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Sample Templates for Analyzing 

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A Suggested Approach to Analyzing Racia

Sample Templates for Analyzing Car-Stop Dat

By 

Joyce McMahon, Ph.D.,

Amanda Kraus, Ph.D.,

The CNA Corporation 

This project was supported by cooperative agreement #2002-H

by the Office of Community Oriented Policing Services, U.S. D

Justice. The opinions contained herein are those of the author(s

necessarily represent the official position of the U.S. Departme

References to specific companies, products, or services should 

considered an endorsement of the product by the author(s) or th

Department of Justice. Rather, the references are illustrations to 

discussion of the issues. 

A Suggested Approach to Analyzing 

Sample Templates for Ana

Introduction 

Because of community concerns about the use of race as a 

factor in police decision-making, many police department

and used data from car stops to investigate the issue of rac

bias-based policing. Many more departments are consider

Most existing evaluations based on car-stop data have reli

comparisons between the racial composition of stops in a 

racial composition of the city’s total population. Unfortunat

aggregate approach can be dangerous. By failing to include 

information on police operational procedures, specific city 

and socio-demographic characteristics of neighborhoods, th

approach is too simplistic and may generate misleading res

Decisions regarding the merits of racial profiling concern

and should not be based on either anecdotal evidence or i

analysis. Evaluating the extent and nature of police profil

may lead to decisions regarding proper training and appro

tactics. It is crucial that such evaluations rely on appropriate 

approaches, objectively obtained data, and appropriate be

comparison guidelines. 

The Justice Department’s Office of Community Oriented 

Services (COPS) asked The CNA Corporation (CNAC) t

two police departments to address data collection and eva

This effort represents a follow-on to a previous CNAC st

COPS Office, which produced the report, How to Correc

and Analyze Racial Profiling Data: Your Reputation Dep

(available on the COPS web site).1 The purpose of the ne

apply the tools and methods recommended in the previou

creating partnerships with two police departments and he

rigorous analytical methods that go beyond the standard p

approach relies on blending analytical research methods w

police insights. 

1 How to Correctly Collect and Analyze Racial Profiling Data, 2002. Report available in 

COPS Office (800-421-6770) or at the COPS web site, http://www.cops.usdoj.gov/default
2 COPS Innovations 

This pamphlet does not include any actual data or specifi

the two departments. Instead, it describes the general app

and illustrates them with sample templates of the analytic

These templates represent examples of how to display an

results from various methods of analysis. They were selecte

the range of analytical questions that can be addressed by 

techniques. 

The traditional (standard) approach to data analysis–and its lim

The typical approach for using police administrative data 

presence of racial profiling practices is to compare the ra

of vehicle stops to that of the city’s population. Consider 

a hypothetical city in which 33 percent of the people who 

belong to a minority race group and 67 percent belong to 

group. If the demographic data for the city show that the ra

of the population is the same–33 percent minority and 67 

minority–the standard conclusion that would be drawn fr

equal racial shares is that there is no racial profiling; all i

By using this approach, people are effectively allowing th

mix of a city’s total population to act as an estimate of th

mix of the observed violator population. More specificall

assuming that members of each race group violate traffic 

same rates and that officers observe members of each rac

committing these violations in the same proportions. Whe

assumption holds has been a subject of much debate, and 

impossible to prove or disprove. The second assumption 

not expected to hold because it is well understood that de

officers, drivers of different races, and problem traffic are

dispersed unevenly across a city’s geography. 
A Suggested Approach to Analyzing 

Sample Templates for Ana

Finding the right comparison group 

A more general characterization of the main conceptual p

the standard approach is that city population data don’t p

comparison groups. Figure 1 shows how members of a ci

population–whether they are residents of the city or not–b

of the stopped and cited populations. The ideal approach 

whether members of one race group are inappropriately s

higher rates than members of other groups would be (a) t

race breakdown of the total violator population with that 

violator population, (b) to compare the race breakdown o

violator population with that of the stopped population, a

consider reasons why variations between the groups exist

it is virtually impossible to determine the racial make-up 

total violator population or the observed violator population

and police analysts have usually made comparisons betw

breakