NE
MT
IL
TN
NC
MS
NH
NM
PA
IN
GA
AL
AL
NC
TX
SC

40
40
40
40
39
39
39
39
38
38
38
37
37
37
37
37
37
37
36
36
36
36
36
36
36
35
35
35
35
35
35
34
34
34
34
34

2
1
1
1
1
1
1
1
1
2
1
1
1
1
1
2
1
1
1
1
2
1
1
1
1
1
1
1
1
1
3
1
1
1
1
1

50.00
25.00
25.00
25.00
25.64
25.64
25.64
25.64
26.32
52.63
26.32
27.03
27.03
27.03
27.03
54.05
27.03
27.03
27.78
27.78
55.56
27.78
27.78
27.78
27.78
28.57
28.57
28.57
28.57
28.57
85.71
29.41
29.41
29.41
29.41
29.41

7.18
2.86
2.22
3.68
2.70
1.51
2.34
2.36
1.96
5.25
1.36
2.45
2.12
0.87
2.63
3.61
1.41
1.34
3.46
2.36
6.38
1.10
0.88
3.14
2.09
3.82
1.12
1.40
1.10
2.02
7.23
1.41
1.41
4.08
2.76
1.35

434
Holmes County Sheriff's Office
Tuskegee University Police Department
Baxter County Sheriff's Office
Eunice Police Department
Moberly Police Department
Olean Police Department
Tahlequah Police Department
Traverse City Police Department
Austin Police Department
Murray Police Department
Starr County Sheriff's Office
Stephens County Sheriff's Office
Andalusia Police Dept.
Berea Police Department
Del Norte County Sheriff's Office
Great Bend Police Department
Greenbrier County Sheriff's Office
Helena\/West Helena Police Department
Kings Mountain Police Department
Lebanon Police Department
Luna County Sheriff's Office
Miami Police Department
Mineral Wells Police Department
Okanogan County Sheriff's Office
Otero County Sheriff's Office
Scottsbluff Police Dept
Shelby County Sheriff's Office
Warren County Sheriff's Office
Albert Lea Police Department
Halifax County Sheriff's Office
Mount Pleasant Police Department
Ravalli County Sheriff's Office
Hooksett Police Department
Lee County Sheriff's Office
Leesville Police Department
Vail Police Department

Holmes
Macon
Baxter
St. Landry
Randolph
Cattaraugus
Cherokee
Grand Traverse
Mower
Calloway
Starr
Stephens
Covington
Madison
Del Norte
Barton
Greenbrier
Phillips
Cleveland
Laclede
Luna
Ottawa
Palo Pinto
Okanogan
Otero
Scotts Bluff
Shelby
Warren
Freeborn
Halifax
Titus
Ravalli
Merrimack
Lee
Vernon
Eagle

OH
AL
AR
LA
MO
NY
OK
MI
MN
KY
TX
GA
AL
KY
CA
KS
WV
AR
NC
MO
NM
OK
TX
WA
NM
NE
OH
NC
MN
VA
TX
MT
NH
SC
LA
CO

33
33
32
32
32
32
32
32
31
31
31
31
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
30
29
29
29
29
28
28
28
28

1
1
1
5
1
1
1
1
1
1
1
1
1
1
2
1
1
4
2
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1

30.30
30.30
31.25
156.25
31.25
31.25
31.25
31.25
32.26
32.26
32.26
32.26
33.33
33.33
66.67
33.33
33.33
133.33
66.67
33.33
33.33
33.33
66.67
33.33
33.33
33.33
33.33
33.33
34.48
34.48
34.48
34.48
35.71
35.71
35.71
35.71

2.36
4.66
2.41
6.00
3.93
1.25
2.13
1.15
2.55
2.69
1.64
3.82
2.65
1.21
6.99
3.61
2.82
18.38
2.04
2.81
3.98
3.14
7.11
2.43
1.57
2.70
2.02
4.77
3.20
2.76
3.09
2.49
0.68
5.20
1.91
1.92

435
White County Sheriff's Office
Accomack County Sheriff's Office
Farmville Police Department
Jim Wells County Sheriff's Office
Las Vegas Police Department
Marble Falls Police Department
Marion County Sheriff's Office
Philadelphia Police Department
Polk County Sheriff's Office
Rosebud Sioux Tribal Police
Emery County Sheriff's Office
Hampton County Sheriff's Office
Humboldt Police Department
Ruidoso Police Department
Steuben County Sheriff's Office
Appalachian State University Police Dept
Dillon Police Department
Lawrence County Sheriff's Office
Marlboro County Sheriff's Office
Newaygo County Sheriff's Office
Ogdensburg Police Department
Russellville Police Department
Sault Ste. Marie Police Department
Tuscola County Sheriff's Office
Commerce Police Department
Deridder Police Department
Huron Police Department
Jennings Police Department
Marion Police Department
Aspen Police Department
Beeville Police Department
Canton Police Dept
Emmet County Sheriff's Office
Graham County Sheriff's Office
Monroe County Sheriff's Office
Seneca County Sheriff's Office

White
Accomack
Prince Edward
Jim Wells
San Miguel
Burnet
Marion
Neshoba
Polk
Todd
Emery
Hampton
Gibson
Lincoln
Steuben
Watauga
Dillon
Lawrence
Marlboro
Newaygo
St. Lawrence
Franklin
Chippewa
Tuscola
Jackson
Beauregard
Beadle
Jefferson Davis
Marion
Pitkin
Bee
Fulton
Emmet
Graham
Monroe
Seneca

TN
VA
VA
TX
NM
TX
WV
MS
GA
SD
UT
SC
TN
NM
NY
NC
SC
IN
SC
MI
NY
AL
MI
MI
GA
LA
SD
LA
SC
CO
TX
IL
MI
AZ
WI
NY

28
27
27
27
27
27
27
27
27
27
26
26
26
26
26
25
25
25
25
25
25
25
25
25
24
24
24
24
24
23
23
23
23
23
23
23

1
1
1
1
1
1
1
1
2
1
2
1
2
1
1
1
1
1
2
1
1
2
1
1
1
1
1
1
1
2
1
1
1
1
1
6

35.71
37.04
37.04
37.04
37.04
37.04
37.04
37.04
74.07
37.04
76.92
38.46
76.92
38.46
38.46
40.00
40.00
40.00
80.00
40.00
40.00
80.00
40.00
40.00
41.67
41.67
41.67
41.67
41.67
86.96
43.48
43.48
43.48
43.48
43.48
260.87

3.87
3.02
4.28
2.45
3.40
2.34
1.77
3.37
4.82
10.40
18.22
4.74
4.03
4.88
1.01
1.96
3.12
2.17
6.91
2.06
0.89
6.31
2.60
1.79
1.65
2.80
5.75
3.17
3.02
11.66
3.14
2.70
3.06
2.69
2.24
17.02

436
Tuskegee Police Department
Winn Parish Sheriff's Office
Columbiana County Sheriff's Office
Craig Police Department
Fayette County Sheriff's Office
Hardeman County Sheriff's Office
Haywood County Sheriff's Office
Hornell Police Department
Indiana Borough Police Department
Ketchikan Police Dept.
Ontario Police Department
Rio Arriba County Sheriff's Office
Routt County Sheriff's Office
Vernal Police Department
Washington Court House Police Dept.
Bluefield Police Department
Bolivar Police Department
Caruthersville Police Department
Central Michigan University Police Department
Clearlake Police Department
Mason County Sheriff's Office
San Jacinto County Sheriff's Office
Clewiston Police Department
Marksville Police Department
Oakdale Police Department
Platteville Police Department
Vidalia Police Department
Wilkesboro Police Department
Conneaut Police Department
Corbin Police Department
Daviess County Sheriff's Office
Delavan Police Department
Demopolis Police Department
Norwich Police Department
Sturgis Police Department
Barre Police Department

Macon
Winn
Columbiana
Moffat
Fayette
Hardeman
Haywood
Steuben
Indiana
Ketchikan Gateway
Malheur
Rio Arriba
Routt
Uintah
Fayette
Mercer
Hardeman
Pemiscot
Isabella
Lake
Mason
San Jacinto
Hendry
Avoyelles
Allen
Grant
Concordia
Wilkes
Ashtabula
Whitley
Daviess
Walworth
Marengo
Chenango
St. Joseph
Washington

AL
LA
OH
CO
OH
TN
TN
NY
PA
AK
OR
NM
CO
UT
OH
WV
TN
MO
MI
CA
WV
TX
FL
LA
LA
WI
LA
NC
OH
KY
IN
WI
AL
NY
MI
VT

23
23
22
22
22
22
22
22
22
22
22
22
22
22
22
21
21
21
21
21
21
21
20
20
20
20
20
20
19
19
19
19
19
19
19
18

2
1
2
1
3
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
3
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1

86.96
43.48
90.91
45.45
136.36
45.45
45.45
45.45
45.45
45.45
45.45
45.45
45.45
45.45
45.45
47.62
47.62
95.24
47.62
47.62
142.86
47.62
100.00
50.00
50.00
50.00
50.00
50.00
52.63
52.63
52.63
52.63
52.63
52.63
52.63
55.56

9.32
6.53
1.85
7.25
10.33
3.67
5.32
1.01
1.13
7.42
3.19
2.48
4.25
3.07
3.44
1.61
3.67
10.93
1.42
1.55
10.98
3.79
5.11
2.38
3.88
1.95
4.80
1.44
0.99
2.81
3.16
0.98
4.76
1.98
1.63
1.68

437
Black Mountain Police Department
Bolivar County Sheriff's Office
Calhoun County Sheriff's Office
Duval County Sheriff's Office
Greensburg Police
Kendallville Police
Leland Police Department
Marion Police Department
Osceola County Sheriff's Office
Rockmart Police Department
Seaside Police Department
Selmer Police Department
Spencer Police Department
Sweetwater Police Department
Towns County Sheriff's Office
Winnemucca Police Department
Cross County Sheriff's Office
Elkin Police Department
Fort Madison Police Department
Kitty Hawk Police Department
McIntosh County Sheriff's Office
Middlesex County Sheriff's Office
Princeton Police Department
Bishopville Police Department
Eatonton Police Department
Harrodsburg Police Department
Lamesa Police Department
Miller County Sheriff's Office
Phillips County Sheriff's Office
Red Springs Police Department
Crossett Police Department
Ephrata Police Department
Greene County Sheriff's Office
McDowell County Sheriff's Office
Moab Police Department
Nashville Police Department

Buncombe
Bolivar
Calhoun
Duval
Decatur
Noble
Washington
Smyth
Osceola
Polk
Clatsop
McNairy
Clay
Monroe
Towns
Humboldt
Cross
Surry
Lee
Dare
McIntosh
Middlesex
Mercer
Lee
Putnam
Mercer
Dawson
Miller
Phillips
Robeson
Ashley
Grant
Greene
McDowell
Grand
Berrien

NC
MS
FL
TX
IN
IN
MS
VA
MI
GA
OR
TN
IA
TN
GA
NV
AR
NC
IA
NC
OK
VA
WV
SC
GA
KY
TX
MO
AR
NC
AR
WA
AR
WV
UT
GA

18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
17
17
17
17
17
17
17
16
16
16
16
16
16
16
15
15
15
15
15
15

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1

55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
58.82
58.82
58.82
58.82
117.65
58.82
117.65
62.50
62.50
62.50
62.50
62.50
62.50
62.50
66.67
66.67
66.67
66.67
66.67
66.67

2.22
2.93
6.84
8.49
3.89
2.10
1.96
3.10
4.25
2.41
2.70
3.84
6.00
2.25
9.55
6.05
5.60
1.36
2.79
2.95
9.88
9.12
3.21
5.20
4.71
4.69
7.23
4.04
4.60
0.75
4.58
1.12
2.38
4.52
10.84
5.19

438
Nebraska City Police Dept
Royston Police Department
Shelby County Sheriff's Office
Springfield Police Department
St. Marys Police Department
Thief River Falls Police Department
Vinita Police Department
Appling County Sheriff's Office
Caribou Police Department
Clyde Police Department
Cochran Police Department
Grants Police Department
Jackson County Sheriff's Office
Morrow County Sheriff's Office
Newport Police Department
Ocean Shores Police Department
Pelham Police Department
Benzie County Sheriff's Office
Denton Police Department
Fairmont Police Department
Frisco Police Department
Hempstead County Sheriff's Office
Pauls Valley Police Department
Randolph County Sheriff's Office
Tucumcari Police Department
Upper Sandusky Police Department
Winnfield Police Dept
Braselton Police Department
Bunkie Police Department
Clayton County Sheriff's Office
Custer County Sheriff's Office
Edenton Police Department
Fayette Police Department
Franklin County Sheriff's Office
Gage County Sheriff's Office
Lakeport Police Department

Otoe
Franklin
Shelby
Windsor
Auglaize
Pennington
Craig
Appling
Aroostook
Sandusky
Bleckley
Cibola
Jackson
Morrow
Sullivan
Grays Harbor
Mitchell
Benzie
Caroline
Robeson
Summit
Hempstead
Garvin
Randolph
Quay
Wyandot
Winn
Jackson
Avoyelles
Clayton
Custer
Chowan
Fayette
Franklin
Gage
Lake

NE
GA
TX
VT
OH
MN
OK
GA
ME
OH
GA
NM
TN
OR
NH
WA
GA
MI
MD
NC
CO
AR
OK
IL
NM
OH
LA
GA
LA
IA
OK
NC
AL
IN
NE
CA

15
15
15
15
15
15
15
14
14
14
14
14
14
14
14
14
14
13
13
13
13
13
13
13
13
13
13
12
12
12
12
12
12
12
12
12

2
1
2
1
1
1
1
1
1
1
3
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1
1
1

133.33
66.67
133.33
66.67
66.67
66.67
66.67
71.43
71.43
71.43
214.29
71.43
214.29
71.43
71.43
71.43
71.43
76.92
76.92
76.92
76.92
76.92
76.92
76.92
76.92
76.92
76.92
83.33
83.33
83.33
83.33
250.00
83.33
83.33
83.33
83.33

12.71
4.53
7.86
1.76
2.18
7.18
6.65
5.48
1.39
1.64
22.97
3.67
25.78
8.95
2.29
1.37
4.26
5.71
3.02
0.75
3.57
4.42
3.63
2.99
11.06
4.42
6.53
1.65
2.38
5.52
3.64
20.28
5.80
4.33
4.48
1.55

439
Madill Police Department
Monticello Police
Perry County Sheriff's Office
Quincy Police Department
Wallace Police Department
Whitley County Sheriff's Office
Williamsburg Police Department
Wolfeboro Police Department
Algood Police Department
Crystal City Police Department
Eastman Police Department
Floyd County Sheriff's Office
Fort Gibson Police Department
Greene County Sheriff's Office
Hannahville Tribal Police Department
Hockley County Sheriff's Office
Medina Police Department
Missouri University of Science & Tech Police Dept
Montezuma Police Department
Pulaski County Sheriff's Office
Rainsville Police Department
Roosevelt County Sheriff's Office
Simpson County Sheriff's Office
Stark County Sheriff's Office
Stephens County Sheriff's Office
Allendale Police Department
Allenstown Police Department
Alma Police Department
BYU-Idaho Police Department
Columbus Police Department
Creston Police Department
Dewitt Police Department
Ferriday Police Department
Ferry County Sheriff's Office
Helen Police Department
Ishpeming Police Department

Marshall
White
Perry
Grant
Duplin
Whitley
Whitley
Carroll
Putnam
Zavala
Dodge
Floyd
Muskogee
Greene
Menominee
Hockley
Gibson
Phelps
Macon
Pulaski
DeKalb
Roosevelt
Simpson
Stark
Stephens
Allendale
Merrimack
Bacon
Madison
Colorado
Union
Clinton
Concordia
Ferry
White
Marquette

OK
IN
IL
WA
NC
KY
KY
NH
TN
TX
GA
IA
OK
AL
MI
TX
TN
MO
GA
IL
AL
MT
KY
ND
OK
SC
NH
GA
ID
TX
IA
IA
LA
WA
GA
MI

12
12
12
12
12
12
12
12
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
10
10
10
10
10
10
10
10
10
10
10

1
1
1
1
1
1
1
1
1
4
1
1
1
1
1
2
1
1
2
2
1
1
1
1
2
2
1
1
1
1
2
1
1
1
1
1

83.33
83.33
83.33
83.33
83.33
83.33
83.33
83.33
90.91
363.64
90.91
90.91
90.91
90.91
90.91
181.82
90.91
90.91
181.82
181.82
90.91
90.91
90.91
90.91
181.82
200.00
100.00
100.00
100.00
100.00
200.00
100.00
100.00
100.00
100.00
100.00

6.31
4.06
4.47
1.12
1.71
2.81
2.81
2.09
1.38
34.26
4.59
6.13
1.41
11.06
4.16
8.72
2.01
2.21
13.57
32.46
1.41
9.59
5.77
4.13
4.44
19.20
0.68
9.01
2.66
4.79
15.96
2.04
4.80
13.24
3.68
1.49

440
Jaffrey Police Department
Kermit Police Department
La Salle County Sheriff's Office
Lancaster Police Department
Lander University Public Safety
Marlow Police Department
Millersburg Police Department
Montague County Sheriff's Office
New Martinsville Police Department
Powell County Sheriff's Office
Socorro County Sheriff's Office
St. George Police Department
Williams Police Department
Winkler County Sheriff's Office
Andrews Police Department
Arcade Police Department
Beardstown Police Dept
Belpre Police Department
Butler County Sheriff's Office
Dillon Police Department
Forest City Police Department
Forks Police Department
Glendive Police Department
Grambling State University Police Dept.
Hanceville Police Department
Lake County Sheriff's Office
Lawrence Township Police Department
Minocqua Police Department
Osceola Police Department
Ripley Police Department
Tabor City Police Department
Big Horn County Sheriff's Office
Blackford County Sheriff's Office
Blountstown Police Dept.
Canton Village Police Department
Chandler Police Department

Cheshire
Winkler
La Salle
Garrard
Greenwood
Stephens
Holmes
Montague
Wetzel
Powell
Socorro
Dorchester
Colusa
Winkler
Georgetown
Jackson
Cass
Washington
Butler
Summit
Winnebago
Clallam
Dawson
Lincoln
Cullman
Lake
Clearfield
Oneida
Clarke
Jackson
Columbus
Big Horn
Blackford
Calhoun
St. Lawrence
Henderson

NH
TX
TX
KY
SC
OK
OH
TX
WV
MT
NM
SC
CA
TX
SC
GA
IL
OH
AL
CO
IA
WA
MT
LA
AL
CO
PA
WI
IA
WV
NC
MT
IN
FL
NY
TX

10
10
10
10
10
10
10
10
10
10
10
10
10
10
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
8
8
8
8
8

1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
1
1
1
2
1
2
1
1
1
1
2
1
1
1
1
1

100.00
100.00
300.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
111.11
111.11
111.11
111.11
333.33
111.11
111.11
111.11
111.11
222.22
111.11
222.22
111.11
111.11
111.11
111.11
222.22
125.00
125.00
125.00
125.00
125.00

1.30
14.06
43.57
5.91
1.44
2.22
2.36
5.07
6.03
14.23
5.60
2.57
4.67
14.06
1.66
1.65
7.33
1.62
14.32
3.57
9.20
1.40
11.15
4.28
1.24
27.36
1.22
2.78
10.77
3.42
3.44
7.77
7.83
6.84
0.89
1.27

441
Eunice Police Department
Kingfisher County Sheriff's Office
Madison County Sheriff's Office
Oglesby Police Dept
Talbot County Sheriff's Office
Waynesburg Borough Police Department
Whiteville Police Department
Black River Falls Police Department
Byron Police Dept
Clay County Sheriff's Office
Coeburn Police Department
Flemingsburg Police Department
Fremont County Sheriff's Office
Haynesville Police Dept
Hermann Police Department
Holly Hill Police Department
Murphy Police Department
New Castle Police Department
Port Barre Police Dept
Providence Police Department
Shelby Police Department
Shenandoah Borough Police Department
Shinnston Police Department
Union Police Department
Winnsboro Police Department
Yemassee Police Department
Bingen-White Salmon Police Department
Bridgeport Police Department
Chilhowie Police Department
Columbus Police Department
East Brewton Police Department
Fairfax Police Department
Florala Police Department
Geneva Township Police Department
Kaw Nation Tribal Police
Norton Police Department

Lea
Kingfisher
Madison
La Salle
Talbot
Greene
Hardeman
Jackson
Ogle
Clay
Wise
Fleming
Fremont
Claiborne
Gasconade
Orangeburg
Cherokee
Garfield
St. Landry
Webster
Bolivar
Schuylkill
Harrison
Newton
Franklin
Hampton
Klickitat
Jackson
Smyth
Polk
Escambia
Allendale
Covington
Walworth
Kay
Norton

NM
OK
MO
IL
GA
PA
TN
WI
IL
AR
VA
KY
IA
LA
MO
SC
NC
CO
LA
KY
MS
PA
WV
MS
LA
SC
WA
AL
VA
NC
AL
SC
AL
WI
OK
KS

8
8
8
8
8
8
8
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
6
6
6
6
6
6
6
6
6
6

1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
4
2
1
1
1
1
1
4
1
1
1
1
1
1
1

125.00
125.00
125.00
125.00
375.00
125.00
125.00
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
285.71
571.43
285.71
142.86
142.86
142.86
166.67
166.67
666.67
166.67
166.67
166.67
166.67
166.67
166.67
166.67

1.54
6.65
8.18
0.88
43.70
2.58
3.67
4.89
1.87
6.22
2.41
6.97
13.44
5.82
6.57
1.08
3.64
1.77
1.20
7.34
5.86
2.70
2.89
4.60
4.82
4.74
4.92
1.88
12.42
4.88
2.61
9.60
2.65
0.98
2.15
17.63

442
Santee Police Department
Sleepy Eye Police Department
St. Paul Police Department
University of West Alabama Police
Woodruff County Sheriff's Office
Carlisle Police Department
Chaffee Police Department
Commerce Police Department
Crescent City Police Department
Delhi Police Department
Dixon Police Department
Durand Police Department
Estill County Sheriff's Office
Fryeburg Police Department
Grand Rapids Police Department
Grant County Sheriff's Office
Haskell Police Department
Holt County Sheriff's Office
Kenton Police Department
Lake County Sheriff's Office
Level Plains Police Department
Lewis County Sheriff's Office
Middletown Police
Montpelier Police Department
Mora County Sheriff's Office
North Kingsville Police Department
Oglethorpe Police Department
Olney Police Department
Oregon County Sheriff's Office
Pearson Police Department
Pocahontas County Sheriff's Office
Ranger Police Department
Richland Police Dept
Robbins Police Department
Roseboro Police Department
Rosedale Police Department

Orangeburg
Redwood
Wise
Sumter
Woodruff
Nicholas
Scott
Ottawa
Putnam
Richland
Pulaski
Shiawassee
Estill
Oxford
Wood
Grant
Muskogee
Holt
Obion
Lake
Dale
Lewis
Henry
Bear Lake
Mora
Ashtabula
Macon
Young
Oregon
Atkinson
Pocahontas
Eastland
Pulaski
Moore
Sampson
Bolivar

SC
MN
VA
AL
AR
KY
MO
OK
FL
LA
MO
MI
KY
ME
WI
OK
OK
NE
TN
SD
AL
MO
IN
ID
NM
OH
GA
TX
MO
GA
WV
TX
MO
NC
NC
MS

6
6
6
6
6
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5

2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
2
1

333.33
166.67
166.67
166.67
166.67
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
400.00
200.00
200.00
200.00
200.00
200.00
400.00
200.00

2.16
3.86
2.41
7.27
13.77
14.02
2.55
3.14
1.34
4.83
1.91
1.42
6.82
1.73
1.34
22.09
1.41
9.58
3.14
8.93
1.99
9.79
2.02
16.71
20.49
0.99
6.78
5.39
18.38
11.94
11.47
5.38
1.91
1.13
3.15
2.93

443
Santa Clara Police Department
Sugarcreek Borough Police Department
Sugarcreek Police Department
Terrell County Sheriff's Office
Vergennes Police Department
West Yellowstone Police Department
Woodstock Police Department
Blue Lake Police Department
Butler Township Police Department
Columbus Police Department
Delhi Village Police Department
Elmore Police Department
Fairland Police Department
Foster Township Police Department
Gold Beach Police Department
Hemingway Police Department
Keokuk County Sheriff's Office
Mahanoy City Borough Police Department
Malakoff Police Department
Marble Head Police Department
Marshallville Police Dept.
McArthur Police Department
Monroeville Police Department
New Lisbon Police Department
Onley Police Department
Rochelle Police Department
Roodhouse Police Dept
Tutwiler Police Department
Winsted Police Department
Belle Police Department
Bismarck Police Department
Bowman Police Department
Caddo Police Department
Carter County Sheriff's Office
Earlville Police Dept
Fair Bluff Police Department

Grant
Venango
Tuscarawas
Terrell
Addison
Gallatin
Grafton
Humboldt
Schuylkill
Luna
Delaware
Ottawa
Ottawa
McKean
Curry
Williamsburg
Keokuk
Schuylkill
Henderson
Ottawa
Macon
Vinton
Huron
Juneau
Accomack
Wilcox
Greene
Tallahatchie
Litchfield
Maries
St. Francois
Orangeburg
Bryan
Carter
La Salle
Columbus

NM
PA
OH
TX
VT
MT
NH
CA
PA
NM
NY
OH
OK
PA
OR
SC
IA
PA
TX
OH
GA
OH
OH
WI
VA
GA
IL
MS
CT
MO
MO
SC
OK
MO
IL
NC

5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
1
2
1
1

200.00
200.00
200.00
200.00
200.00
200.00
200.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
333.33
333.33
1000.00
333.33
666.67
333.33
333.33

3.39
1.82
1.08
101.63
2.72
1.12
1.12
0.74
0.67
3.98
2.08
2.41
3.14
2.30
4.47
2.91
9.51
0.67
1.27
2.41
6.78
7.44
1.68
3.75
3.02
10.80
7.20
6.50
0.53
10.90
1.53
3.24
2.36
31.92
0.88
1.72

444
Freedom Police Department
Gallatin County Sheriff's Office
Griggs County Sheriff's Office
Guadalupe County Sheriff's Office
Haskell Police Department
Hennessey Police Department
Kahoka Police Department
Marvell Police Department
Meigs Police Department
Newbury Police Department
Ravenna Police Dept
Scotts Hill Police Department
Stover Police Department
Waukomis Police Department
Athena Police Department
Birchwood Police Department
Boswell Police Department
Cherokee Police Department
Franklin Police Dept
Hegins Township Police Department
Homer City Borough Police Department
Hunter Police Department
Inman Police Department
Meyersdale Borough Police Department
Pineland Police Department
Pink Hill Police Department
Ridgeville Police
Ridgeville Police Department
Seadrift Police Department
Springfield Police Department
White Cloud Police Department
Windber Borough Police Department
Atwater Police Department
Berlin Borough Police Department
Berlin Heights Police Department
Burr Oak Police Department

Carroll
Gallatin
Griggs
Guadalupe
Haskell
Kingfisher
Clark
Phillips
Thomas
Merrimack
Buffalo
Henderson
Morgan
Garfield
Umatilla
Washburn
Choctaw
Alfalfa
Franklin
Schuylkill
Indiana
Greene
McPherson
Somerset
Sabine
Lenoir
Randolph
Dorchester
Calhoun
Orangeburg
Newaygo
Somerset
Kandiyohi
Somerset
Erie
St. Joseph

NH
IL
ND
NM
TX
OK
MO
AR
GA
NH
NE
TN
MO
OK
OR
WI
OK
OK
NE
PA
PA
NY
KS
PA
TX
NC
IN
SC
TX
SC
MI
PA
MN
PA
OH
MI

3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
1000.00
1000.00
1000.00
1000.00

2.09
17.89
41.32
21.34
16.95
6.65
6.58
4.60
2.24
0.68
2.17
3.60
4.86
1.65
1.32
6.28
6.58
17.72
31.01
0.67
1.13
2.03
3.43
1.29
9.23
1.68
3.82
2.57
4.68
1.08
2.06
1.29
2.37
1.29
1.30
1.63

445
Cooter Police Department
Elgin Police Department
Hamburg Police Department
Lamoure Police Department
Lockhart Police Department
Marion Township Police Department
Mounds Police Dept
Nicholas County Sheriff's Office
Oakwood Police Department
Perryville Police Department
Petroleum County Sheriff's Office
Pineview Police Department
Tipton Police Department
Turkey Creek Police Department
Wakeman Police Department
Wilson Police Department
Zolfo Springs Police Department
Bloomville Police Department
Dale County Sheriff's Office
Errol Police Department
New Athens Police Department
Port Jefferson Police Department
Wheeler Police Department

Pemiscot
Grant
Fremont
LaMoure
Covington
Waushara
Pulaski
Nicholas
Paulding
Boyle
Petroleum
Wilcox
Tillman
Evangeline
Huron
Ellsworth
Hardee
Seneca
Dale
Coos
Harrison
Shelby
Dunn

MO
ND
IA
ND
AL
WI
IL
KY
OH
KY
MT
GA
OK
LA
OH
KS
FL
OH
AL
NH
OH
OH
WI

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0

1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
2000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00

5.47
41.77
13.44
24.16
2.65
4.08
32.46
14.02
5.10
3.52
202.43
10.80
12.51
2.94
1.68
15.39
3.61
1.76
1.99
3.03
6.30
2.02
2.28

446
Appendix A-2. Primary State Police Agencies in Study: Rates of Officers Arrested, 2005-2011 (Sorted by # FT
Sworn)
# of Full-Time
Agency
County
State
Sworn Personnel
California Highway Patrol
New York State Police
Pennsylvania State Police
Texas Department of Public Safety
New Jersey State Police
Massachusetts State Police
Illinois State Police
Virginia State Police
North Carolina State Highway Patrol
Michigan State Police
Florida Highway Patrol
Ohio State Highway Patrol
Maryland State Police
Indiana State Police
Arizona Department of Public Safety
Connecticut State Police
Louisiana State Police
Washington State Patrol
Georgia Department of Public Safety
South Carolina Highway Patrol
Tennessee Department of Safety
Kentucky State Police
Oklahoma Department of Public Safety
Alabama Department of Public Safety
Colorado State Patrol
Iowa Department of Public Safety
Delaware State Police
Mississippi Highway Safety Patrol
Minnesota State Patrol
New Mexico State Police
Arkansas State Police

Sacramento
Albany
Dauphin
Travis
Mercer
Middlesex
Sangamon
Chesterfield
Wake
Ingham
Leon
Franklin
Baltimore
Marion
Maricopa
Middlesex
East Baton Rouge
Thurston
Fulton
Richland
Davidson
Franklin
Oklahoma
Montgomery
Jefferson
Polk
Kent
Hinds
Ramsey
Santa Fe
Pulaski

CA
NY
PA
TX
NJ
MA
IL
VA
NC
MI
FL
OH
MD
IN
AZ
CT
LA
WA
GA
SC
TN
KY
OK
AL
CO
IA
DE
MS
MN
NM
AR

Per
Agency

7202
4847
4458
3529
3053
2310
2105
1873
1827
1732
1606
1560
1440
1315
1244
1227
1215
1132
1048
967
942
882
825
763
742
669
658
594
530
528
525

13
5
26
6
13
10
10
3
8
9
14
4
9
4
3
4
2
2
1
10
4
3
5
2
4
2
3
6
4
13
2

Per 1,000
Officers
1.81
1.03
5.83
1.70
4.26
4.33
4.75
1.60
4.38
5.20
8.72
2.56
6.25
3.04
2.41
3.26
1.65
1.77
0.95
10.34
4.25
3.40
6.06
2.62
5.39
2.99
4.56
10.10
7.55
24.62
3.81

Per 100,000
Population
0.03
0.03
0.20
0.02
0.15
0.15
0.08
0.04
0.08
0.09
0.07
0.03
0.16
0.06
0.05
0.11
0.04
0.03
0.01
0.22
0.06
0.07
0.13
0.04
0.08
0.07
0.33
0.20
0.08
0.63
0.07

447
Nebraska State Patrol
Utah Department of Public Safety
Nevada Highway Patrol
New Hampshire State Police
Maine State Police
Vermont State Police
Alaska State Troopers
Idaho State Police
Wyoming Highway Patrol
Rhode Island State Police
Arkansas Highway Police

Lancaster
Salt Lake
Carson
Merrimack
Kennebec
Washington
Anchorage
Ada
Laramie
Providence
Pulaski

NE
UT
NV
NH
ME
VT
AK
ID
WY
RI
AR

491
475
417
350
334
307
274
264
204
201
149

1
2
3
3
1
1
1
1
1
2
2

2.04
4.21
7.19
8.57
2.99
3.26
3.65
3.79
4.90
9.95
13.42

0.05
0.07
0.11
0.23
0.08
0.16
0.14
0.06
0.18
0.19
0.07

448
Appendix A-3. Sheriff's Offices in Study: Rates of Officers Arrested, 2005-2011 (Sorted by # FT Sworn)
# of Full-Time
Agency
County
State
Sworn Personnel
Los Angeles County Sheriff's Office
Cook County Sheriff's Office
Harris County Sheriff's Office
Riverside County Sheriff's Office
San Bernardino County Sheriff's Office
Orange County Sheriff-Coroner Department
Broward County Sheriff's Office
Palm Beach County Sheriff's Office
Sacramento County Sheriff's Office
Orange County Sheriff's Office
San Diego County Sheriff's Office
Hillsborough County Sheriff's Office
Wayne County Sheriff's Office
Alameda County Sheriff's Office
Pinellas County Sheriff's Office
Jefferson Parish Sheriff's Office
Maricopa County Sheriff's Office
Ventura County Sheriff's Office
Marion County Sheriff's Office
Denver County Sheriff's Office
King County Sheriff's Office
Contra Costa County Sheriff's Office
Collier County Sheriff's Office
Lee County Sheriff's Office
Polk County Sheriff's Office
Calcasieu Parish Sheriff's Office
Pima County Sheriff's Dept.
Gwinnett County Sheriff's Office
Passaic County Sheriff's Office
Bexar County Sheriff's Office
Milwaukee County Sheriff's Office
Fulton County Sheriff's Office

Los Angeles
Cook
Harris
Riverside
San Bernardino
Orange
Broward
Palm Beach
Sacramento
Orange
San Diego
Hillsborough
Wayne
Alameda
Pinellas
Jefferson
Maricopa
Ventura
Marion
Denver
King
Contra Costa
Collier
Lee
Polk
Calcasieu
Pima
Gwinnett
Passaic
Bexar
Milwaukee
Fulton

CA
IL
TX
CA
CA
CA
FL
FL
CA
FL
CA
FL
MI
CA
FL
LA
AZ
CA
IN
CO
WA
CA
FL
FL
FL
LA
AZ
GA
NJ
TX
WI
GA

9461
5655
2558
2147
1797
1794
1624
1447
1409
1398
1322
1223
1062
928
863
825
766
755
740
739
721
679
628
621
600
592
554
531
530
526
524
516

Per
Agency
14
8
3
11
11
14
16
12
6
16
7
10
1
2
8
12
2
1
7
5
5
1
6
7
13
1
1
3
6
5
7
4

Per 1,000
Officers
1.48
1.41
1.17
5.12
6.12
7.80
9.85
8.29
4.26
11.44
5.30
8.18
0.94
2.16
9.27
14.55
2.61
1.32
9.46
6.77
6.93
1.47
9.55
11.27
21.67
1.69
1.81
5.65
11.32
9.51
13.36
7.75

Per 100,000
Population
0.14
0.15
0.07
0.50
0.54
0.47
0.92
0.91
0.42
1.40
0.23
0.81
0.05
0.13
0.87
2.77
0.05
0.12
0.77
0.83
0.26
0.10
1.87
1.13
2.16
0.52
0.10
0.37
1.20
0.29
0.74
0.43

449
Shelby County Sheriff's Office
Tulare County Sheriff's Office
Kern County Sheriff's Office
Richland County Sheriff's Office
Orleans Parish Sheriff's Office (Criminal Division)
Fairfax County Sheriff's Office
Brevard County Sheriff's Office
Monmouth County Sheriff's Office
Pasco County Sheriff's Office
Manatee County Sheriff's Office
Fresno County Sheriff's Office
Knox County Sheriff's Office
Franklin County Sheriff's Office
Dane County Sheriff's Office
El Paso County Sheriff's Office
Santa Clara County Sheriff's Office
Volusia County Sheriff's Office
Dallas County Sheriff's Office
Richmond County Sheriff's Office
Loudoun County Sheriff's Office
Will County Sheriff's Office
Leon County Sheriff's Office
Cobb County Sheriff's Office
Ouachita Parish Sheriff's Office
Richmond (City) Sheriff's Office
Martin County Sheriff's Office
Norfolk (City) Sheriff's Office
Washoe County Sheriff's Office
Sarasota County Sheriff's Office
St. Tammany Parish Sheriff's Office
Arapahoe County Sheriff's Office
Greenville County Sheriff's Office
Summit County Sheriff's Office
Escambia County Sheriff's Office
Osceola County Sheriff's Office
Adams County Sheriff's Office

Shelby
Tulare
Kern
Richland
Orleans
Fairfax
Brevard
Monmouth
Pasco
Manatee
Fresno
Knox
Franklin
Dane
El Paso
Santa Clara
Volusia
Dallas
Richmond
Loudoun
Will
Leon
Cobb
Ouachita
Richmond City
Martin
Norfolk City
Washoe
Sarasota
St. Tammany
Arapahoe
Greenville
Summit
Escambia
Osceola
Adams

TN
CA
CA
SC
LA
VA
FL
NJ
FL
FL
CA
TN
OH
WI
CO
CA
FL
TX
GA
VA
IL
FL
GA
LA
VA
FL
VA
NV
FL
LA
CO
SC
OH
FL
FL
CO

516
513
512
512
505
499
497
494
485
476
461
456
455
454
454
450
450
449
449
448
445
443
435
431
424
414
414
414
409
409
407
397
393
388
388
364

8
3
11
3
4
3
2
1
4
4
1
1
2
2
1
1
4
1
5
2
1
3
1
1
2
1
1
4
1
2
2
2
1
3
2
2

15.50
5.85
21.48
5.86
7.92
6.01
4.02
2.02
8.25
8.40
2.17
2.19
4.40
4.41
2.20
2.22
8.89
2.23
11.14
4.46
2.25
6.77
2.30
2.32
4.72
2.42
2.42
9.66
2.44
4.89
4.91
5.04
2.54
7.73
5.15
5.49

0.86
0.68
1.31
0.78
1.16
0.28
0.37
0.16
0.86
1.24
0.11
0.23
0.17
0.41
0.16
0.06
0.81
0.04
2.49
0.64
0.15
1.09
0.15
0.65
0.98
0.68
0.41
0.95
0.26
0.86
0.35
0.44
0.18
1.01
0.74
0.45

450
East Baton Rouge Parish Sheriff's Office
Chesapeake (City) Sheriff's Office
Seminole County Sheriff's Office
Wake County Sheriff's Office
Marion County Sheriff's Office
Hamilton County Sheriff's Office
DeKalb County Sheriff's Office
Clackamas County Sheriff's Office
New Hanover County Sheriff's Office
Bossier Parish Sheriff's Office
Terrebonne Parish Sheriff's Office
Spartanburg County Sheriff's Office
Santa Barbara County Sheriff's Office
Pierce County Sheriff's Office
St. Charles Parish Sheriff's Office
Bibb County Sheriff's Office
Lucas County Sheriff's Office
Lafourche Parish Sheriff's Office
Montgomery County Sheriff's Office
Snohomish County Sheriff's Office
Lake County Sheriff's Office
Charlotte County Sheriff's Office
Clay County Sheriff's Office
Harford County Sheriff's Office
Bernalillo County Sheriff's Office
Alachua County Sheriff's Office
Monroe County Sheriff's Office
Hidalgo County Sheriff's Office
Morris County Sheriff's Office
Charleston County Sheriff's Office
St. Lucie County Sheriff's Office
Okaloosa County Sheriff's Office
Hall County Sheriff's Office
Marion County Sheriff's Office
Barnstable County Sheriff's Office
Forsyth County Sheriff's Office

East Baton Rouge
Chesapeake City
Seminole
Wake
Marion
Hamilton
DeKalb
Clackamas
New Hanover
Bossier
Terrebonne
Spartanburg
Santa Barbara
Pierce
St. Charles
Bibb
Lucas
Lafourche
Montgomery
Snohomish
Lake
Charlotte
Clay
Harford
Bernalillo
Alachua
Monroe
Hidalgo
Morris
Charleston
St. Lucie
Okaloosa
Hall
Marion
Barnstable
Forsyth

LA
VA
FL
NC
FL
OH
GA
OR
NC
LA
LA
SC
CA
WA
LA
GA
OH
LA
TN
WA
FL
FL
FL
MD
NM
FL
NY
TX
NJ
SC
FL
FL
GA
OR
MA
GA

359
358
355
354
349
330
325
319
315
300
300
297
294
292
291
290
289
287
287
287
286
285
284
280
279
276
273
262
262
259
259
258
257
255
254
253

3
2
2
4
3
1
2
2
3
3
2
2
1
2
1
3
4
3
1
2
3
3
3
1
3
1
3
5
1
4
2
3
2
1
5
5

8.36
5.59
5.63
11.30
8.60
3.03
6.15
6.27
9.52
10.00
6.67
6.73
3.40
6.85
3.44
10.34
13.84
10.45
3.48
6.97
10.49
10.53
10.56
3.57
10.75
3.62
10.99
19.08
3.82
15.44
7.72
11.63
7.78
3.92
19.69
19.76

0.68
0.90
0.47
0.44
0.91
0.12
0.29
0.53
1.48
2.56
1.79
0.70
0.24
0.25
1.89
1.93
0.91
3.11
0.58
0.28
1.01
1.88
1.57
0.41
0.45
0.40
0.40
0.65
0.20
1.14
0.72
1.66
1.11
0.32
2.32
2.85

451
Hernando County Sheriff's Office
El Paso County Sheriff's Office
Macomb County Sheriff's Office
Spokane County Sheriff's Office
Iberia Parish Sheriff's Office
Onondaga County Sheriff's Office
Galveston County Sheriff's Office
Ramsey County Sheriff's Office
Prince George's County Sheriff's Office
Stanislaus County Sheriff's Office
Placer County Sheriff's Office
Indian River County Sheriff's Office
Chesterfield County Sheriff's Office
Montgomery County Sheriff's Office
Hudson County Sheriff's Office
Pinal County Sheriff's Office
Forsyth County Sheriff's Office
Bay County Sheriff's Office
Rapides Parish Sheriff's Office
Beaufort County Sheriff's Office
Columbia County Sheriff's Office
Williamson County Sheriff's Office
Marin County Sheriff's Office
Wilson County Sheriff's Office
St. Louis County Sheriff's Office
Anderson County Sheriff's Office
Santa Rosa County Sheriff's Office
Monroe County Sheriff's Office
Rutherford County Sheriff's Office
St. Bernard Parish Sheriff's Office
Lake County Sheriff's Office
Sullivan County Sheriff's Office
Frederick County Sheriff's Office
Paulding County Sheriff's Office
Boulder County Sheriff's Office
Lake County Sheriff's Office

Hernando
El Paso
Macomb
Spokane
Iberia
Onondaga
Galveston
Ramsey
Prince Georges
Stanislaus
Placer
Indian River
Chesterfield
Montgomery
Hudson
Pinal
Forsyth
Bay
Rapides
Beaufort
Columbia
Williamson
Marin
Wilson
St. Louis
Anderson
Santa Rosa
Monroe
Rutherford
St. Bernard
Lake
Sullivan
Frederick
Paulding
Boulder
Lake

FL
TX
MI
WA
LA
NY
TX
MN
MD
CA
CA
FL
VA
OH
NJ
AZ
NC
FL
LA
SC
GA
TX
CA
TN
MO
SC
FL
FL
TN
LA
IL
TN
MD
GA
CO
IN

249
248
245
244
242
242
240
235
233
230
228
226
225
222
221
218
217
213
212
209
206
206
202
202
200
191
190
189
189
189
188
183
177
175
174
170

1
2
1
2
6
1
1
2
3
1
1
3
1
1
3
2
2
2
1
1
2
2
1
1
4
2
1
3
2
1
1
1
1
1
1
5

4.02
8.06
4.08
8.20
24.79
4.13
4.17
8.51
12.88
4.35
4.39
13.27
4.44
4.50
13.57
9.17
9.22
9.39
4.72
4.78
9.71
9.71
4.95
4.95
20.00
10.47
5.26
15.87
10.58
5.29
5.32
5.46
5.65
5.71
5.75
29.41

0.58
0.25
0.12
0.42
8.19
0.21
0.34
0.39
0.35
0.19
0.29
2.17
0.32
0.19
0.47
0.53
0.57
1.18
0.76
0.62
1.61
0.47
0.40
0.88
0.40
1.07
0.66
4.10
0.76
2.79
0.14
0.64
0.43
0.70
0.34
1.01

452
Middlesex County Sheriff's Department
Union County Sheriff's Office
Walton County Sheriff's Office
Salem County Sheriff's Office
San Luis Obispo County Sheriff's Office
St. Charles County Sheriff's Office
Newton County Sheriff's Office
Allegheny County Sheriff's Office
St. John The Baptist Parish Sheriff's Office
Waukesha County Sheriff's Office
Hamilton County Sheriff's Office
Lowndes County Sheriff's Office
Plaquemines Parish Sheriff's Office
Polk County Sheriff's Office
Winnebago County Sheriff's Office
Cuyahoga County Sheriff's Office
Coweta County Sheriff's Office
Clark County Sheriff's Office
McHenry County Sheriff's Office
Anoka County Sheriff's Office
Washtenaw County Sheriff's Office
Boone County Sheriff's Office
Flagler County Sheriff's Office
Highlands County Sheriff's Office
Robeson County Sheriff's Office
Barrow County Sheriff's Office
Brazoria County Sheriff's Office
Catawba County Sheriff's Office
Livingston Parish Sheriff's Office
Pitt County Sheriff's Office
Kenosha County Sheriff's Office
Kitsap County Sheriff's Office
Weld County Sheriff's Office
New York City Sheriff's Office
St. Mary's County Sheriff's Office
Sauk County Sheriff's Office

Middlesex
Union
Walton
Salem
San Luis Obispo
St. Charles
Newton
Allegheny
St. John the Baptist
Waukesha
Hamilton
Lowndes
Plaquemines
Polk
Winnebago
Cuyahoga
Coweta
Clark
McHenry
Anoka
Washtenaw
Boone
Flagler
Highlands
Robeson
Barrow
Brazoria
Catawba
Livingston
Pitt
Kenosha
Kitsap
Weld
New York
St. Marys
Sauk

NJ
NC
FL
NJ
CA
MO
GA
PA
LA
WI
TN
GA
LA
IA
IL
OH
GA
OH
IL
MN
MI
KY
FL
FL
NC
GA
TX
NC
LA
NC
WI
WA
CO
NY
MD
WI

170
170
165
157
156
153
152
151
150
150
146
145
145
143
142
141
137
134
134
133
133
130
130
130
128
127
127
126
125
125
122
121
121
120
120
118

4
1
4
1
2
2
1
3
3
1
2
1
1
3
1
3
1
1
2
1
4
1
2
1
3
1
1
1
1
3
1
1
1
2
1
1

23.53
5.88
24.24
6.37
12.82
13.07
6.58
19.87
20.00
6.67
13.70
6.90
6.90
20.98
7.04
21.28
7.30
7.46
14.93
7.52
30.08
7.69
15.38
7.69
23.44
7.87
7.87
7.94
8.00
24.00
8.20
8.26
8.26
16.67
8.33
8.47

0.49
0.50
7.27
1.51
0.74
0.55
1.00
0.25
6.53
0.26
0.59
0.92
4.34
0.70
0.34
0.23
0.79
0.72
0.65
0.30
1.16
0.84
2.09
1.01
2.24
1.44
0.32
0.65
0.78
1.78
0.60
0.40
0.40
2.44
0.95
1.61

453
St. Joseph County Sheriff's Office
Clay County Sheriff's Office
Orange County Sheriff's Office
Whitfield County Sheriff's Office
Brunswick County Sheriff's Office
Solano County Sheriff's Office
Henry County Sheriff's Office
Butte County Sheriff's Office
Niagara County Sheriff's Office
Nassau County Sheriff's Office
Nye County Sheriff's Office
Cameron County Sheriff's Office
Madison County Sheriff's Office
Acadia Parish Sheriff's Office
Sutter County Sheriff's Office
Atlantic County Sheriff's Office
Benton County Sheriff's Office
Douglas County Sheriff's Office
Baltimore (City) Sheriff's Office
Orange County Sheriff's Office
Williamson County Sheriff's Office
Lincoln County Sheriff's Office
Johnston County Sheriff's Office
Rock County Sheriff's Office
San Juan County Sheriff's Office
Washington County Sheriff's Office
Lancaster County Sheriff's Office
Potter County Sheriff's Office
Kane County Sheriff's Office
Orangeburg County Sheriff's Office
Harrison County Sheriff's Office
Smith County Sheriff's Office
Carver County Sheriff's Office
Burke County Sheriff's Office
Delaware County Sheriff's Office
Washington County Sheriff's Office

St. Joseph
Clay
Orange
Whitfield
Brunswick
Solano
Henry
Butte
Niagara
Nassau
Nye
Cameron
Madison
Acadia
Sutter
Atlantic
Benton
Douglas
Baltimore City
Orange
Williamson
Lincoln
Johnston
Rock
San Juan
Washington
Lancaster
Potter
Kane
Orangeburg
Harrison
Smith
Carver
Burke
Delaware
Washington

IN
MO
NC
GA
NC
CA
VA
CA
NY
FL
NV
TX
AL
LA
CA
NJ
AR
NV
MD
NY
TN
NC
NC
WI
NM
MD
SC
TX
IL
SC
MS
TX
MN
NC
OH
TN

116
115
115
115
114
113
112
110
110
109
108
107
107
105
105
103
103
100
99
99
99
98
97
94
94
94
93
93
92
92
90
88
87
86
86
86

3
1
1
1
2
1
14
1
1
2
3
2
1
1
1
1
1
1
2
1
2
4
1
2
2
1
1
2
1
1
1
1
1
1
1
1

25.86
8.70
8.70
8.70
17.54
8.85
125.00
9.09
9.09
18.35
27.78
18.69
9.35
9.52
9.52
9.71
9.71
10.00
20.20
10.10
20.20
40.82
10.31
21.28
21.28
10.64
10.75
21.51
10.87
10.87
11.11
11.36
11.49
11.63
11.63
11.63

1.12
0.45
0.75
0.97
1.86
0.24
25.85
0.45
0.46
2.73
6.83
0.49
0.30
1.62
1.06
0.36
0.45
2.13
0.32
0.27
1.09
5.11
0.59
1.25
1.54
0.68
1.30
1.65
0.19
1.08
0.53
0.48
1.10
1.10
0.57
0.81

454
Wayne County Sheriff's Office
Baltimore County Sheriff's Office
Ozaukee County Sheriff's Office
Walker County Sheriff's Office
Cullman County Sheriff's Office
Franklin County Sheriff's Office
Madera County Sheriff's Office
Bedford County Sheriff's Office
Dakota County Sheriff's Office
Eaton County Sheriff's Office
Prince William County Sheriff's Office
Santa Fe County Sheriff's Office
Outagamie County Sheriff's Office
Saginaw County Sheriff's Office
Cleveland County Sheriff's Office
Minnehaha County Sheriff's Office
Yakima County Sheriff's Office
Hendry County Sheriff's Office
Marathon County Sheriff's Office
Portage County Sheriff's Office
Laurens County Sheriff's Office
Lincoln County Sheriff's Office
Livingston County Sheriff's Office
Pennington County Sheriff's Office
Darlington County Sheriff's Office
Grand Traverse County Sheriff's Office
Hamilton County Sheriff's Office
Vernon Parish Sheriff's Office
Jackson County Sheriff's Office
Kershaw County Sheriff's Office
Lake County Sheriff's Office
Porter County Sheriff's Office
Allegan County Sheriff's Office
Campbell County Sheriff's Office
Delaware County Sheriff's Office
La Porte County Sheriff's Office

Wayne
Baltimore
Ozaukee
Walker
Cullman
Franklin
Madera
Bedford
Dakota
Eaton
Prince William
Santa Fe
Outagamie
Saginaw
Cleveland
Minnehaha
Yakima
Hendry
Marathon
Portage
Laurens
Lincoln
Livingston
Pennington
Darlington
Grand Traverse
Hamilton
Vernon
Jackson
Kershaw
Lake
Porter
Allegan
Campbell
Delaware
La Porte

NC
MD
WI
GA
AL
VA
CA
VA
MN
MI
VA
NM
WI
MI
OK
SD
WA
FL
WI
OH
SC
OR
MI
SD
SC
MI
IN
LA
FL
SC
CA
IN
MI
WY
PA
IN

85
84
83
80
78
78
78
77
77
77
75
75
74
71
70
69
69
67
67
66
65
65
65
65
64
64
64
64
61
61
61
61
59
58
58
58

2
1
1
2
1
1
1
2
1
1
1
1
1
1
1
1
2
1
1
1
1
1
2
1
1
2
1
2
1
1
2
2
1
1
2
1

23.53
11.90
12.05
25.00
12.82
12.82
12.82
25.97
12.99
12.99
13.33
13.33
13.51
14.08
14.29
14.49
28.99
14.93
14.93
15.15
15.38
15.38
30.77
15.38
15.63
31.25
15.63
31.25
16.39
16.39
32.79
32.79
16.95
17.24
34.48
17.24

1.63
0.12
1.16
2.91
1.24
1.78
0.66
2.91
0.25
0.93
0.25
0.69
0.57
0.50
0.39
0.59
0.82
2.55
0.75
0.62
1.50
2.17
1.11
0.99
1.46
2.30
0.36
3.82
2.01
1.62
3.09
1.22
0.90
2.17
0.36
0.90

455
Lenoir County Sheriff's Office
Putnam County Sheriff's Office
Elko County Sheriff's Office
Pender County Sheriff's Office
Ulster County Sheriff's Office
Horry County Sheriff's Office
Morgan County Sheriff's Office
Yellowstone County Sheriff's Office
Assumption Parish Sheriff's Office
Bucks County Sheriff's Office
Hoke County Sheriff's Office
Pottawattamie County Sheriff's Office
Queen Anne's County Sheriff's Office
Caroline County Sheriff's Office
Page County Sheriff's Office
Stanly County Sheriff's Office
Cherokee County Sheriff's Office
Lee County Sheriff's Office
Thomas County Sheriff's Office
Lincoln Parish Sheriff's Office
Rockingham County Sheriff's Office
Davie County Sheriff's Office
Grant County Sheriff's Office
Harrison County Sheriff's Office
Juneau County Sheriff's Office
Sandoval County Sheriff's Office
St. Martin Parish Sheriff's Office
Upshur County Sheriff's Office
Chambers County Sheriff's Office
Lenawee County Sheriff's Office
Saline County Sheriff's Office
St. Croix County Sheriff's Office
Sullivan County Sheriff's Office
Zapata County Sheriff's Office
La Crosse County Sheriff's Office
Hawkins County Sheriff's Office

Lenoir
Putnam
Elko
Pender
Ulster
Horry
Morgan
Yellowstone
Assumption
Bucks
Hoke
Pottawattomie
Queen Annes
Caroline
Page
Stanly
Cherokee
Lee
Thomas
Lincoln
Rockingham
Davie
Grant
Harrison
Juneau
Sandoval
St. Martin
Upshur
Chambers
Lenawee
Saline
St. Croix
Sullivan
Zapata
La Crosse
Hawkins

NC
TN
NV
NC
NY
SC
AL
MT
LA
PA
NC
IA
MD
VA
VA
NC
SC
NC
GA
LA
VA
NC
IN
TX
WI
NM
LA
TX
TX
MI
KS
WI
NY
TX
WI
TN

58
58
57
57
57
55
55
55
50
50
50
50
50
49
49
49
48
47
47
46
46
45
45
45
45
45
45
45
44
44
44
44
44
44
43
42

2
2
1
1
1
1
1
1
2
2
1
1
1
3
1
1
2
1
2
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1

34.48
34.48
17.54
17.54
17.54
18.18
18.18
18.18
40.00
40.00
20.00
20.00
20.00
61.22
20.41
20.41
41.67
21.28
42.55
21.74
21.74
66.67
22.22
22.22
22.22
22.22
22.22
22.22
22.73
22.73
22.73
22.73
22.73
22.73
23.26
23.81

3.36
2.77
2.05
1.92
0.55
0.37
0.84
0.68
8.54
0.32
2.13
1.07
2.09
10.51
4.16
1.65
3.61
1.73
4.47
2.14
1.31
7.27
1.43
1.52
3.75
0.76
1.92
2.54
2.85
1.00
1.80
1.19
1.29
7.13
0.87
1.76

456
Saline County Sheriff's Office
Chesterfield County Sheriff's Office
Columbia County Sheriff's Office
Bullitt County Sheriff's Office
Burleigh County Sheriff's Office
Hancock County Sheriff's Office
Isle of Wight County Sheriff's Office
McDowell County Sheriff's Office
White County Sheriff's Office
Wood County Sheriff's Office
Luzerne County Sheriff's Office
Vigo County Sheriff's Office
Liberty County Sheriff's Office
Muskogee County Sheriff's Office
Valencia County Sheriff's Office
Woodford County Sheriff's Office
Dauphin County Sheriff's Office
La Salle County Sheriff's Office
Yamhill County Sheriff's Office
Clark County Sheriff's Office
McKinley County Sheriff's Office
DeKalb County Sheriff's Office
Madison County Sheriff's Office
Martin County Sheriff's Office
Holmes County Sheriff's Office
Baxter County Sheriff's Office
Russell County Sheriff's Office
Starr County Sheriff's Office
Stephens County Sheriff's Office
Clay County Sheriff's Office
Del Norte County Sheriff's Office
Greenbrier County Sheriff's Office
Luna County Sheriff's Office
Okanogan County Sheriff's Office
Otero County Sheriff's Office
Shelby County Sheriff's Office

Saline
Chesterfield
Columbia
Bullitt
Burleigh
Hancock
Isle of Wight
McDowell
White
Wood
Luzerne
Vigo
Liberty
Muskogee
Valencia
Woodford
Dauphin
La Salle
Yamhill
Clark
McKinley
DeKalb
Madison
Martin
Holmes
Baxter
Russell
Starr
Stephens
Clay
Del Norte
Greenbrier
Luna
Okanogan
Otero
Shelby

IL
SC
OR
KY
ND
IN
VA
NC
GA
WV
PA
IN
TX
OK
NM
IL
PA
IL
OR
IN
NM
AL
GA
NC
OH
AR
AL
TX
GA
IN
CA
WV
NM
WA
NM
OH

42
41
41
40
40
40
40
40
40
40
38
38
37
37
37
37
36
36
36
35
35
34
34
34
33
32
32
31
31
30
30
30
30
30
30
30

1
1
1
1
2
3
1
1
1
1
2
1
2
1
1
1
2
1
1
1
1
1
1
1
1
1
2
1
1
1
2
1
1
1
1
1

23.81
24.39
24.39
25.00
50.00
75.00
25.00
25.00
25.00
25.00
52.63
26.32
54.05
27.03
27.03
27.03
55.56
27.78
27.78
28.57
28.57
29.41
29.41
29.41
30.30
31.25
62.50
32.26
32.26
33.33
66.67
33.33
33.33
33.33
33.33
33.33

4.01
2.14
2.03
1.35
2.46
4.29
2.84
2.22
3.68
1.15
0.62
0.93
2.64
1.41
1.31
2.59
0.75
0.88
1.01
0.91
1.40
1.41
3.56
4.08
2.36
2.41
3.78
1.64
3.82
3.72
6.99
2.82
3.98
2.43
1.57
2.02

457
Warren County Sheriff's Office
Dearborn County Sheriff's Office
Halifax County Sheriff's Office
Orleans County Sheriff's Office
Ravalli County Sheriff's Office
San Benito County Sheriff's Office
Williamson County Sheriff's Office
Lee County Sheriff's Office
White County Sheriff's Office
Accomack County Sheriff's Office
Crawford County Sheriff's Office
Jim Wells County Sheriff's Office
Marion County Sheriff's Office
Polk County Sheriff's Office
Emery County Sheriff's Office
Hampton County Sheriff's Office
Morgan County Sheriff's Office
Steuben County Sheriff's Office
Dade County Sheriff's Office
Lawrence County Sheriff's Office
Marlboro County Sheriff's Office
Newaygo County Sheriff's Office
Nez Perce County Sheriff's Office
Tuscola County Sheriff's Office
Dallas County Sheriff's Office
Emmet County Sheriff's Office
Graham County Sheriff's Office
Monroe County Sheriff's Office
Seneca County Sheriff's Office
Winn Parish Sheriff's Office
Columbiana County Sheriff's Office
Fayette County Sheriff's Office
Hardeman County Sheriff's Office
Harrison County Sheriff's Office
Haywood County Sheriff's Office
Rio Arriba County Sheriff's Office

Warren
Dearborn
Halifax
Orleans
Ravalli
San Benito
Williamson
Lee
White
Accomack
Crawford
Jim Wells
Marion
Polk
Emery
Hampton
Morgan
Steuben
Dade
Lawrence
Marlboro
Newaygo
Nez Perce
Tuscola
Dallas
Emmet
Graham
Monroe
Seneca
Winn
Columbiana
Fayette
Hardeman
Harrison
Haywood
Rio Arriba

NC
IN
VA
NY
MT
CA
IL
SC
TN
VA
AR
TX
WV
GA
UT
SC
IN
NY
GA
IN
SC
MI
ID
MI
IA
MI
AZ
WI
NY
LA
OH
OH
TN
IN
TN
NM

30
29
29
29
29
29
29
28
28
27
27
27
27
27
26
26
26
26
25
25
25
25
25
25
23
23
23
23
23
23
22
22
22
22
22
22

1
2
1
1
1
1
2
1
1
1
1
1
1
2
2
1
2
1
1
1
2
1
2
1
2
1
1
1
6
1
2
3
1
1
1
1

33.33
68.97
34.48
34.48
34.48
34.48
68.97
35.71
35.71
37.04
37.04
37.04
37.04
74.07
76.92
38.46
76.92
38.46
40.00
40.00
80.00
40.00
80.00
40.00
86.96
43.48
43.48
43.48
260.87
43.48
90.91
136.36
45.45
45.45
45.45
45.45

4.77
4.00
2.76
2.33
2.49
1.81
3.01
5.20
3.87
3.02
1.61
2.45
1.77
4.82
18.22
4.74
2.90
1.01
0.04
2.17
6.91
2.06
5.09
1.79
3.02
3.06
2.69
2.24
17.02
6.53
1.85
10.33
3.67
2.54
5.32
2.48

458
Routt County Sheriff's Office
St. Helena Parish Sheriff's Office
Mason County Sheriff's Office
San Jacinto County Sheriff's Office
Daviess County Sheriff's Office
Oglethorpe County Sheriff's Office
Pawnee County Sheriff's Office
Bolivar County Sheriff's Office
Calhoun County Sheriff's Office
Clarke County Sheriff's Office
Duval County Sheriff's Office
Osceola County Sheriff's Office
Towns County Sheriff's Office
Centre County Sheriff's Office
Cross County Sheriff's Office
Hunterdon County Sheriff's Office
Lincoln County Sheriff's Office
McIntosh County Sheriff's Office
Middlesex County Sheriff's Office
Dewitt County Sheriff's Office
Miller County Sheriff's Office
Phillips County Sheriff's Office
Greene County Sheriff's Office
Jackson County Sheriff's Office
Long County Sheriff's Office
McDowell County Sheriff's Office
Jackson County Sheriff's Office
Lowndes County Sheriff's Office
Morrow County Sheriff's Office
Benzie County Sheriff's Office
Hempstead County Sheriff's Office
Randolph County Sheriff's Office
Bibb County Sheriff's Office
Clayton County Sheriff's Office
Custer County Sheriff's Office
Franklin County Sheriff's Office

Routt
St. Helena
Mason
San Jacinto
Daviess
Oglethorpe
Pawnee
Bolivar
Calhoun
Clarke
Duval
Osceola
Towns
Centre
Cross
Hunterdon
Lincoln
McIntosh
Middlesex
De Witt
Miller
Phillips
Greene
Jackson
Long
McDowell
Jackson
Lowndes
Morrow
Benzie
Hempstead
Randolph
Bibb
Clayton
Custer
Franklin

CO
LA
WV
TX
IN
GA
OK
MS
FL
VA
TX
MI
GA
PA
AR
NJ
SD
OK
VA
IL
MO
AR
AR
KS
GA
WV
TN
AL
OR
MI
AR
IL
AL
IA
OK
IN

22
22
21
21
19
19
19
18
18
18
18
18
18
17
17
17
17
17
17
16
16
16
15
15
15
15
14
14
14
13
13
13
12
12
12
12

1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
1
1

45.45
45.45
142.86
47.62
52.63
52.63
52.63
55.56
55.56
55.56
55.56
55.56
55.56
58.82
58.82
58.82
58.82
117.65
58.82
62.50
62.50
62.50
66.67
66.67
66.67
66.67
214.29
71.43
71.43
76.92
76.92
76.92
83.33
83.33
83.33
83.33

4.25
8.93
10.98
3.79
3.16
6.71
6.03
2.93
6.84
7.13
8.49
4.25
9.55
0.65
5.60
0.78
2.23
9.88
9.12
6.04
4.04
4.60
2.38
7.43
6.91
4.52
25.78
8.85
8.95
5.71
4.42
2.99
4.36
5.52
3.64
4.33

459
Gage County Sheriff's Office
Lincoln County Sheriff's Office
Perry County Sheriff's Office
Whitley County Sheriff's Office
Floyd County Sheriff's Office
Greene County Sheriff's Office
Hockley County Sheriff's Office
Pulaski County Sheriff's Office
Roosevelt County Sheriff's Office
Simpson County Sheriff's Office
Stark County Sheriff's Office
Stephens County Sheriff's Office
Ferry County Sheriff's Office
La Salle County Sheriff's Office
Montague County Sheriff's Office
Powell County Sheriff's Office
Socorro County Sheriff's Office
Winkler County Sheriff's Office
Butler County Sheriff's Office
Lake County Sheriff's Office
Big Horn County Sheriff's Office
Blackford County Sheriff's Office
Henry County Sheriff's Office
Kingfisher County Sheriff's Office
Madison County Sheriff's Office
Talbot County Sheriff's Office
Clay County Sheriff's Office
Fremont County Sheriff's Office
Union County Sheriff's Office
Woodruff County Sheriff's Office
Estill County Sheriff's Office
Grant County Sheriff's Office
Holt County Sheriff's Office
Kent County Sheriff's Office
Lake County Sheriff's Office
Lewis County Sheriff's Office

Gage
Lincoln
Perry
Whitley
Floyd
Greene
Hockley
Pulaski
Roosevelt
Simpson
Stark
Stephens
Ferry
La Salle
Montague
Powell
Socorro
Winkler
Butler
Lake
Big Horn
Blackford
Henry
Kingfisher
Madison
Talbot
Clay
Fremont
Union
Woodruff
Estill
Grant
Holt
Kent
Lake
Lewis

NE
GA
IL
KY
IA
AL
TX
IL
MT
KY
ND
OK
WA
TX
TX
MT
NM
TX
AL
CO
MT
IN
AL
OK
MO
GA
AR
IA
IN
AR
KY
OK
NE
DE
SD
MO

12
12
12
12
11
11
11
11
11
11
11
11
10
10
10
10
10
10
9
9
8
8
8
8
8
8
7
7
6
6
5
5
5
5
5
5

1
1
1
1
1
1
2
2
1
1
1
2
1
3
1
1
1
1
3
2
1
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1

83.33
83.33
83.33
83.33
90.91
90.91
181.82
181.82
90.91
90.91
90.91
181.82
100.00
300.00
100.00
100.00
100.00
100.00
333.33
222.22
125.00
125.00
125.00
125.00
125.00
375.00
142.86
142.86
166.67
166.67
200.00
200.00
200.00
200.00
200.00
200.00

4.48
12.51
4.47
2.81
6.13
11.06
8.72
32.46
9.59
5.77
4.13
4.44
13.24
43.57
5.07
14.23
5.60
14.06
14.32
27.36
7.77
7.83
5.78
6.65
8.18
43.70
6.22
13.44
13.30
13.77
6.82
22.09
9.58
0.62
8.93
9.79

460
Mesilla Marshal's Office
Mora County Sheriff's Office
Oregon County Sheriff's Office
Pocahontas County Sheriff's Office
Terrell County Sheriff's Office
Bracken County Sheriff's Office
Keokuk County Sheriff's Office
Baker County Sheriff's Office
Carter County Sheriff's Office
Gallatin County Sheriff's Office
Griggs County Sheriff's Office
Guadalupe County Sheriff's Office
Nicholas County Sheriff's Office
Petroleum County Sheriff's Office
Dale County Sheriff's Office
Westmoreland County Sheriff's Office
Wicomico County Sheriff's Office

Dona Ana
Mora
Oregon
Pocahontas
Terrell
Bracken
Keokuk
Baker
Carter
Gallatin
Griggs
Guadalupe
Nicholas
Petroleum
Dale
Westmoreland
Wicomico

NM
NM
MO
WV
TX
KY
IA
GA
MO
IL
ND
NM
KY
MT
AL
PA
MD

5
5
5
5
5
4
4
3
3
3
3
3
1
1
0
0
0

1
1
2
1
1
1
1
1
2
1
1
1
1
1
1
1
1

200.00
200.00
400.00
200.00
200.00
250.00
250.00
333.33
666.67
333.33
333.33
333.33
1000.00
1000.00

0.48
20.49
18.38
11.47
101.63
11.78
9.51
28.98
31.92
17.89
41.32
21.34
14.02
202.43
1.99
0.27
1.01

461
Appendix A-4. County Police Departments in Study: Rates of Officers Arrested, 2005-2011 (Sorted by # FT Sworn)
# of Full-Time
Per
Agency
County
State Sworn Personnel
Agency
Miami-Dade (County) Police Department
Nassau County Police Department
Suffolk County Police Department
Honolulu (City and County) Police Department
Baltimore County Police Department
Charlotte - Mecklenburg Police Department
Indianapolis Police
Prince George's County Police Department
Fairfax County Police Department
Montgomery County Police Department
DeKalb County Police Department
St. Louis County Police Dept
Gwinnett County Police Department
Anne Arundel County Police Department
Cobb County Police Department
Henrico County Division of Police
Prince William County Police Department
Savannah-Chatham Metropolitan Police Department
Chesterfield County Police Department
Howard County Police Department
Arlington County Police Department
New Castle County Police Department
Clayton County Police Department
Maui (County) Police Department
Westchester County Department of Public Safety
Horry County Police Department
Henry County Police Department
Athens-Clarke County Police Dept
Allegheny County Police Department.
Roanoke County Police Department
Gaston County Police Department
Fulton County Police Department

Miami-Dade
Nassau
Suffolk
Honolulu
Baltimore
Mecklenburg
Marion
Prince Georges
Fairfax
Montgomery
DeKalb
St. Louis
Gwinnett
Anne Arundel
Cobb
Henrico
Prince William
Chatham
Chesterfield
Howard
Arlington
New Castle
Clayton
Maui
Westchester
Horry
Henry
Clarke
Allegheny
Roanoke
Gaston
Fulton

FL
NY
NY
HI
MD
NC
IN
MD
VA
MD
GA
MO
GA
MD
GA
VA
VA
GA
VA
MD
VA
DE
GA
HI
NY
SC
GA
GA
PA
VA
NC
GA

3093
2732
2622
1934
1910
1672
1582
1578
1419
1206
1074
781
682
633
590
554
546
534
475
424
364
358
336
329
270
243
225
213
202
135
133
129

Per 1,000
Officers
25
6
6
26
4
20
33
19
7
16
13
2
4
3
5
4
3
6
1
3
1
2
3
5
3
9
1
2
2
2
2
2

8.08
2.20
2.29
13.44
2.09
11.96
20.86
12.04
4.93
13.27
12.10
2.56
5.87
4.74
8.47
7.22
5.49
11.24
2.11
7.08
2.75
5.59
8.93
15.20
11.11
37.04
4.44
9.39
9.90
14.81
15.04
15.50

Per 100,000
Population
1.00
0.45
0.40
2.73
0.50
2.17
3.65
2.20
0.65
1.65
1.88
0.20
0.50
0.56
0.73
1.30
0.75
2.26
0.32
1.04
0.48
0.37
1.16
3.23
0.32
3.34
0.49
1.71
0.16
2.17
0.97
0.22

462
Kauai (County) Police Department
Riley County Police Department
James City County Police Dept.
Floyd County Police Department
Dougherty County Police Dept.
Polk County Police Department
Oldham County Police Department

Kauai
Riley
James City
Floyd
Dougherty
Polk
Oldham

HI
KS
VA
GA
GA
GA
KY

125
101
94
71
47
35
31

3
1
1
1
2
3
2

24.00
9.90
10.64
14.08
42.55
85.71
64.52

4.47
1.41
1.49
1.04
2.11
7.23
3.32

463
Appendix A-5. 500 Largest Municipal Police Departments in Study: Rates of Officers Arrested, 2005-2011 (Sorted by # FT Sworn)
# of Full-Time
Per
Per 1,000
Agency
County
State
Sworn Personnel
Agency
Officers
New York City Police Department
Chicago Police Dept
Los Angeles Police Department
Philadelphia Police Department
Houston Police Department
Washington Metropolitan Police Dept
Dallas Police Department
Phoenix Police Department
Baltimore Police Department
Las Vegas Metro Police Department
Detroit Police Department
Boston Police Department
San Antonio Police Department
Milwaukee Police Department
San Diego Police Department
San Francisco Police Department
Columbus Police Department
Atlanta Police Department
Jacksonville Sheriff's Office
Cleveland Police Department
Memphis Police Department
Denver Police Department
Austin Police Department
Fort Worth Police Department
New Orleans Police Department
Kansas City Police Department
San Jose Police Department
St. Louis (city) Police Dept
Nashville Metro Police Department
Newark Police
Seattle Police Department
Louisville Metro Police Department

New York
Cook
Los Angeles
Philadelphia
Harris
District of Columbia
Dallas
Maricopa
Baltimore City
Clark
Wayne
Suffolk
Bexar
Milwaukee
San Diego
San Francisco
Franklin
Fulton
Duval
Cuyahoga
Shelby
Denver
Travis
Tarrant
Orleans
Jackson
Santa Clara
St. Louis City
Davidson
Essex
King
Jefferson

NY
IL
CA
PA
TX
DC
TX
AZ
MD
NV
MI
MA
TX
WI
CA
CA
OH
GA
FL
OH
TN
CO
TX
TX
LA
MO
CA
MO
TN
NJ
WA
KY

36023
13354
9727
6624
5053
3742
3389
3388
2990
2942
2250
2181
2020
1987
1951
1940
1886
1719
1662
1616
1549
1525
1515
1489
1425
1421
1382
1351
1315
1310
1283
1197

196
83
30
66
35
29
39
17
55
8
23
14
30
73
12
7
9
22
12
27
46
13
10
20
63
5
9
10
18
8
9
16

Per 100,000
Population
5.44
6.22
3.08
9.96
6.93
7.75
11.51
5.02
18.39
2.72
10.22
6.42
14.85
36.74
6.15
3.61
4.77
12.80
7.22
16.71
29.70
8.52
6.60
13.43
44.21
3.52
6.51
7.40
13.69
6.11
7.01
13.37

2.40
1.60
0.31
4.33
0.86
4.82
1.65
0.45
8.86
0.41
1.26
1.94
1.75
7.70
0.39
0.87
0.77
2.39
1.39
2.11
4.96
2.17
0.98
1.11
18.32
0.74
0.51
3.13
2.87
1.02
0.47
2.16

464
El Paso Police Department
Miami Police Department
Cincinnati Police Department
Oklahoma City Police Department
Tucson Police Department
Albuquerque Police Department
Tampa Police Department
Long Beach Police Department
Portland Police Bureau
Minneapolis Police Department
Jersey City Police
Pittsburgh Police Department
Mesa Police Department
Fresno Police Department
Tulsa Police Department
Birmingham Police Department
Virginia Beach Police Department
Buffalo Police Department
Oakland Police Department
Norfolk Police Department
Orlando Police Department
Richmond Police Department
Omaha Police Dept
Rochester Police Department
Raleigh Police Department
Sacramento Police Department
Colorado Springs Police Department
Wichita Police Department
Yonkers Police Department
Toledo Police Department
Baton Rouge Police Department
Aurora Police Department
Arlington Police Department
St. Paul Police Department
Greensboro Police Department
Little Rock Police Department

El Paso
Miami-Dade
Hamilton
Oklahoma
Pima
Bernalillo
Hillsborough
Los Angeles
Multnomah
Hennepin
Hudson
Allegheny
Maricopa
Fresno
Tulsa
Jefferson
Virginia Beach City
Erie
Alameda
Norfolk City
Orange
Richmond City
Douglas
Monroe
Wake
Sacramento
El Paso
Sedgwick
Westchester
Lucas
East Baton Rouge
Arapahoe
Tarrant
Ramsey
Guilford
Pulaski

TX
FL
OH
OK
AZ
NM
FL
CA
OR
MN
NJ
PA
AZ
CA
OK
AL
VA
NY
CA
VA
FL
VA
NE
NY
NC
CA
CO
KS
NY
OH
LA
CO
TX
MN
NC
AR

1132
1104
1082
1046
1032
1020
980
968
928
902
900
891
831
828
826
816
813
793
773
772
757
752
747
703
702
701
668
662
641
640
630
627
610
598
593
520

15
14
10
12
6
13
3
4
10
18
12
21
2
7
11
8
9
4
3
14
8
7
6
3
8
5
5
2
2
12
6
6
3
8
5
2

13.25
12.68
9.24
11.47
5.81
12.75
3.06
4.13
10.78
19.96
13.33
23.57
2.41
8.45
13.32
9.80
11.07
5.04
3.88
18.13
10.57
9.31
8.03
4.27
11.40
7.13
7.49
3.02
3.12
18.75
9.52
9.57
4.92
13.38
8.43
3.85

1.87
0.56
1.25
1.67
0.61
1.96
0.24
0.04
1.36
1.56
1.89
1.72
0.05
0.75
1.82
1.21
2.05
0.44
0.20
5.77
0.70
3.43
1.16
0.40
0.89
0.35
0.80
0.40
0.21
2.72
1.36
1.05
0.17
1.57
1.02
0.52

465
Mobile Police Department
Shreveport Police Department
St. Petersburg Police Department
Winston-Salem Police Department
Montgomery Police Department
Paterson Police
Durham Police Department
Syracuse Police Department
Providence Police Department
Fort Lauderdale Police Department
Worcester Police Department
Jackson Police Department
Akron Police Department
North Las Vegas Police Department
Springfield Police Department
Corpus Christi Police Department
Fort Wayne Police
Madison Police Department
New Haven Police Department
Chattanooga Police Department
Salt Lake City Police Department
Laredo Police Department
Bridgeport Police Department
Dayton Police Department
Scottsdale Police Dept
Newport News Police Department
Stockton Police Department
Hartford Police Department
Huntsville Police Department
Columbus Police Department
Anaheim Police Department
Camden Police
Des Moines Police Department
Riverside Police Department
Charleston Police Department
Chesapeake Police Department

Mobile
Caddo
Pinellas
Forsyth
Montgomery
Passaic
Durham
Onondaga
Providence
Broward
Worcester
Hinds
Summit
Clark
Hampden
Nueces
Allen
Dane
New Haven
Hamilton
Salt Lake
Webb
Fairfield
Montgomery
Maricopa
Newport News City
San Joaquin
Hartford
Madison
Muscogee
Orange
Camden
Polk
Riverside
Charleston
Chesapeake City

AL
LA
FL
NC
AL
NJ
NC
NY
RI
FL
MA
MS
OH
NV
MA
TX
IN
WI
CT
TN
UT
TX
CT
OH
AZ
VA
CA
CT
AL
GA
CA
NJ
IA
CA
SC
VA

515
511
510
508
500
497
494
489
483
482
482
480
472
471
464
448
447
437
436
434
433
430
422
421
417
415
415
408
405
400
398
397
385
385
382
376

4
12
6
3
6
3
9
3
7
6
5
9
8
2
9
3
4
1
8
14
1
4
3
1
1
10
2
10
3
6
3
10
6
4
2
3

7.77
23.48
11.76
5.91
12.00
6.04
18.22
6.13
14.49
12.45
10.37
18.75
16.95
4.25
19.40
6.70
8.95
2.29
18.35
32.26
2.31
9.30
7.11
2.38
2.40
24.10
4.82
24.51
7.41
15.00
7.54
25.19
15.58
10.39
5.24
7.98

0.97
4.71
0.65
0.86
2.62
0.60
3.36
0.64
1.12
0.34
0.63
3.67
1.48
0.10
1.94
0.88
1.13
0.20
0.93
4.16
0.10
1.60
0.33
0.19
0.03
5.53
0.29
1.12
0.90
3.16
0.10
1.95
1.39
0.18
0.57
1.35

466
Lubbock Police Department
Miami Beach Police Department
Anchorage Police Department
Tacoma Police Department
Tallahassee Police Department
Reno Police Department
Trenton Police
Tempe Police Department
Kansas City Police Department
Columbia Police Department
Bakersfield Police Department
Elizabeth Police
San Bernardino Police Department
Irving Police Department
Plano Police Department
Salt Lake County Sheriff's Office
Fayetteville Police Department
Hialeah Police Department
Henderson Police Department
Chandler Police Department
Albany Police Department
North Charleston Police Department
Grand Rapids Police Department
Hollywood Police Department
Alexandria Police Department
West Palm Beach Police Department
Lincoln Police Dept
Boise Police Department
Springfield Police Dept
Wilmington Police Department
Joliet Police Dept
Rockford Police Dept
Spokane Police Department
Stamford Police Department
New Bedford Police Department
East Orange Police

Lubbock
Miami-Dade
Anchorage
Pierce
Leon
Washoe
Mercer
Maricopa
Wyandotte
Richland
Kern
Union
San Bernardino
Dallas
Collin
Salt Lake
Cumberland
Miami-Dade
Clark
Maricopa
Albany
Charleston
Kent
Broward
Alexandria City
Palm Beach
Lancaster
Ada
Greene
New Castle
Will
Winnebago
Spokane
Fairfield
Bristol
Essex

TX
FL
AK
WA
FL
NV
NJ
AZ
KS
SC
CA
NJ
CA
TX
TX
UT
NC
FL
NV
AZ
NY
SC
MI
FL
VA
FL
NE
ID
MO
DE
IL
IL
WA
CT
MA
NJ

376
374
372
371
364
362
361
357
354
351
348
348
345
344
343
342
341
338
336
333
328
325
319
316
315
310
308
306
306
306
302
300
295
292
288
283

5
3
4
3
1
3
6
1
10
8
8
1
2
2
1
1
2
1
2
2
7
5
1
8
3
3
1
2
3
2
3
1
6
3
2
3

13.30
8.02
10.75
8.09
2.75
8.29
16.62
2.80
28.25
22.79
22.99
2.87
5.80
5.81
2.92
2.92
5.87
2.96
5.95
6.01
21.34
15.38
3.13
25.32
9.52
9.68
3.25
6.54
9.80
6.54
9.93
3.33
20.34
10.27
6.94
10.60

1.79
0.12
1.37
0.38
0.36
0.71
1.64
0.03
6.35
2.08
0.95
0.19
0.10
0.08
0.13
0.10
0.63
0.04
0.10
0.05
2.30
1.43
0.17
0.46
2.14
0.23
0.35
0.51
1.09
0.37
0.44
0.34
1.27
0.33
0.36
0.38

467
Topeka Police Department
Evansville Police
Gainesville Police Department
McAllen Police Department
Springfield Police Dept
Cambridge Police Department
Macon Police Department
Wilmington Police Department
Glendale Police Department
Roanoke City Police Department
Tuscaloosa Police Department
Modesto Police Department
Pasadena Police Department
Waterbury Police Department
Clearwater Police Department
South Bend Police
Daytona Beach Police Department
Beaumont Police Department
Peoria Police Dept
Waco Police Department
Port St. Lucie Police Department
Chula Vista Police Department
Kalamazoo Dept of Public Safety
Gary Police
Lafayette Police Department
Lansing Police Department
Lowell Police Department
Clarksville Police Department
Pembroke Pines Police Department
Fall River Police Department
Portsmouth Police Department
Hampton Police Department
Brownsville Police Department
Warren Police Department
Oxnard Police Department
Lakeland Police Department

Shawnee
Vanderburgh
Alachua
Hidalgo
Sangamon
Middlesex
Bibb
New Hanover
Los Angeles
Roanoke City
Tuscaloosa
Stanislaus
Harris
New Haven
Pinellas
St. Joseph
Volusia
Jefferson
Peoria
McLennan
St. Lucie
San Diego
Kalamazoo
Lake
Lafayette
Ingham
Middlesex
Montgomery
Broward
Bristol
Portsmouth City
Hampton City
Cameron
Macomb
Ventura
Polk

KS
IN
FL
TX
IL
MA
GA
NC
CA
VA
AL
CA
TX
CT
FL
IN
FL
TX
IL
TX
FL
CA
MI
IN
LA
MI
MA
TN
FL
MA
VA
VA
TX
MI
CA
FL

283
277
275
273
273
272
270
266
264
264
263
262
260
256
255
255
250
246
246
246
245
244
244
243
243
240
239
238
238
237
235
232
230
230
228
226

4
3
4
3
1
1
9
2
1
2
1
5
5
4
1
8
6
2
1
8
1
1
1
10
1
3
2
5
1
1
10
2
1
1
3
5

14.13
10.83
14.55
10.99
3.66
3.68
33.33
7.52
3.79
7.58
3.80
19.08
19.23
15.63
3.92
31.37
24.00
8.13
4.07
32.52
4.08
4.10
4.10
41.15
4.12
12.50
8.37
21.01
4.20
4.22
42.55
8.62
4.35
4.35
13.16
22.12

2.25
1.67
1.62
0.39
0.51
0.07
5.79
0.99
0.01
2.06
0.51
0.97
0.12
0.46
0.11
3.00
1.21
0.79
0.54
3.41
0.36
0.03
0.40
2.02
0.45
1.07
0.13
7.54
0.06
0.18
10.47
1.46
0.25
0.12
0.36
0.83

468
Mesquite Police Department
Huntington Beach Police Department
Sioux Falls Police Department
Cape Coral Police Department
Manchester Police Department
Murfreesboro Police Department
Oceanside Police Department
White Plains Police Department
Independence Police Department
Woodbridge Police
Mount Vernon Police Department
Quincy Police Department
Flint Police Department
Jackson Police Department
Allentown Police Department
Dearborn Police Department
Everett Police Department
Brockton Police Department
Cedar Rapids Police Department
Irvine Police Department
Pueblo Police Dept
Racine Police Department
Gulfport Police Department
Irvington Police
Killeen Police Department
Edison Police
Peoria Police Department
Monroe Police Department
Inglewood Police Department
Salem Police Department
Berkeley Police Department
West Valley City Police Department
Hayward Police Department
New Rochelle Police Department
Albany Police Department
Coral Gables Police Department

Dallas
Orange
Minnehaha
Lee
Hillsborough
Rutherford
San Diego
Westchester
Jackson
Middlesex
Westchester
Norfolk
Genesee
Madison
Lehigh
Wayne
Snohomish
Plymouth
Linn
Orange
Pueblo
Racine
Harrison
Essex
Bell
Middlesex
Maricopa
Ouachita
Los Angeles
Marion
Alameda
Salt Lake
Alameda
Westchester
Dougherty
Miami-Dade

TX
CA
SD
FL
NH
TN
CA
NY
MO
NJ
NY
MA
MI
TN
PA
MI
WA
MA
IA
CA
CO
WI
MS
NJ
TX
NJ
AZ
LA
CA
OR
CA
UT
CA
NY
GA
FL

226
223
221
220
218
213
210
210
206
206
205
205
204
204
200
198
198
197
197
197
195
195
193
190
190
189
189
188
187
187
186
186
185
185
184
184

1
1
1
2
1
5
4
1
1
1
1
1
6
4
1
4
1
3
2
1
4
1
1
2
3
7
1
1
1
1
1
1
1
2
4
2

4.42
4.48
4.52
9.09
4.59
23.47
19.05
4.76
4.85
4.85
4.88
4.88
29.41
19.61
5.00
20.20
5.05
15.23
10.15
5.08
20.51
5.13
5.18
10.53
15.79
37.04
5.29
5.32
5.35
5.35
5.38
5.38
5.41
10.81
21.74
10.87

0.04
0.03
0.59
0.32
0.25
1.90
0.13
0.11
0.15
0.12
0.11
0.15
1.41
4.07
0.29
0.22
0.14
0.61
0.95
0.03
2.51
0.51
0.53
0.26
0.97
0.86
0.03
0.65
0.01
0.32
0.07
0.10
0.07
0.21
4.23
0.08

469
Naperville Police Dept
Charleston Police Department
Fremont Police Department
Elgin Police Dept
Myrtle Beach Police Department
Utica Police Department
Bossier City Police Dept
Greenville Police Department
Lynn Police Department
Westminster Police Dept
Salinas Police Department
Wichita Falls Police Department
Sunrise Police Department
Canton Police Department
Nashua Police Department
Miramar Police Department
Suffolk Police Department
Fort Myers Police Department
Erie Police Department
Las Cruces Police Department
Norwalk Police Department
Yuma Police Department
Decatur Police Dept
Garden Grove Police Department
Gastonia Police Department
Schenectady Police Department
Boulder Police Department
Boynton Beach Police Department
Lawton Police Department
Richmond Police Department
Union City Police
Davie Police Department
Melbourne Police Department
Fort Collins Police Department
Kenner Police Department
Concord Police Department

Du Page
Kanawha
Alameda
Kane
Horry
Oneida
Bossier
Greenville
Essex
Adams
Monterey
Wichita
Broward
Stark
Hillsborough
Broward
Suffolk
Lee
Erie
Dona Ana
Fairfield
Yuma
Macon
Orange
Gaston
Schenectady
Boulder
Palm Beach
Comanche
Contra Costa
Hudson
Broward
Brevard
Larimer
Jefferson
Contra Costa

IL
WV
CA
IL
SC
NY
LA
SC
MA
CO
CA
TX
FL
OH
NH
FL
VA
FL
PA
NM
CT
AZ
IL
CA
NC
NY
CO
FL
OK
CA
NJ
FL
FL
CO
LA
CA

184
182
182
181
179
179
178
178
178
178
177
177
175
172
172
171
171
170
167
167
167
167
166
166
166
166
165
165
165
165
165
164
163
162
162
161

1
9
1
5
5
2
3
4
2
4
4
2
1
1
1
2
1
4
1
1
5
2
1
2
2
8
1
7
1
3
3
2
1
3
1
1

5.43
49.45
5.49
27.62
27.93
11.17
16.85
22.47
11.24
22.47
22.60
11.30
5.71
5.81
5.81
11.70
5.85
23.53
5.99
5.99
29.94
11.98
6.02
12.05
12.05
48.19
6.06
42.42
6.06
18.18
18.18
12.20
6.13
18.52
6.17
6.21

0.11
4.66
0.07
0.97
1.86
0.85
2.56
0.89
0.27
0.91
0.96
1.52
0.06
0.27
0.25
0.11
1.18
0.65
0.36
0.48
0.55
1.02
0.90
0.07
0.97
5.17
0.34
0.53
0.81
0.29
0.47
0.11
0.18
1.00
0.23
0.10

470
Galveston Police Department
Lancaster Police Department
Olathe Police Department
Ann Arbor Police Department
New Britain Police Department
Fullerton Police Department
Palm Bay Police Department
Portland Police Department
Clifton Police
Costa Mesa Police Department
Fort Smith Police Department
Vineland Police
Hoboken Police
Columbia Police Department
Denton Police Department
Harrisburg Police Department
Ocala Police Department
Scranton Police Department
Waukegan Police Dept
Bethlehem Police Department
Concord Police Department
Danbury Police Department
Lake Charles Police Department
Midland Police Department
Pawtucket Police Department
Delray Beach Police Department
Lawrence Police Department
Plainfield Police Department
Santa Fe Police Department
Cranston Police Department
Dothan Police Department
Cicero Police Dept
Greeley Police Department
Johnson City Police Department
Pensacola Police Department
Cherry Hill Police

Galveston
Lancaster
Johnson
Washtenaw
Hartford
Orange
Brevard
Cumberland
Passaic
Orange
Sebastian
Cumberland
Hudson
Boone
Denton
Dauphin
Marion
Lackawanna
Lake
Northampton
Cabarrus
Fairfield
Calcasieu
Midland
Providence
Palm Beach
Essex
Union
Santa Fe
Providence
Houston
Cook
Weld
Washington
Escambia
Camden

TX
PA
KS
MI
CT
CA
FL
ME
NJ
CA
AR
NJ
NJ
MO
TX
PA
FL
PA
IL
PA
NC
CT
LA
TX
RI
FL
MA
NJ
NM
RI
AL
IL
CO
TN
FL
NJ

161
161
161
160
160
159
159
159
158
158
158
157
156
155
155
155
155
155
155
154
153
153
153
153
153
152
151
151
150
148
148
146
146
146
146
145

1
2
1
4
2
3
1
3
1
1
3
2
3
1
1
1
2
4
2
4
1
1
2
1
2
1
4
5
2
3
2
4
2
1
1
2

6.21
12.42
6.21
25.00
12.50
18.87
6.29
18.87
6.33
6.33
18.99
12.74
19.23
6.45
6.45
6.45
12.90
25.81
12.90
25.97
6.54
6.54
13.07
6.54
13.07
6.58
26.49
33.11
13.33
20.27
13.51
27.40
13.70
6.85
6.85
13.79

0.34
0.39
0.18
1.16
0.22
0.10
0.18
1.07
0.20
0.03
2.39
1.27
0.47
0.61
0.15
0.37
0.60
1.87
0.28
1.34
0.56
0.11
1.04
0.73
0.32
0.08
0.54
0.93
1.39
1.81
1.97
0.08
0.79
0.81
0.34
0.39

471
Niagara Falls Police Department
Rocky Mount Police Department
Santa Clara Police Department
Largo Police Department
Newport Beach Police Department
Pine Bluff Police Department
Binghamton Police Department
Billings Police Department
Frederick Police Department
New Brunswick Police
Mount Pleasant Police Department
Ogden Police Department
Baytown Police Department
Kissimmee Police Department
Longmont Police Department
Newton Police Department
Santa Barbara Police Department
Visalia Department of Public Safety
Hamilton Police Department
Yakima Police Department
Biloxi Police Department
Chicopee Police Department
Decatur Police Department
Federal Way Police Department
Marietta Police Department
Round Rock Police Department
Valdosta Police Department
Sanford Police Department
Somerville Police Department
Sugar Land Police Department
Brookline Police Department
Farmington Police Department
Gresham Police Department
Lafayette Police
Brick Township Police
Springfield Police Department

Niagara
Nash
Santa Clara
Pinellas
Orange
Jefferson
Broome
Yellowstone
Frederick
Middlesex
Charleston
Weber
Harris
Osceola
Boulder
Middlesex
Santa Barbara
Tulare
Butler
Yakima
Harrison
Hampden
Morgan
King
Cobb
Williamson
Lowndes
Seminole
Middlesex
Fort Bend
Norfolk
San Juan
Multnomah
Tippecanoe
Ocean
Clark

NY
NC
CA
FL
CA
AR
NY
MT
MD
NJ
SC
UT
TX
FL
CO
MA
CA
CA
OH
WA
MS
MA
AL
WA
GA
TX
GA
FL
MA
TX
MA
NM
OR
IN
NJ
OH

145
143
141
140
140
140
139
138
138
138
137
137
136
136
136
136
136
136
135
134
133
133
133
133
133
133
133
131
130
130
129
129
129
128
127
127

3
1
2
1
1
1
1
2
1
2
1
2
1
3
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
2
3
5
1
1
1
1

20.69
6.99
14.18
7.14
7.14
7.14
7.19
14.49
7.25
14.49
7.30
14.60
7.35
22.06
7.35
7.35
7.35
7.35
7.41
7.46
15.04
7.52
7.52
7.52
7.52
7.52
7.52
7.63
15.38
15.38
23.26
38.76
7.75
7.81
7.87
7.87

1.39
1.77
0.11
0.11
0.03
1.29
0.50
1.35
0.43
0.25
0.29
0.86
0.02
1.12
0.34
0.07
0.24
0.23
0.27
0.41
1.07
0.22
0.84
0.05
0.15
0.24
0.92
0.24
0.13
0.34
0.45
3.84
0.14
0.58
0.17
0.72

472
Surprise Police Department
Danville Police Department
Elk Grove Police Department
Fort Pierce Police Department
Hattiesburg Police Department
Mission Police Department
Rochester Police Department
Altamonte Springs Police Department
Beaverton Police Department
Broken Arrow Police Department
Elkhart Police
Harlingen Police Department
Holyoke Police Department
North Bergen Police
Bloomington Police Dept
Bolingbrook Police Dept
Champaign Police Dept
West Haven Police Department
Rock Hill Police Department
Troy Police Department
El Cajon Police Department
Ramapo Town Police Department
Sandy Springs Police Department
Waterloo Police Department
Bristol Police Department
West New York Police
Framingham Police Department
Redding Police Department
Anderson Police
Fayetteville Police Department
Hackensack Police
Wayne Township Police
East Point Police Department
Manchester Police Department
Charlottesville Police Dept.
Lauderhill Police Department

Maricopa
Pittsylvania
Sacramento
St. Lucie
Forrest
Hidalgo
Olmsted
Seminole
Washington
Tulsa
Elkhart
Cameron
Hampden
Hudson
McLean
Will
Champaign
New Haven
York
Rensselaer
San Diego
Rockland
Fulton
Black Hawk
Hartford
Hudson
Middlesex
Shasta
Madison
Washington
Bergen
Passaic
Fulton
Hartford
Charlottesville City
Broward

AZ
VA
CA
FL
MS
TX
MN
FL
OR
OK
IN
TX
MA
NJ
IL
IL
IL
CT
SC
NY
CA
NY
GA
IA
CT
NJ
MA
CA
IN
AR
NJ
NJ
GA
CT
VA
FL

127
126
126
126
126
125
125
124
124
124
124
123
123
123
122
122
122
122
121
121
120
120
120
120
119
119
118
118
117
117
117
117
116
116
115
115

1
1
1
4
2
2
1
1
1
2
4
1
1
1
2
3
1
2
1
1
1
1
3
4
1
1
1
1
4
2
9
1
2
2
1
1

7.87
7.94
7.94
31.75
15.87
16.00
8.00
8.06
8.06
16.13
32.26
8.13
8.13
8.13
16.39
24.59
8.20
16.39
8.26
8.26
8.33
8.33
25.00
33.33
8.40
8.40
8.47
8.47
34.19
17.09
76.92
8.55
17.24
17.24
8.70
8.70

0.03
1.57
0.38
1.44
2.67
0.26
0.69
0.24
0.19
0.33
2.02
0.25
0.22
0.16
1.18
0.44
0.50
0.23
0.44
0.63
0.03
0.32
0.33
3.05
0.11
0.16
0.07
0.56
3.04
0.98
0.99
0.20
0.22
0.22
2.30
0.06

473
Oak Park Police Dept
Ottawa County Sheriff's Office
Tupelo Police Department
Hickory Police Department
Palm Beach Gardens Police Department
St. Joseph Police Dept
Wilson Police Department
Daly City Police Department
Flagstaff Police Department
Meriden Police Department
Covington Police Department
Hempstead Village Police Department
St. Charles Police Dept
Gloucester Township Police
Kingsport Police Department
Stratford Police Department
Vacaville Police Department
Barnstable Police Department
East Chicago Police
Edinburg Police Department
Edmond Police Department
Medford Police Department
Milford Police Department
Sandy Police Department
York Police Department
Colonie Town Police Department
Florence Police Department
Grand Junction Police Department
Riviera Beach Police Department
Ocean City Police Department
O'Fallon Police Department
Rapid City Police Department
Sumter Police Department
Allen Police Department
Homestead Police Department
Oak Lawn Police Dept

Cook
Ottawa
Lee
Catawba
Palm Beach
Buchanan
Wilson
San Mateo
Coconino
New Haven
Kenton
Nassau
St. Charles
Camden
Sullivan
Fairfield
Solano
Barnstable
Lake
Hidalgo
Oklahoma
Middlesex
New Haven
Salt Lake
York
Albany
Florence
Mesa
Palm Beach
Worcester
St. Charles
Pennington
Sumter
Collin
Miami-Dade
Cook

IL
MI
MS
NC
FL
MO
NC
CA
AZ
CT
KY
NY
MO
NJ
TN
CT
CA
MA
IN
TX
OK
MA
CT
UT
PA
NY
SC
CO
FL
MD
MO
SD
SC
TX
FL
IL

115
115
115
114
114
114
114
113
113
113
112
112
112
111
111
111
111
110
110
110
110
110
110
110
110
109
109
108
108
107
107
107
107
106
106
106

1
1
1
1
2
1
2
1
1
1
1
1
1
2
3
3
1
2
2
5
2
1
1
1
1
2
2
2
6
1
1
2
3
2
3
1

8.70
8.70
8.70
8.77
17.54
8.77
17.54
8.85
8.85
8.85
8.93
8.93
8.93
18.02
27.03
27.03
9.01
18.18
18.18
45.45
18.18
9.09
9.09
9.09
9.09
18.35
18.35
18.52
55.56
9.35
9.35
18.69
28.04
18.87
28.30
9.43

0.02
0.38
1.21
0.65
0.15
1.12
2.46
0.14
0.74
0.12
0.63
0.07
0.28
0.39
1.91
0.33
0.24
0.93
0.40
0.65
0.28
0.07
0.12
0.10
0.23
0.66
1.46
1.36
0.45
1.94
0.28
1.98
2.79
0.26
0.12
0.02

474
Victoria Police Department
Bartlett Police Department
Berwyn Police Dept
Broomfield Police Department
Burlington Police Department
Council Bluffs Police Department
Hagerstown Police Department
Hamden Police Department
Merced Police Department
Newburgh City Police Department
Poughkeepsie Police Department
Bensalem Township Police Department
Carmel Police
Lakewood Police Department
Medford Police Department
Oro Valley Police Department
Des Plaines Police Dept
Jupiter Police Department
Muncie Police
Southampton Town Police Department
West Jordan Police Department
Goldsboro Police Department
Dubuque Police Department
Euclid Police Department
Kokomo Police
Lorain Police Department
Missoula Police Department
Westminster Police Department
Conroe Police Department
Meridian Police Department
Middletown Police Department
Pinellas Park Police Department
Provo Police Department
Redondo Beach Police Department
Rogers Police Department
Woonsocket Police Department

Victoria
Shelby
Cook
Broomfield
Alamance
Pottawattomie
Washington
New Haven
Merced
Orange
Dutchess
Bucks
Hamilton
Pierce
Jackson
Pima
Cook
Palm Beach
Delaware
Suffolk
Salt Lake
Wayne
Dubuque
Cuyahoga
Howard
Lorain
Missoula
Orange
Montgomery
Lauderdale
Middlesex
Pinellas
Utah
Los Angeles
Benton
Providence

TX
TN
IL
CO
NC
IA
MD
CT
CA
NY
NY
PA
IN
WA
OR
AZ
IL
FL
IN
NY
UT
NC
IA
OH
IN
OH
MT
CA
TX
MS
CT
FL
UT
CA
AR
RI

106
105
105
105
105
105
105
105
105
105
105
104
104
103
103
103
102
102
102
102
102
101
100
100
100
100
100
100
99
99
99
99
99
99
99
99

2
1
3
1
3
1
1
2
1
1
1
1
3
1
2
1
1
1
3
1
2
1
3
1
1
5
1
2
2
3
2
2
3
1
1
4

18.87
9.52
28.57
9.52
28.57
9.52
9.52
19.05
9.52
9.52
9.52
9.62
28.85
9.71
19.42
9.71
9.80
9.80
29.41
9.80
19.61
9.90
30.00
10.00
10.00
50.00
10.00
20.00
20.20
30.30
20.20
20.20
30.30
10.10
10.10
40.40

2.30
0.11
0.06
1.79
1.99
1.07
0.68
0.23
0.39
0.27
0.34
0.16
1.09
0.13
0.98
0.10
0.02
0.08
2.55
0.07
0.19
0.82
3.20
0.08
1.21
1.66
0.91
0.07
0.44
3.74
1.21
0.22
0.58
0.01
0.45
0.64

475
East Providence Police Department
Eau Claire Police Department
Greenville Police Department
Pharr Police Department
Weymouth Police Department
Egg Harbor Township Police
Enfield Police Department
San Leandro Police Department
San Marcos Police Department
Alameda Police Department
Florence Police Department
Greece Town Police Department
Texarkana Police Department
College Park Police Department
Haverhill Police Department
Palm Springs Police Department
Palo Alto Police Department
Bellevue Police Dept
Chelsea Police Department
Joplin Police Department
New Braunfels Police Department
Parma Police Department
Saginaw Police Department
Wilkes Barre City Police Department
Anderson Police Department
Calumet City Police Dept
Dover Police Department
Grapevine Police Department
Kennewick Police Department
Muskogee Police Department
New Bern Police Department
New London Police Department
Orangetown Town Police Department
Orem Department of Public Safety
Piscataway Township Police
West Chester Police Department

Providence
Eau Claire
Washington
Hidalgo
Norfolk
Atlantic
Hartford
Alameda
Hays
Alameda
Lauderdale
Monroe
Bowie
Fulton
Essex
Riverside
Santa Clara
Sarpy
Suffolk
Jasper
Comal
Cuyahoga
Saginaw
Luzerne
Anderson
Cook
Kent
Tarrant
Benton
Muskogee
Craven
New London
Rockland
Utah
Middlesex
Butler

RI
WI
MS
TX
MA
NJ
CT
CA
TX
CA
AL
NY
TX
GA
MA
CA
CA
NE
MA
MO
TX
OH
MI
PA
SC
IL
DE
TX
WA
OK
NC
CT
NY
UT
NJ
OH

98
96
96
96
96
95
95
95
95
94
94
94
94
93
93
93
93
92
92
92
92
92
92
92
91
91
90
90
90
90
90
90
90
90
90
90

1
1
2
2
1
2
4
2
1
2
1
4
1
1
1
1
1
1
1
1
2
3
1
1
3
1
2
1
1
4
1
2
1
1
1
1

10.20
10.42
20.83
20.83
10.42
21.05
42.11
21.05
10.53
21.28
10.64
42.55
10.64
10.75
10.75
10.75
10.75
10.87
10.87
10.87
21.74
32.61
10.87
10.87
32.97
10.99
22.22
11.11
11.11
44.44
11.11
22.22
11.11
11.11
11.11
11.11

0.16
1.01
3.91
0.26
0.15
0.73
0.45
0.13
0.64
0.13
1.08
0.54
1.08
0.11
0.13
0.05
0.06
0.63
0.14
0.85
1.84
0.23
0.50
0.31
1.60
0.02
1.23
0.06
0.57
5.63
0.97
0.73
0.32
0.19
0.12
0.27

476
Cleveland Police Department
Coconut Creek Police Department
Idaho Falls Police Department
Chico Police Department
Folsom Police Department
Jefferson City Police Department
Salisbury Police Department
Titusville Police Department
Bend Police Department
Cuyahoga Falls Police Department
Elyria Police Department
Michigan City Police
Panama City Police Department
Revere Police Department
Salisbury Police Department
Washington Township Police
Apopka Police Department
Moline Police Department
Pleasanton Police Department
Royal Oak Police Department
Texas City Police Department
Wheeling Police Department
Alhambra Police Department
Citrus Heights Police Department
Euless Police Department
LaGrange Police Department
North Brunswick Police
Port Orange Police Department
Salem Police Department
Mentor Police Department
Petersburg Police Department
Belleville Police Dept
Downers Grove Police Dept
Norwich Police Department
Sanford Police Department
Tinley Park Police Dept

Bradley
Broward
Bonneville
Butte
Sacramento
Cole
Wicomico
Brevard
Deschutes
Summit
Lorain
La Porte
Bay
Suffolk
Rowan
Gloucester
Orange
Rock Island
Alameda
Oakland
Galveston
Ohio
Los Angeles
Sacramento
Tarrant
Troup
Middlesex
Volusia
Essex
Lake
Petersburg City
St. Clair
Du Page
New London
Lee
Cook

TN
FL
ID
CA
CA
MO
MD
FL
OR
OH
OH
IN
FL
MA
NC
NJ
FL
IL
CA
MI
TX
WV
CA
CA
TX
GA
NJ
FL
MA
OH
VA
IL
IL
CT
NC
IL

89
89
89
88
88
88
88
88
86
86
86
86
86
86
86
86
85
85
85
85
85
84
83
83
83
83
83
83
83
82
82
81
81
81
81
81

4
3
1
2
2
1
1
1
2
2
2
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
2
2
1
2
1
1
5
1
2

44.94
33.71
11.24
22.73
22.73
11.36
11.36
11.36
23.26
23.26
23.26
11.63
11.63
11.63
11.63
11.63
11.76
11.76
11.76
11.76
23.53
23.81
12.05
12.05
12.05
12.05
12.05
24.10
24.10
12.20
24.39
12.35
12.35
61.73
12.35
24.69

4.04
0.17
0.96
0.91
0.14
1.32
1.01
0.18
1.27
0.37
0.66
0.90
0.59
0.14
0.72
0.35
0.09
0.68
0.07
0.08
0.69
4.50
0.01
0.07
0.06
1.49
0.12
0.40
0.27
0.43
6.17
0.37
0.11
1.82
1.73
0.04

477
West Bloomfield Township Police Dept

Oakland

12.35

0.08

478
Appendix A-6. Special State & Local Law Enforcement Agencies in Study: Rates of Officers Arrested, 2005-2011 (Sorted by # FT Sworn)
# of Full-Time
Per
Per 1,000
Agency
County
State Sworn Personnel
Agency
Officers
Port Authority of New York & New Jersey Police Dept
New York State Metro Transportation Auth. Police
Florida Fish & Wildlife Conservation Commission
District of Columbia Protective Services Police
Maryland Transportation Authority Police
School District of Philadelphia Police
Washington Metropolitan Area Transit Auth. Police
California Dept. of Justice
Ohio Dept of Natural Resources - Ofc. of Law Enf.
Los Angeles School Police Department
New York State Park Police
Texas Alcoholic Beverage Commission
MBTA Transit Police
South Carolina Department of Natural Resources
Mississippi Department of Wildlife, Fisheries & Parks
Maryland Natural Resources Police
Metropolitan Washington Airports Authority Police
New Jersey Transit Police
BART Police Department
Palm Beach County School District Police
New York City Dept of Environmental Protection Police
Delaware River Port Authority - Transit Police
Baltimore City School Police Dept.
Maryland Transit Administration Police
Pennsylvania Dept of Conservation & Natural Resources
South Carolina Dept of Mental Health-Public Safety
San Antonio Park Rangers
New Mexico Department Of Game & Fish
Wisconsin Dept of Justice - Criminal Investigation Div.
Vanderbilt University Police Department
New Jersey Department of Environmental Protection
Dallas I.S.D. Police Dept.

Hudson
New York
Leon
District of Columbia
Baltimore
Philadelphia
District of Columbia
Sacramento
Franklin
Los Angeles
Albany
Travis
Suffolk
Richland
Hinds
Anne Arundel
Arlington
Essex
Alameda
Palm Beach
Westchester
Camden
Baltimore City
Baltimore City
Dauphin
Richland
Bexar
Santa Fe
Dane
Davidson
Burlington
Dallas

NJ
NY
FL
DC
MD
PA
DC
CA
OH
CA
NY
TX
MA
SC
MS
MD
VA
NJ
CA
FL
NY
NJ
MD
MD
PA
SC
TX
NM
WI
TN
NJ
TX

1667
694
626
484
456
450
442
419
394
340
305
277
256
238
230
224
206
201
192
176
168
144
142
140
136
120
112
106
92
91
90
88

1
2
4
1
1
1
2
1
2
2
1
1
2
1
1
1
1
3
1
4
1
1
1
1
1
1
3
1
1
1
1
1

0.60
2.88
6.39
2.07
2.19
2.22
4.52
2.39
5.08
5.88
3.28
3.61
7.81
4.20
4.35
4.46
4.85
14.93
5.21
22.73
5.95
6.94
7.04
7.14
7.35
8.33
26.79
9.43
10.87
10.99
11.11
11.36

Per 100,000
Population
0.16
0.13
0.44
0.17
0.16
0.07
0.33
0.10
3.24
0.02
0.21
0.10
0.28
0.26
1.05
0.19
0.09
0.47
0.07
0.30
1.29
0.19
0.16
0.16
0.79
0.26
0.17
0.69
0.20
0.16
0.11
0.04

479
Indiana State Excise Police
Maryland National Capital Park Police - Montgomery Co
University of Florida Police
Northside I.S.D. Police Dept.
Boston School Police
Middlesex County Prosecutor's Office
University of Alabama - Birmingham Police Dept
Virginia Commonwealth University Police Dept.
California Bureau of Narcotics Enforcement
University of Pittsburgh - Main Campus Police
Austin I.S.D. Police Department
Georgia State University Police
Maryland Department of General Services Police
Anchorage International Airport Police
Louisiana State University Police Department
Medical University of South Carolina Public Safety
University of Texas - Austin Police
Duke University Police Department
Virginia Marine Resources Commission
Volusia County Beach Patrol
MIT Police Department
Northern Illinois University Police
University of California - Los Angeles Police
University of Illinois Police Dept
George Mason University Police Dept.
University of Tennessee at Knoxville Police
Ohio State University Police Department
United I.S.D. Police Dept.
Delaware River & Bay Authority Police
Louisiana State Univ. Health Sciences Center Police Dept
Wayne State University Dept of Public Safety
Houston Community College System
McAllen I.S.D. Police Dept.
Southern University and A & M College Police
Allegheny Port Authority Transit Police
State University at Albany Police

Marion
Montgomery
Alachua
Bexar
Suffolk
Middlesex
Jefferson
Richmond City
Sacramento
Allegheny
Travis
Fulton
Baltimore City
Anchorage
East Baton Rouge
Charleston
Travis
Durham
Newport News City
Volusia
Middlesex
DeKalb
Los Angeles
Champaign
Fairfax
Knox
Franklin
Webb
New Castle
Caddo
Wayne
Harris
Hidalgo
East Baton Rouge
Allegheny
Albany

IN
MD
FL
TX
MA
NJ
AL
VA
CA
PA
TX
GA
MD
AK
LA
SC
TX
NC
VA
FL
MA
IL
CA
IL
VA
TN
OH
TX
DE
LA
MI
TX
TX
LA
PA
NY

88
86
85
83
80
80
79
74
73
73
70
68
68
65
62
62
62
60
60
60
59
59
57
54
52
52
51
51
50
49
49
48
43
43
42
41

1
1
1
2
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1

11.36
11.63
11.76
24.10
12.50
12.50
12.66
27.03
27.40
13.70
14.29
14.71
14.71
15.38
16.13
16.13
16.13
16.67
16.67
33.33
16.95
16.95
17.54
18.52
19.23
38.46
19.61
19.61
20.00
20.41
20.41
20.83
23.26
23.26
23.81
24.39

0.11
0.41
0.40
0.12
0.14
0.12
0.15
0.98
0.02
0.08
0.10
0.11
0.16
0.34
0.23
0.29
0.10
0.37
3.02
0.40
0.07
0.95
0.01
0.50
0.09
10.47
0.09
0.40
0.51
0.39
0.05
0.02
0.13
0.23
0.08
0.33

480
Massachusetts Dept of Mental Health Police Dept
University of North Texas Police Department
Florida Atlantic University Police
Florida International University Police
Kansas State Law Enforcement Training Center
University of Arkansas Medical Sciences Dept of Pub
Tuskegee University Police Department
Medical College of Georgia
Buffalo State College Police
Cameron Co. District Attorney Investigations Div
Houston Baptist University Police Department
Kentucky Alcoholic Beverage Control
Ohio Department of Taxation - Enforcement Division
Socorro I.S.D. Police Dept.
Dayton International Airport Police Dept
Tennessee State University
Appalachian State University Police Dept
Humble I.S.D. Police Dept.
Beaumont I.S.D. Police Dept.
Central Michigan University Police Department
North Forest I.S.D. Police Dept.
Santa Ana Unified School District Police Dept.
St. Mary's University Police Department
Georgia Public Safety Training Center
Indiana University Purdue U. Fort Wayne U. Police
Lafayette College Office of Public Safety
Arkansas State University Police Dept.
Shippensburg University of Pennsylvania Police
St. Joseph County Airport Police
Bowie State University Dept. of Public Safety
St. Edward's University Police Department
University of Colorado - Colorado Springs Police Dept.
College of Lake County Police Dept
Missouri Department of Corrections
University of Pittsburgh at Johnstown Police
University of South Carolina - Upstate Police Dept

Suffolk
Denton
Palm Beach
Miami-Dade
Reno
Pulaski
Macon
Richmond
Erie
Cameron
Harris
Franklin
Franklin
El Paso
Montgomery
Davidson
Watauga
Harris
Jefferson
Isabella
Harris
Orange
Bexar
Monroe
Allen
Northampton
Craighead
Cumberland
St. Joseph
Prince Georges
Travis
El Paso
Lake
Cole
Cambria
Spartanburg

MA
TX
FL
FL
KS
AR
AL
GA
NY
TX
TX
KY
OH
TX
OH
TN
NC
TX
TX
MI
TX
CA
TX
GA
IN
PA
AR
PA
IN
MD
TX
CO
IL
MO
PA
SC

40
40
39
39
39
36
33
32
30
30
30
30
30
30
29
27
25
24
22
21
21
21
20
19
18
18
17
17
17
14
14
14
13
13
13
12

2
1
1
1
1
1
1
1
1
1
1
2
1
1
1
2
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1

50.00
25.00
25.64
25.64
25.64
27.78
30.30
31.25
33.33
33.33
33.33
66.67
33.33
33.33
34.48
74.07
40.00
41.67
45.45
47.62
47.62
95.24
50.00
52.63
55.56
55.56
58.82
58.82
58.82
71.43
71.43
71.43
76.92
76.92
76.92
83.33

0.28
0.15
0.08
0.04
0.20
0.26
4.66
0.50
0.11
0.25
0.02
0.27
0.18
0.12
0.19
0.32
1.96
0.02
0.40
1.42
0.02
0.07
0.06
3.78
0.28
0.34
1.04
0.42
0.37
0.12
0.10
0.16
0.14
1.32
0.70
0.35

481
Midland I.S.D. Police Dept.
Missouri Univ. of Science & Technology Police Dept
Morrisville State College Police
BYU-Idaho Police Department
Lander University Public Safety
Grambling State University Police Dept.
Greenville Technical College Public Safety
Lehigh-Northampton Airport Auth. Police Dept
California Exposition And State Fair Police
Lancaster I.S.D. Police Dept.
University of Maryland Eastern Shore Public Safety
University of West Alabama Police
Ennis I.S.D. Police Department
Ventura College
Connally I.S.D. Police Dept.
Santa Rosa I.S.D. Police Department

Midland
Phelps
Madison
Madison
Greenwood
Lincoln
Greenville
Lehigh
Sacramento
Dallas
Somerset
Sumter
Ellis
Ventura
McLennan
Cameron

TX
MO
NY
ID
SC
LA
SC
PA
CA
TX
MD
AL
TX
CA
TX
TX

11
11
11
10
10
9
9
9
6
6
6
6
5
5
3
3

1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1

90.91
90.91
90.91
100.00
100.00
222.22
111.11
111.11
166.67
166.67
166.67
166.67
200.00
200.00
333.33
333.33

0.73
2.21
1.36
2.66
1.44
4.28
0.22
0.29
0.07
0.04
3.78
7.27
0.67
0.12
0.43
0.25

482
Appendix B-1. 200 Largest State & Local Law Enforcement Agencies in Study: Rates of Officers Arrested, 2005-2011 (Sorted by Rate per 1,000 Officers)
# of Full-Time
Per
Per 1,000
Per 100,000
Agency
County
State
Sworn Personnel
Agency
Officers
Population
New Orleans Police Department
Milwaukee Police Department
Memphis Police Department
New Mexico State Police
Pittsburgh Police Department
Shreveport Police Department
Polk County Sheriff's Office
Kern County Sheriff's Office
Indianapolis Police
Minneapolis Police Department
Springfield Police Department
Toledo Police Department
Jackson Police Department
Baltimore Police Department
Durham Police Department
Norfolk Police Department
Akron Police Department
Cleveland Police Department
Shelby County Sheriff's Office
San Antonio Police Department
Jefferson Parish Sheriff's Office
Providence Police Department
Nashville Metro Police Department
Honolulu (City & County) Police Department
Fort Worth Police Department
St. Paul Police Department
Louisville Metro Police Department
Milwaukee County Sheriff's Office
Jersey City Police
Tulsa Police Department
Montgomery County Police Department
El Paso Police Department

Orleans
Milwaukee
Shelby
Santa Fe
Allegheny
Caddo
Polk
Kern
Marion
Hennepin
Hampden
Lucas
Hinds
Baltimore City
Durham
Norfolk City
Summit
Cuyahoga
Shelby
Bexar
Jefferson
Providence
Davidson
Honolulu
Tarrant
Ramsey
Jefferson
Milwaukee
Hudson
Tulsa
Montgomery
El Paso

LA
WI
TN
NM
PA
LA
FL
CA
IN
MN
MA
OH
MS
MD
NC
VA
OH
OH
TN
TX
LA
RI
TN
HI
TX
MN
KY
WI
NJ
OK
MD
TX

1425
1987
1549
528
891
511
600
512
1582
902
464
640
480
2990
494
772
472
1616
516
2020
825
483
1315
1934
1489
598
1197
524
900
826
1206
1132

63
73
46
13
21
12
13
11
33
18
9
12
9
55
9
14
8
27
8
30
12
7
18
26
20
8
16
7
12
11
16
15

44.21
36.74
29.70
24.62
23.57
23.48
21.67
21.48
20.86
19.96
19.40
18.75
18.75
18.39
18.22
18.13
16.95
16.71
15.50
14.85
14.55
14.49
13.69
13.44
13.43
13.38
13.37
13.36
13.33
13.32
13.27
13.25

18.32
7.70
4.96
0.63
1.72
4.71
2.16
1.31
3.65
1.56
1.94
2.72
3.67
8.86
3.36
5.77
1.48
2.11
0.86
1.75
2.77
1.12
2.87
2.73
1.11
1.57
2.16
0.74
1.89
1.82
1.65
1.87

483
Atlanta Police Department
Albuquerque Police Department
Miami Police Department
Fort Lauderdale Police Department
DeKalb County Police Department
Prince George's County Police Department
Montgomery Police Department
Charlotte - Mecklenburg Police Department
St. Petersburg Police Department
Dallas Police Department
Oklahoma City Police Department
Orange County Sheriff's Office
Raleigh Police Department
Passaic County Sheriff's Office
Lee County Sheriff's Office
Savannah-Chatham Metropolitan Police Dept
Richmond County Sheriff's Office
Virginia Beach Police Department
Portland Police Bureau
Orlando Police Department
Worcester Police Department
South Carolina Highway Patrol
Detroit Police Department
Mississippi Highway Safety Patrol
Philadelphia Police Department
Broward County Sheriff's Office
Birmingham Police Department
Aurora Police Department
Collier County Sheriff's Office
Baton Rouge Police Department
Bexar County Sheriff's Office
Marion County Sheriff's Office
Richmond Police Department
Pinellas County Sheriff's Office
Cincinnati Police Department
Fort Wayne Police

Fulton
Bernalillo
Miami-Dade
Broward
DeKalb
Prince Georges
Montgomery
Mecklenburg
Pinellas
Dallas
Oklahoma
Orange
Wake
Passaic
Lee
Chatham
Richmond
VA Beach City
Multnomah
Orange
Worcester
Richland
Wayne
Hinds
Philadelphia
Broward
Jefferson
Arapahoe
Collier
E. Baton Rouge
Bexar
Marion
Richmond City
Pinellas
Hamilton
Allen

GA
NM
FL
FL
GA
MD
AL
NC
FL
TX
OK
FL
NC
NJ
FL
GA
GA
VA
OR
FL
MA
SC
MI
MS
PA
FL
AL
CO
FL
LA
TX
IN
VA
FL
OH
IN

1719
1020
1104
482
1074
1578
500
1672
510
3389
1046
1398
702
530
621
534
449
813
928
757
482
967
2250
594
6624
1624
816
627
628
630
526
740
752
863
1082
447

22
13
14
6
13
19
6
20
6
39
12
16
8
6
7
6
5
9
10
8
5
10
23
6
66
16
8
6
6
6
5
7
7
8
10
4

12.80
12.75
12.68
12.45
12.10
12.04
12.00
11.96
11.76
11.51
11.47
11.44
11.40
11.32
11.27
11.24
11.14
11.07
10.78
10.57
10.37
10.34
10.22
10.10
9.96
9.85
9.80
9.57
9.55
9.52
9.51
9.46
9.31
9.27
9.24
8.95

2.39
1.96
0.56
0.34
1.88
2.20
2.62
2.17
0.65
1.65
1.67
1.40
0.89
1.20
1.13
2.26
2.49
2.05
1.36
0.70
0.63
0.22
1.26
0.20
4.33
0.92
1.21
1.05
1.87
1.36
0.29
0.77
3.43
0.87
1.25
1.13

484
Volusia County Sheriff's Office
Florida Highway Patrol
Denver Police Department
Cobb County Police Department
Fresno Police Department
Greensboro Police Department
Manatee County Sheriff's Office
Palm Beach County Sheriff's Office
Pasco County Sheriff's Office
Hillsborough County Sheriff's Office
Miami-Dade (County) Police Department
Omaha Police Dept
Orleans Parish Sheriff's Office (Criminal Division)
Orange County Sheriff-Coroner Department
Mobile Police Department
Washington Metropolitan Police Department
Fulton County Sheriff's Office
Minnesota State Patrol
Colorado Springs Police Department
St. Louis (city) Police Dept
Jacksonville Sheriff's Office
Henrico County Division of Police
Sacramento Police Department
Seattle Police Department
Houston Police Department
King County Sheriff's Office
Denver County Sheriff's Office
Leon County Sheriff's Office
Corpus Christi Police Department
Austin Police Department
San Jose Police Department
Boston Police Department
Florida Fish & Wildlife Conservation Commission
Maryland State Police
Chicago Police Department
San Diego Police Department

Volusia
Leon
Denver
Cobb
Fresno
Guilford
Manatee
Palm Beach
Pasco
Hillsborough
Miami-Dade
Douglas
Orleans
Orange
Mobile
DC
Fulton
Ramsey
El Paso
St. Louis City
Duval
Henrico
Sacramento
King
Harris
King
Denver
Leon
Nueces
Travis
Santa Clara
Suffolk
Leon
Baltimore
Cook
San Diego

FL
FL
CO
GA
CA
NC
FL
FL
FL
FL
FL
NE
LA
CA
AL
DC
GA
MN
CO
MO
FL
VA
CA
WA
TX
WA
CO
FL
TX
TX
CA
MA
FL
MD
IL
CA

450
1606
1525
590
828
593
476
1447
485
1223
3093
747
505
1794
515
3742
516
530
668
1351
1662
554
701
1283
5053
721
739
443
448
1515
1382
2181
626
1440
13354
1951

4
14
13
5
7
5
4
12
4
10
25
6
4
14
4
29
4
4
5
10
12
4
5
9
35
5
5
3
3
10
9
14
4
9
83
12

8.89
8.72
8.52
8.47
8.45
8.43
8.40
8.29
8.25
8.18
8.08
8.03
7.92
7.80
7.77
7.75
7.75
7.55
7.49
7.40
7.22
7.22
7.13
7.01
6.93
6.93
6.77
6.77
6.70
6.60
6.51
6.42
6.39
6.25
6.22
6.15

0.81
0.07
2.17
0.73
0.75
1.02
1.24
0.91
0.86
0.81
1.00
1.16
1.16
0.47
0.97
4.82
0.43
0.08
0.80
3.13
1.39
1.30
0.35
0.47
0.86
0.26
0.83
1.09
0.88
0.98
0.51
1.94
0.02
0.16
1.60
0.39

485
Syracuse Police Department
San Bernardino County Sheriff's Office
Newark Police
Oklahoma Department of Public Safety
Paterson Police
Fairfax County Sheriff's Office
Winston-Salem Police Department
Gwinnett County Police Department
Richland County Sheriff's Office
Tulare County Sheriff's Office
Pennsylvania State Police
Tucson Police Department
Gwinnett County Sheriff's Office
Prince William County Police Department
New York City Police Department
Colorado State Patrol
San Diego County Sheriff's Office
Michigan State Police
Riverside County Sheriff's Office
Buffalo Police Department
Phoenix Police Department
Fairfax County Police Department
Arlington Police Department
Columbus Police Department
Illinois State Police
Anne Arundel County Police Department
Delaware State Police
Washington Metro Area Transit Auth. Police
Loudoun County Sheriff's Office
Dane County Sheriff's Office
Franklin County Sheriff's Office
North Carolina State Highway Patrol
Massachusetts State Police
Rochester Police Department
New Jersey State Police
Sacramento County Sheriff's Office

Onondaga
San Bernardino
Essex
Oklahoma
Passaic
Fairfax
Forsyth
Gwinnett
Richland
Tulare
Dauphin
Pima
Gwinnett
Prince William
New York
Jefferson
San Diego
Ingham
Riverside
Erie
Maricopa
Fairfax
Tarrant
Franklin
Sangamon
Anne Arundel
Kent
DC
Loudoun
Dane
Franklin
Wake
Middlesex
Monroe
Mercer
Sacramento

NY
CA
NJ
OK
NJ
VA
NC
GA
SC
CA
PA
AZ
GA
VA
NY
CO
CA
MI
CA
NY
AZ
VA
TX
OH
IL
MD
DE
DC
VA
WI
OH
NC
MA
NY
NJ
CA

489
1797
1310
825
497
499
508
682
512
513
4458
1032
531
546
36023
742
1322
1732
2147
793
3388
1419
610
1886
2105
633
658
442
448
454
455
1827
2310
703
3053
1409

3
11
8
5
3
3
3
4
3
3
26
6
3
3
196
4
7
9
11
4
17
7
3
9
10
3
3
2
2
2
2
8
10
3
13
6

6.13
6.12
6.11
6.06
6.04
6.01
5.91
5.87
5.86
5.85
5.83
5.81
5.65
5.49
5.44
5.39
5.30
5.20
5.12
5.04
5.02
4.93
4.92
4.77
4.75
4.74
4.56
4.52
4.46
4.41
4.40
4.38
4.33
4.27
4.26
4.26

0.64
0.54
1.02
0.13
0.60
0.28
0.86
0.50
0.78
0.68
0.20
0.61
0.37
0.75
2.40
0.08
0.23
0.09
0.50
0.44
0.45
0.65
0.17
0.77
0.08
0.56
0.33
0.33
0.64
0.41
0.17
0.08
0.15
0.40
0.15
0.42

486
Tennessee Department of Safety
North Las Vegas Police Department
Utah Department of Public Safety
Long Beach Police Department
Brevard County Sheriff's Office
Oakland Police Department
Little Rock Police Department
Arkansas State Police
San Francisco Police Department
Kansas City Police Department
Kentucky State Police
Connecticut State Police
Yonkers Police Department
Los Angeles Police Department
Tampa Police Department
Indiana State Police
Wichita Police Department
Iowa Department of Public Safety
New York State Metro Transportation Auth. Police
Las Vegas Metro Police Department
Alabama Department of Public Safety
Maricopa County Sheriff's Office
Ohio State Highway Patrol
St. Louis County Police Dept
Arizona Department of Public Safety
Mesa Police Department
Suffolk County Police Department
Madison Police Department
Will County Sheriff's Office
Dallas County Sheriff's Office
Santa Clara County Sheriff's Office
School District of Philadelphia Police
Nassau County Police Department
El Paso County Sheriff's Office
Knox County Sheriff's Office
Maryland Transportation Authority Police

Davidson
Clark
Salt Lake
Los Angeles
Brevard
Alameda
Pulaski
Pulaski
San Francisco
Jackson
Franklin
Middlesex
Westchester
Los Angeles
Hillsborough
Marion
Sedgwick
Polk
New York
Clark
Montgomery
Maricopa
Franklin
St. Louis
Maricopa
Maricopa
Suffolk
Dane
Will
Dallas
Santa Clara
Philadelphia
Nassau
El Paso
Knox
Baltimore

TN
NV
UT
CA
FL
CA
AR
AR
CA
MO
KY
CT
NY
CA
FL
IN
KS
IA
NY
NV
AL
AZ
OH
MO
AZ
AZ
NY
WI
IL
TX
CA
PA
NY
CO
TN
MD

942
471
475
968
497
773
520
525
1940
1421
882
1227
641
9727
980
1315
662
669
694
2942
763
766
1560
781
1244
831
2622
437
445
449
450
450
2732
454
456
456

4
2
2
4
2
3
2
2
7
5
3
4
2
30
3
4
2
2
2
8
2
2
4
2
3
2
6
1
1
1
1
1
6
1
1
1

4.25
4.25
4.21
4.13
4.02
3.88
3.85
3.81
3.61
3.52
3.40
3.26
3.12
3.08
3.06
3.04
3.02
2.99
2.88
2.72
2.62
2.61
2.56
2.56
2.41
2.41
2.29
2.29
2.25
2.23
2.22
2.22
2.20
2.20
2.19
2.19

0.06
0.10
0.07
0.04
0.37
0.20
0.52
0.07
0.87
0.74
0.07
0.11
0.21
0.31
0.24
0.06
0.40
0.07
0.02
0.41
0.04
0.05
0.03
0.20
0.05
0.05
0.40
0.20
0.15
0.04
0.06
0.07
0.45
0.16
0.23
0.02

487
Fresno County Sheriff's Office
Alameda County Sheriff's Office
Chesterfield County Police Department
Baltimore County Police Department
District of Columbia Protective Services Police
Nebraska State Patrol
Monmouth County Sheriff's Office
California Highway Patrol
Pima County Sheriff's Dept.
Washington State Patrol
Texas Department of Public Safety
Calcasieu Parish Sheriff's Office
Louisiana State Police
Virginia State Police
Los Angeles County Sheriff's Office
Contra Costa County Sheriff's Office
Cook County Sheriff's Office
Ventura County Sheriff's Office
Harris County Sheriff's Office
New York State Police
Georgia Department of Public Safety
Wayne County Sheriff's Office
Port Authority of New York & New Jersey Police
New York State Courts Officers

Fresno
Alameda
Chesterfield
Baltimore
DC
Lancaster
Monmouth
Sacramento
Pima
Thurston
Travis
Calcasieu
E. Baton Rouge
Chesterfield
Los Angeles
Contra Costa
Cook
Ventura
Harris
Albany
Fulton
Wayne
Hudson
New York

CA
CA
VA
MD
DC
NE
NJ
CA
AZ
WA
TX
LA
LA
VA
CA
CA
IL
CA
TX
NY
GA
MI
NJ
NY

461
928
475
1910
484
491
494
7202
554
1132
3529
592
1215
1873
9461
679
5655
755
2558
4847
1048
1062
1667
4500

1
2
1
4
1
1
1
13
1
2
6
1
2
3
14
1
8
1
3
5
1
1
1
1

2.17
2.16
2.11
2.09
2.07
2.04
2.02
1.81
1.81
1.77
1.70
1.69
1.65
1.60
1.48
1.47
1.41
1.32
1.17
1.03
0.95
0.94
0.60
0.22

0.11
0.13
0.32
0.50
0.17
0.05
0.16
0.03
0.10
0.03
0.02
0.52
0.04
0.04
0.14
0.10
0.15
0.12
0.07
0.03
0.01
0.05
0.01
0.01

488
Appendix B-2. Nonmetropolitan State & Local Law Enforcement Agencies in Study: Rates of Officers Arrested, 2005-2011 (Sorted by Rate per 1,000
Officers)
# of Full-Time
Per
Per 1,000
Per 100,000
Agency
County
State Sworn Personnel
Agency
Officers
Population
Mounds Police Dept
Atwater Police Department
Berlin Borough Police Department
Berlin Heights Police Department
Bowman Police Department
Burr Oak Police Department
Cooter Police Department
Elgin Police Department
Hamburg Police Department
Lamoure Police Department
Lockhart Police Department
Marion Township Police Department
Nicholas County Sheriff's Office
Oakwood Police Department
Perryville Police Department
Petroleum County Sheriff's Office
Pineview Police Department
Tipton Police Department
Turkey Creek Police Department
Wakeman Police Department
Wilson Police Department
Zolfo Springs Police Department
Carter County Sheriff's Office
Chilhowie Police Department
Shenandoah Borough Police Department
Athena Police Department
Birchwood Police Department
Boswell Police Department
Cherokee Police Department
Franklin Police Dept
Hegins Township Police Department

Pulaski
Kandiyohi
Somerset
Erie
Orangeburg
St. Joseph
Pemiscot
Grant
Fremont
LaMoure
Covington
Waushara
Nicholas
Paulding
Boyle
Petroleum
Wilcox
Tillman
Evangeline
Huron
Ellsworth
Hardee
Carter
Smyth
Schuylkill
Umatilla
Washburn
Choctaw
Alfalfa
Franklin
Schuylkill

IL
MN
PA
OH
SC
MI
MO
ND
IA
ND
AL
WI
KY
OH
KY
MT
GA
OK
LA
OH
KS
FL
MO
VA
PA
OR
WI
OK
OK
NE
PA

1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
6
7
2
2
2
2
2
2

2
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
4
4
1
1
1
1
1
1

2000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
1000.00
666.67
666.67
571.43
500.00
500.00
500.00
500.00
500.00
500.00

32.46
2.37
1.29
1.30
3.24
1.63
5.47
41.77
13.44
24.16
2.65
4.08
14.02
5.10
3.52
202.43
10.80
12.51
2.94
1.68
15.39
3.61
31.92
12.42
2.70
1.32
6.28
6.58
17.72
31.01
0.67

489
Homer City Borough Police Department
Hunter Police Department
Inman Police Department
Meyersdale Borough Police Department
Pineland Police Department
Pink Hill Police Department
Ridgeville Police
Ridgeville Police Department
Seadrift Police Department
Springfield Police Department
White Cloud Police Department
Windber Borough Police Department
Oregon County Sheriff's Office
Roseboro Police Department
Talbot County Sheriff's Office
Crystal City Police Department
Belle Police Department
Bismarck Police Department
Butler County Sheriff's Office
Caddo Police Department
Earlville Police Dept
Fair Bluff Police Department
Freedom Police Department
Gallatin County Sheriff's Office
Griggs County Sheriff's Office
Guadalupe County Sheriff's Office
Haskell Police Department
Hennessey Police Department
Kahoka Police Department
Marvell Police Department
Meigs Police Department
Newbury Police Department
Ravenna Police Dept
Santee Police Department
Scotts Hill Police Department
Stover Police Department

Indiana
Greene
McPherson
Somerset
Sabine
Lenoir
Randolph
Dorchester
Calhoun
Orangeburg
Newaygo
Somerset
Oregon
Sampson
Talbot
Zavala
Maries
St. Francois
Butler
Bryan
La Salle
Columbus
Carroll
Gallatin
Griggs
Guadalupe
Haskell
Kingfisher
Clark
Phillips
Thomas
Merrimack
Buffalo
Orangeburg
Henderson
Morgan

PA
NY
KS
PA
TX
NC
IN
SC
TX
SC
MI
PA
MO
NC
GA
TX
MO
MO
AL
OK
IL
NC
NH
IL
ND
NM
TX
OK
MO
AR
GA
NH
NE
SC
TN
MO

2
2
2
2
2
2
2
2
2
2
2
2
5
5
8
11
3
3
9
3
3
3
3
3
3
3
3
3
3
3
3
3
3
6
3
3

1
1
1
1
1
1
1
1
1
1
1
1
2
2
3
4
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1

500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
500.00
400.00
400.00
375.00
363.64
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33
333.33

1.13
2.03
3.43
1.29
9.23
1.68
3.82
2.57
4.68
1.08
2.06
1.29
18.38
3.15
43.70
34.26
10.90
1.53
14.32
2.36
0.88
1.72
2.09
17.89
41.32
21.34
16.95
6.65
6.58
4.60
2.24
0.68
2.17
2.16
3.60
4.86

490
Waukomis Police Department
La Salle County Sheriff's Office
Shelby Police Department
Shinnston Police Department
Seneca County Sheriff's Office
Blue Lake Police Department
Butler Township Police Department
Columbus Police Department
Delhi Village Police Department
Edenton Police Department
Elmore Police Department
Fairland Police Department
Foster Township Police Department
Gold Beach Police Department
Hemingway Police Department
Keokuk County Sheriff's Office
Mahanoy City Borough Police Department
Malakoff Police Department
Marble Head Police Department
Marshallville Police Dept.
McArthur Police Department
Monroeville Police Department
New Lisbon Police Department
Onley Police Department
Rochelle Police Department
Roodhouse Police Dept
Tutwiler Police Department
Winsted Police Department
Grambling State University Police Dept.
Lake County Sheriff's Office
Tabor City Police Department
Cochran Police Department
Jackson County Sheriff's Office
Allendale Police Department
Carlisle Police Department
Chaffee Police Department

Garfield
La Salle
Bolivar
Harrison
Seneca
Humboldt
Schuylkill
Luna
Delaware
Chowan
Ottawa
Ottawa
McKean
Curry
Williamsburg
Keokuk
Schuylkill
Henderson
Ottawa
Macon
Vinton
Huron
Juneau
Accomack
Wilcox
Greene
Tallahatchie
Litchfield
Lincoln
Lake
Columbus
Bleckley
Jackson
Allendale
Nicholas
Scott

OK
TX
MS
WV
NY
CA
PA
NM
NY
NC
OH
OK
PA
OR
SC
IA
PA
TX
OH
GA
OH
OH
WI
VA
GA
IL
MS
CT
LA
CO
NC
GA
TN
SC
KY
MO

3
10
7
7
23
4
4
4
4
12
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
9
9
9
14
14
10
5
5

1
3
2
2
6
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
3
3
2
1
1

333.33
300.00
285.71
285.71
260.87
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
250.00
222.22
222.22
222.22
214.29
214.29
200.00
200.00
200.00

1.65
43.57
5.86
2.89
17.02
0.74
0.67
3.98
2.08
20.28
2.41
3.14
2.30
4.47
2.91
9.51
0.67
1.27
2.41
6.78
7.44
1.68
3.75
3.02
10.80
7.20
6.50
0.53
4.28
27.36
3.44
22.97
25.78
19.20
14.02
2.55

491
Commerce Police Department
Crescent City Police Department
Creston Police Department
Delhi Police Department
Dixon Police Department
Durand Police Department
Estill County Sheriff's Office
Fryeburg Police Department
Grand Rapids Police Department
Grant County Sheriff's Office
Haskell Police Department
Holt County Sheriff's Office
Kenton Police Department
Lake County Sheriff's Office
Level Plains Police Department
Lewis County Sheriff's Office
Middletown Police
Montpelier Police Department
Mora County Sheriff's Office
North Kingsville Police Department
Oglethorpe Police Department
Olney Police Department
Pearson Police Department
Pocahontas County Sheriff's Office
Ranger Police Department
Richland Police Dept
Robbins Police Department
Rosedale Police Department
Santa Clara Police Department
Sugarcreek Borough Police Department
Sugarcreek Police Department
Terrell County Sheriff's Office
Vergennes Police Department
West Yellowstone Police Department
Woodstock Police Department
Hockley County Sheriff's Office

Ottawa
Putnam
Union
Richland
Pulaski
Shiawassee
Estill
Oxford
Wood
Grant
Muskogee
Holt
Obion
Lake
Dale
Lewis
Henry
Bear Lake
Mora
Ashtabula
Macon
Young
Atkinson
Pocahontas
Eastland
Pulaski
Moore
Bolivar
Grant
Venango
Tuscarawas
Terrell
Addison
Gallatin
Grafton
Hockley

OK
FL
IA
LA
MO
MI
KY
ME
WI
OK
OK
NE
TN
SD
AL
MO
IN
ID
NM
OH
GA
TX
GA
WV
TX
MO
NC
MS
NM
PA
OH
TX
VT
MT
NH
TX

5
5
10
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
11

1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2

200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
200.00
181.82

3.14
1.34
15.96
4.83
1.91
1.42
6.82
1.73
1.34
22.09
1.41
9.58
3.14
8.93
1.99
9.79
2.02
16.71
20.49
0.99
6.78
5.39
11.94
11.47
5.38
1.91
1.13
2.93
3.39
1.82
1.08
101.63
2.72
1.12
1.12
8.72

492
Montezuma Police Department
Pulaski County Sheriff's Office
Stephens County Sheriff's Office
Bingen-White Salmon Police Department
Bridgeport Police Department
Columbus Police Department
East Brewton Police Department
Fairfax Police Department
Florala Police Department
Geneva Township Police Department
Kaw Nation Tribal Police
Norton Police Department
Sleepy Eye Police Department
St. Paul Police Department
University of West Alabama Police
Woodruff County Sheriff's Office
Eunice Police Department
Black River Falls Police Department
Byron Police Dept
Clay County Sheriff's Office
Coeburn Police Department
Flemingsburg Police Department
Fremont County Sheriff's Office
Haynesville Police Dept
Hermann Police Department
Holly Hill Police Department
Mason County Sheriff's Office
Murphy Police Department
New Castle Police Department
Port Barre Police Dept
Providence Police Department
Union Police Department
Winnsboro Police Department
Yemassee Police Department
Fayette County Sheriff's Office
Helena\/West Helena Police Department

Macon
Pulaski
Stephens
Klickitat
Jackson
Polk
Escambia
Allendale
Covington
Walworth
Kay
Norton
Redwood
Wise
Sumter
Woodruff
St. Landry
Jackson
Ogle
Clay
Wise
Fleming
Fremont
Claiborne
Gasconade
Orangeburg
Mason
Cherokee
Garfield
St. Landry
Webster
Newton
Franklin
Hampton
Fayette
Phillips

GA
IL
OK
WA
AL
NC
AL
SC
AL
WI
OK
KS
MN
VA
AL
AR
LA
WI
IL
AR
VA
KY
IA
LA
MO
SC
WV
NC
CO
LA
KY
MS
LA
SC
OH
AR

11
11
11
6
6
6
6
6
6
6
6
6
6
6
6
6
32
7
7
7
7
7
7
7
7
7
21
7
7
7
7
7
7
7
22
30

2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
5
1
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
3
4

181.82
181.82
181.82
166.67
166.67
166.67
166.67
166.67
166.67
166.67
166.67
166.67
166.67
166.67
166.67
166.67
156.25
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
142.86
136.36
133.33

13.57
32.46
4.44
4.92
1.88
4.88
2.61
9.60
2.65
0.98
2.15
17.63
3.86
2.41
7.27
13.77
6.00
4.89
1.87
6.22
2.41
6.97
13.44
5.82
6.57
1.08
10.98
3.64
1.77
1.20
7.34
4.60
4.82
4.74
10.33
18.38

493
Nebraska City Police Dept
Shelby County Sheriff's Office
Big Horn County Sheriff's Office
Blackford County Sheriff's Office
Blountstown Police Dept.
Canton Village Police Department
Chandler Police Department
Eunice Police Department
Henry County Sheriff's Office
Kingfisher County Sheriff's Office
Madison County Sheriff's Office
Oglesby Police Dept
Waynesburg Borough Police Department
Whiteville Police Department
McIntosh County Sheriff's Office
Princeton Police Department
Andrews Police Department
Arcade Police Department
Beardstown Police Dept
Belpre Police Department
Dillon Police Department
Forest City Police Department
Forks Police Department
Glendive Police Department
Hanceville Police Department
Lawrence Township Police Department
Minocqua Police Department
Osceola Police Department
Ripley Police Department
Allenstown Police Department
Alma Police Department
BYU-Idaho Police Department
Clewiston Police Department
Columbus Police Department
Dewitt Police Department
Ferriday Police Department

Otoe
Shelby
Big Horn
Blackford
Calhoun
St. Lawrence
Henderson
Lea
Henry
Kingfisher
Madison
La Salle
Greene
Hardeman
McIntosh
Mercer
Georgetown
Jackson
Cass
Washington
Summit
Winnebago
Clallam
Dawson
Cullman
Clearfield
Oneida
Clarke
Jackson
Merrimack
Bacon
Madison
Hendry
Colorado
Clinton
Concordia

NE
TX
MT
IN
FL
NY
TX
NM
VA
OK
MO
IL
PA
TN
OK
WV
SC
GA
IL
OH
CO
IA
WA
MT
AL
PA
WI
IA
WV
NH
GA
ID
FL
TX
IA
LA

15
15
8
8
8
8
8
8
112
8
8
8
8
8
17
17
9
9
9
9
9
9
9
9
9
9
9
9
9
10
10
10
20
10
10
10

2
2
1
1
1
1
1
1
14
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1

133.33
133.33
125.00
125.00
125.00
125.00
125.00
125.00
125.00
125.00
125.00
125.00
125.00
125.00
117.65
117.65
111.11
111.11
111.11
111.11
111.11
111.11
111.11
111.11
111.11
111.11
111.11
111.11
111.11
100.00
100.00
100.00
100.00
100.00
100.00
100.00

12.71
7.86
7.77
7.83
6.84
0.89
1.27
1.54
25.85
6.65
8.18
0.88
2.58
3.67
9.88
3.21
1.66
1.65
7.33
1.62
3.57
9.20
1.40
11.15
1.24
1.22
2.78
10.77
3.42
0.68
9.01
2.66
5.11
4.79
2.04
4.80

494
Ferry County Sheriff's Office
Helen Police Department
Ishpeming Police Department
Jaffrey Police Department
Kermit Police Department
Lancaster Police Department
Lander University Public Safety
Marlow Police Department
Millersburg Police Department
Montague County Sheriff's Office
New Martinsville Police Department
Powell County Sheriff's Office
Socorro County Sheriff's Office
St. George Police Department
Williams Police Department
Winkler County Sheriff's Office
Caruthersville Police Department
Algood Police Department
Columbiana County Sheriff's Office
Eastman Police Department
Floyd County Sheriff's Office
Fort Gibson Police Department
Greene County Sheriff's Office
Hannahville Tribal Police Department
Medina Police Department
Missouri Univ. of Science & Tech Police Dept
Rainsville Police Department
Roosevelt County Sheriff's Office
Simpson County Sheriff's Office
Stark County Sheriff's Office
Aspen Police Department
Tuskegee Police Department
Polk County Police Department
Braselton Police Department
Bunkie Police Department
Clayton County Sheriff's Office

Ferry
White
Marquette
Cheshire
Winkler
Garrard
Greenwood
Stephens
Holmes
Montague
Wetzel
Powell
Socorro
Dorchester
Colusa
Winkler
Pemiscot
Putnam
Columbiana
Dodge
Floyd
Muskogee
Greene
Menominee
Gibson
Phelps
DeKalb
Roosevelt
Simpson
Stark
Pitkin
Macon
Polk
Jackson
Avoyelles
Clayton

WA
GA
MI
NH
TX
KY
SC
OK
OH
TX
WV
MT
NM
SC
CA
TX
MO
TN
OH
GA
IA
OK
AL
MI
TN
MO
AL
MT
KY
ND
CO
AL
GA
GA
LA
IA

10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
21
11
22
11
11
11
11
11
11
11
11
11
11
11
23
23
35
12
12
12

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
1
1
1
1
1
1
1
1
1
1
2
2
3
1
1
1

100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
95.24
90.91
90.91
90.91
90.91
90.91
90.91
90.91
90.91
90.91
90.91
90.91
90.91
90.91
86.96
86.96
85.71
83.33
83.33
83.33

13.24
3.68
1.49
1.30
14.06
5.91
1.44
2.22
2.36
5.07
6.03
14.23
5.60
2.57
4.67
14.06
10.93
1.38
1.85
4.59
6.13
1.41
11.06
4.16
2.01
2.21
1.41
9.59
5.77
4.13
11.66
9.32
7.23
1.65
2.38
5.52

495
Custer County Sheriff's Office
Fayette Police Department
Franklin County Sheriff's Office
Gage County Sheriff's Office
Lakeport Police Department
Madill Police Department
Monticello Police
Natchez Police Dept.
Perry County Sheriff's Office
Quincy Police Department
Wallace Police Department
Whitley County Sheriff's Office
Williamsburg Police Department
Wolfeboro Police Department
Marlboro County Sheriff's Office
Russellville Police Department
Benzie County Sheriff's Office
Denton Police Department
Emery County Sheriff's Office
Fairmont Police Department
Frisco Police Department
Hempstead County Sheriff's Office
Humboldt Police Department
Pauls Valley Police Department
Randolph County Sheriff's Office
Tucumcari Police Department
Upper Sandusky Police Department
Winnfield Police Dept
Bartlesville Police Department
Polk County Sheriff's Office
Appling County Sheriff's Office
Caribou Police Department
Clyde Police Department
Grants Police Department
Morrow County Sheriff's Office
Newport Police Department

Custer
Fayette
Franklin
Gage
Lake
Marshall
White
Adams
Perry
Grant
Duplin
Whitley
Whitley
Carroll
Marlboro
Franklin
Benzie
Caroline
Emery
Robeson
Summit
Hempstead
Gibson
Garvin
Randolph
Quay
Wyandot
Winn
Washington
Polk
Appling
Aroostook
Sandusky
Cibola
Morrow
Sullivan

OK
AL
IN
NE
CA
OK
IN
MS
IL
WA
NC
KY
KY
NH
SC
AL
MI
MD
UT
NC
CO
AR
TN
OK
IL
NM
OH
LA
OK
GA
GA
ME
OH
NM
OR
NH

12
12
12
12
12
12
12
48
12
12
12
12
12
12
25
25
13
13
26
13
13
13
26
13
13
13
13
13
54
27
14
14
14
14
14
14

1
1
1
1
1
1
1
4
1
1
1
1
1
1
2
2
1
1
2
1
1
1
2
1
1
1
1
1
4
2
1
1
1
1
1
1

83.33
83.33
83.33
83.33
83.33
83.33
83.33
83.33
83.33
83.33
83.33
83.33
83.33
83.33
80.00
80.00
76.92
76.92
76.92
76.92
76.92
76.92
76.92
76.92
76.92
76.92
76.92
76.92
74.07
74.07
71.43
71.43
71.43
71.43
71.43
71.43

3.64
5.80
4.33
4.48
1.55
6.31
4.06
12.39
4.47
1.12
1.71
2.81
2.81
2.09
6.91
6.31
5.71
3.02
18.22
0.75
3.57
4.42
4.03
3.63
2.99
11.06
4.42
6.53
7.85
4.82
5.48
1.39
1.64
3.67
8.95
2.29

496
Ocean Shores Police Department
Pelham Police Department
Crossett Police Department
Del Norte County Sheriff's Office
Ephrata Police Department
Greene County Sheriff's Office
Kings Mountain Police Department
McDowell County Sheriff's Office
Mineral Wells Police Department
Moab Police Department
Nashville Police Department
Royston Police Department
Springfield Police Department
St. Marys Police Department
Thief River Falls Police Department
Vinita Police Department
Bishopville Police Department
Eatonton Police Department
Harrodsburg Police Department
Lamesa Police Department
Miller County Sheriff's Office
Phillips County Sheriff's Office
Red Springs Police Department
Cross County Sheriff's Office
Elkin Police Department
Fort Madison Police Department
Kitty Hawk Police Department
Middlesex County Sheriff's Office
Clovis Police Department
Zanesville Police Department
Barre Police Department
Black Mountain Police Department
Bolivar County Sheriff's Office
Calhoun County Sheriff's Office
Duval County Sheriff's Office
Greensburg Police

Grays Harbor
Mitchell
Ashley
Del Norte
Grant
Greene
Cleveland
McDowell
Palo Pinto
Grand
Berrien
Franklin
Windsor
Auglaize
Pennington
Craig
Lee
Putnam
Mercer
Dawson
Miller
Phillips
Robeson
Cross
Surry
Lee
Dare
Middlesex
Curry
Muskingum
Washington
Buncombe
Bolivar
Calhoun
Duval
Decatur

WA
GA
AR
CA
WA
AR
NC
WV
TX
UT
GA
GA
VT
OH
MN
OK
SC
GA
KY
TX
MO
AR
NC
AR
NC
IA
NC
VA
NM
OH
VT
NC
MS
FL
TX
IN

14
14
15
30
15
15
30
15
30
15
15
15
15
15
15
15
16
16
16
16
16
16
16
17
17
17
17
17
53
53
18
18
18
18
18
18

1
1
1
2
1
1
2
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
1
1
1
1
1
1

71.43
71.43
66.67
66.67
66.67
66.67
66.67
66.67
66.67
66.67
66.67
66.67
66.67
66.67
66.67
66.67
62.50
62.50
62.50
62.50
62.50
62.50
62.50
58.82
58.82
58.82
58.82
58.82
56.60
56.60
55.56
55.56
55.56
55.56
55.56
55.56

1.37
4.26
4.58
6.99
1.12
2.38
2.04
4.52
7.11
10.84
5.19
4.53
1.76
2.18
7.18
6.65
5.20
4.71
4.69
7.23
4.04
4.60
0.75
5.60
1.36
2.79
2.95
9.12
6.20
3.49
1.68
2.22
2.93
6.84
8.49
3.89

497
Hastings Police Dept
Kendallville Police
Leland Police Department
Marion Police Department
Osceola County Sheriff's Office
Rockmart Police Department
Seaside Police Department
Selmer Police Department
Spencer Police Department
Sweetwater Police Department
Towns County Sheriff's Office
Winnemucca Police Department
Gaffney Police Department
Brownwood Police Department
Conneaut Police Department
Corbin Police Department
Daviess County Sheriff's Office
Delavan Police Department
Demopolis Police Department
Norwich Police Department
Sturgis Police Department
Bainbridge Police Department
Marksville Police Department
Oakdale Police Department
Platteville Police Department
Vidalia Police Department
Wilkesboro Police Department
Bluefield Police Department
Bolivar Police Department
Central Michigan University Police Dept
Clearlake Police Department
San Jacinto County Sheriff's Office
Craig Police Department
Hardeman County Sheriff's Office
Haywood County Sheriff's Office
Hornell Police Department

Adams
Noble
Washington
Smyth
Osceola
Polk
Clatsop
McNairy
Clay
Monroe
Towns
Humboldt
Cherokee
Brown
Ashtabula
Whitley
Daviess
Walworth
Marengo
Chenango
St. Joseph
Decatur
Avoyelles
Allen
Grant
Concordia
Wilkes
Mercer
Hardeman
Isabella
Lake
San Jacinto
Moffat
Hardeman
Haywood
Steuben

NE
IN
MS
VA
MI
GA
OR
TN
IA
TN
GA
NV
SC
TX
OH
KY
IN
WI
AL
NY
MI
GA
LA
LA
WI
LA
NC
WV
TN
MI
CA
TX
CO
TN
TN
NY

36
18
18
18
18
18
18
18
18
18
18
18
37
38
19
19
19
19
19
19
19
40
20
20
20
20
20
21
21
21
21
21
22
22
22
22

2
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1

55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
55.56
54.05
52.63
52.63
52.63
52.63
52.63
52.63
52.63
52.63
50.00
50.00
50.00
50.00
50.00
50.00
47.62
47.62
47.62
47.62
47.62
45.45
45.45
45.45
45.45

6.38
2.10
1.96
3.10
4.25
2.41
2.70
3.84
6.00
2.25
9.55
6.05
3.61
5.25
0.99
2.81
3.16
0.98
4.76
1.98
1.63
7.18
2.38
3.88
1.95
4.80
1.44
1.61
3.67
1.42
1.55
3.79
7.25
3.67
5.32
1.01

498
Indiana Borough Police Department
Ketchikan Police Dept.
Ontario Police Department
Rio Arriba County Sheriff's Office
Routt County Sheriff's Office
Vernal Police Department
Washington Court House Police Dept.
Butte - Silver Bow County Sheriff's Office
Muskogee Police Department
St. Landry Parish Sheriff's Office
Beeville Police Department
Canton Police Dept
Danville (City) Sheriff's Office
Emmet County Sheriff's Office
Graham County Sheriff's Office
Monroe County Sheriff's Office
Winn Parish Sheriff's Office
Thomas County Sheriff's Office
Cherokee County Sheriff's Office
Commerce Police Department
Deridder Police Department
Huron Police Department
Jennings Police Department
Marion Police Department
Orangeburg Public Safety
Appalachian State University Police Dept
Assumption Parish Sheriff's Office
Dillon Police Department
Lawrence County Sheriff's Office
Newaygo County Sheriff's Office
Ogdensburg Police Department
Sault Ste. Marie Police Department
Selma Police Department
Tuscola County Sheriff's Office
Hampton County Sheriff's Office
Ruidoso Police Department

Indiana
Ketchikan Gateway
Malheur
Rio Arriba
Routt
Uintah
Fayette
Silver Bow
Muskogee
St. Landry
Bee
Fulton
Danville City
Emmet
Graham
Monroe
Winn
Thomas
Cherokee
Jackson
Beauregard
Beadle
Jefferson Davis
Marion
Orangeburg
Watauga
Assumption
Dillon
Lawrence
Newaygo
St. Lawrence
Chippewa
Dallas
Tuscola
Hampton
Lincoln

PA
AK
OR
NM
CO
UT
OH
MT
OK
LA
TX
IL
VA
MI
AZ
WI
LA
GA
SC
GA
LA
SD
LA
SC
SC
NC
LA
SC
IN
MI
NY
MI
AL
MI
SC
NM

22
22
22
22
22
22
22
45
90
90
23
23
69
23
23
23
23
47
48
24
24
24
24
24
72
25
50
25
25
25
25
25
50
25
26
26

1
1
1
1
1
1
1
2
4
4
1
1
3
1
1
1
1
2
2
1
1
1
1
1
3
1
2
1
1
1
1
1
2
1
1
1

45.45
45.45
45.45
45.45
45.45
45.45
45.45
44.44
44.44
44.44
43.48
43.48
43.48
43.48
43.48
43.48
43.48
42.55
41.67
41.67
41.67
41.67
41.67
41.67
41.67
40.00
40.00
40.00
40.00
40.00
40.00
40.00
40.00
40.00
38.46
38.46

1.13
7.42
3.19
2.48
4.25
3.07
3.44
5.85
5.63
4.80
3.14
2.70
6.97
3.06
2.69
2.24
6.53
4.47
3.61
1.65
2.80
5.75
3.17
3.02
3.24
1.96
8.54
3.12
2.17
2.06
0.89
2.60
4.56
1.79
4.74
4.88

499
Steuben County Sheriff's Office
Accomack County Sheriff's Office
Farmville Police Department
Jim Wells County Sheriff's Office
Las Vegas Police Department
Marble Falls Police Department
Marion County Sheriff's Office
Philadelphia Police Department
Rosebud Sioux Tribal Police
Sandusky Police Department
Hooksett Police Department
Lee County Sheriff's Office
Leesville Police Department
Vail Police Department
White County Sheriff's Office
Albert Lea Police Department
Halifax County Sheriff's Office
Lenoir County Sheriff's Office
Mount Pleasant Police Department
Putnam County Sheriff's Office
Ravalli County Sheriff's Office
Andalusia Police Dept.
Berea Police Department
Great Bend Police Department
Greenbrier County Sheriff's Office
Lebanon Police Department
Luna County Sheriff's Office
Miami Police Department
Nogales Police Department
Okanogan County Sheriff's Office
Otero County Sheriff's Office
Scottsbluff Police Dept
Shelby County Sheriff's Office
Warren County Sheriff's Office
Lake County Sheriff's Office
Austin Police Department

Steuben
Accomack
Prince Edward
Jim Wells
San Miguel
Burnet
Marion
Neshoba
Todd
Erie
Merrimack
Lee
Vernon
Eagle
White
Freeborn
Halifax
Lenoir
Titus
Putnam
Ravalli
Covington
Madison
Barton
Greenbrier
Laclede
Luna
Ottawa
Santa Cruz
Okanogan
Otero
Scotts Bluff
Shelby
Warren
Lake
Mower

NY
VA
VA
TX
NM
TX
WV
MS
SD
OH
NH
SC
LA
CO
TN
MN
VA
NC
TX
TN
MT
AL
KY
KS
WV
MO
NM
OK
AZ
WA
NM
NE
OH
NC
CA
MN

26
27
27
27
27
27
27
27
27
54
28
28
28
28
28
29
29
58
29
58
29
30
30
30
30
30
30
30
60
30
30
30
30
30
61
31

1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
2
1
2
1
1
1
1
1
1
1
1
2
1
1
1
1
1
2
1

38.46
37.04
37.04
37.04
37.04
37.04
37.04
37.04
37.04
37.04
35.71
35.71
35.71
35.71
35.71
34.48
34.48
34.48
34.48
34.48
34.48
33.33
33.33
33.33
33.33
33.33
33.33
33.33
33.33
33.33
33.33
33.33
33.33
33.33
32.79
32.26

1.01
3.02
4.28
2.45
3.40
2.34
1.77
3.37
10.40
2.59
0.68
5.20
1.91
1.92
3.87
3.20
2.76
3.36
3.09
2.77
2.49
2.65
1.21
3.61
2.82
2.81
3.98
3.14
4.22
2.43
1.57
2.70
2.02
4.77
3.09
2.55

500
Jamestown Police Department
Murray Police Department
Starr County Sheriff's Office
Stephens County Sheriff's Office
Baxter County Sheriff's Office
Grand Traverse County Sheriff's Office
Manitowoc Police Department
Moberly Police Department
Olean Police Department
Tahlequah Police Department
Traverse City Police Department
Vernon Parish Sheriff's Office
Holmes County Sheriff's Office
Meridian Police Department
Tuskegee University Police Dept
DeKalb County Sheriff's Office
Fort Payne Police Department
Martin County Sheriff's Office
Plainview Police Department
Seneca Police Department
Clarksdale Police Department
Cookeville Police Department
Lebanon Police Department
McKinley County Sheriff's Office
New Castle (city) Police Department
New Castle Police
Bennettsville Police Department
Durant Police Department
Kalispell Police Department
La Salle County Sheriff's Office
Nye County Sheriff's Office
Union City Police Department
Washington Police Department
Alice Police Department
Bogalusa Police Department
Dunn Police Department

Chautauqua
Calloway
Starr
Stephens
Baxter
Grand Traverse
Manitowoc
Randolph
Cattaraugus
Cherokee
Grand Traverse
Vernon
Holmes
Lauderdale
Macon
DeKalb
DeKalb
Martin
Hale
Oconee
Coahoma
Putnam
Grafton
McKinley
Lawrence
Henry
Marlboro
Bryan
Flathead
La Salle
Nye
Obion
Beaufort
Jim Wells
Washington
Harnett

NY
KY
TX
GA
AR
MI
WI
MO
NY
OK
MI
LA
OH
MS
AL
AL
AL
NC
TX
SC
MS
TN
NH
NM
PA
IN
SC
OK
MT
IL
NV
TN
NC
TX
LA
NC

62
31
31
31
32
64
64
32
32
32
32
64
33
99
33
34
34
34
34
34
35
70
35
35
35
35
36
36
36
36
108
36
36
37
37
37

2
1
1
1
1
2
2
1
1
1
1
2
1
3
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
3
1
1
1
1
1

32.26
32.26
32.26
32.26
31.25
31.25
31.25
31.25
31.25
31.25
31.25
31.25
30.30
30.30
30.30
29.41
29.41
29.41
29.41
29.41
28.57
28.57
28.57
28.57
28.57
28.57
27.78
27.78
27.78
27.78
27.78
27.78
27.78
27.03
27.03
27.03

1.48
2.69
1.64
3.82
2.41
2.30
2.46
3.93
1.25
2.13
1.15
3.82
2.36
3.74
4.66
1.41
1.41
4.08
2.76
1.35
3.82
2.77
1.12
1.40
1.10
2.02
3.46
2.36
1.10
0.88
6.83
3.14
2.09
2.45
2.12
0.87

501
Fort Dodge Police Department
Muskogee County Sheriff's Office
Palatka Police Department
Boone Police Department
Mount Airy Police Department
Corinth Police Department
Klamath Falls Police Department
Muscatine Police Department
Seymour Police
Big Spring Police Department
McDowell County Sheriff's Office
Roswell Police Department
White County Sheriff's Office
Chesterfield County Sheriff's Office
Rutland Police Department.
Searcy Police Department
Kauai (County) Police Department
Ada Police Department
Saline County Sheriff's Office
Robeson County Sheriff's Office
Kingsland Police Dept
Poplar Bluff Police Department
Calhoun Police Department
Cortland Police Department
Juneau Police Dept.
Lenawee County Sheriff's Office
Saline County Sheriff's Office
Scottsboro Police Department
Sedalia Police Dept
Sullivan County Sheriff's Office
Zapata County Sheriff's Office
Gatlinburg Police Department
Gillette Police Department
Grant County Sheriff's Office
Harrison County Sheriff's Office
Juneau County Sheriff's Office

Webster
Muskogee
Putnam
Watauga
Surry
Alcorn
Klamath
Muscatine
Jackson
Howard
McDowell
Chaves
White
Chesterfield
Rutland
White
Kauai
Pontotoc
Saline
Robeson
Camden
Butler
Gordon
Cortland
Juneau
Lenawee
Saline
Jackson
Pettis
Sullivan
Zapata
Sevier
Campbell
Grant
Harrison
Juneau

IA
OK
FL
NC
NC
MS
OR
IA
IN
TX
NC
NM
GA
SC
VT
AR
HI
OK
IL
NC
GA
MO
GA
NY
AK
MI
KS
AL
MO
NY
TX
TN
WY
IN
TX
WI

37
37
37
38
38
39
39
39
39
40
40
80
40
41
41
41
125
42
42
128
43
43
44
44
44
44
44
44
44
44
44
45
45
45
45
45

1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
3
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

27.03
27.03
27.03
26.32
26.32
25.64
25.64
25.64
25.64
25.00
25.00
25.00
25.00
24.39
24.39
24.39
24.00
23.81
23.81
23.44
23.26
23.26
22.73
22.73
22.73
22.73
22.73
22.73
22.73
22.73
22.73
22.22
22.22
22.22
22.22
22.22

2.63
1.41
1.34
1.96
1.36
2.70
1.51
2.34
2.36
2.86
2.22
3.05
3.68
2.14
1.62
1.30
4.47
2.67
4.01
2.24
1.98
2.34
1.81
2.03
3.20
1.00
1.80
1.88
2.37
1.29
7.13
1.11
2.17
1.43
1.52
3.75

502
Kingsville Police Department
Natchitoches Parish Sheriff's Office
Eureka Police Department
Lincoln Parish Sheriff's Office
Mason City Police Department
New Milford Police Department
Chillicothe Police Department
Lee County Sheriff's Office
Plattsburgh Police Department
Greenville Police Department
Dodge City Police Department
Page County Sheriff's Office
Stanly County Sheriff's Office
Durango Police Department
Kerrville Police Department
Shawnee Police Department
Galesburg Police Dept
Paris Police Department
Sevierville Police Department
Elko County Sheriff's Office
Wilson Police Department
Campbell County Sheriff's Office
Opelousas Police Department
Allegan County Sheriff's Office
Virginia Marine Resources Commission
Jackson County Sheriff's Office
Columbus Police Department
Gallup Police Department
Monroe County Sheriff's Office
Twin Falls Police Department
Del Rio Police Department
Frankfort Police Department
Lincoln County Sheriff's Office
Richmond Police Department
Hendry County Sheriff's Office
Auburn Police Department

Kleberg
Natchitoches
Humboldt
Lincoln
Cerro Gordo
Litchfield
Ross
Lee
Clinton
Washington
Ford
Page
Stanly
La Plata
Kerr
Pottawatomie
Knox
Lamar
Sevier
Elko
Wilson
Campbell
St. Landry
Allegan
Newport News City
Jackson
Lowndes
McKinley
Monroe
Twin Falls
Val Verde
Franklin
Lincoln
Madison
Hendry
Cayuga

TX
LA
CA
LA
IA
CT
OH
NC
NY
MS
KS
VA
NC
CO
TX
OK
IL
TX
TN
NV
NC
WY
LA
MI
VA
FL
MS
NM
FL
ID
TX
KY
OR
KY
FL
NY

45
45
46
46
46
46
47
47
47
96
49
49
49
50
51
52
53
54
55
57
114
58
58
59
60
61
62
62
189
64
65
65
65
65
67
70

1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1

22.22
22.22
21.74
21.74
21.74
21.74
21.28
21.28
21.28
20.83
20.41
20.41
20.41
20.00
19.61
19.23
18.87
18.52
18.18
17.54
17.54
17.24
17.24
16.95
16.67
16.39
16.13
16.13
15.87
15.63
15.38
15.38
15.38
15.38
14.93
14.29

3.12
2.53
0.74
2.14
2.26
0.53
1.28
1.73
1.22
3.91
2.95
4.16
1.65
1.95
2.02
1.44
1.89
2.01
1.11
2.05
2.46
2.17
1.20
0.90
3.02
2.01
1.67
1.40
4.10
1.29
2.05
2.03
2.17
1.21
2.55
1.25

503
Shelby Police Department
Alamogordo Department of Public Safety
Lumberton Police Department
Stillwater Police Department
Concord Police Department
Cullman County Sheri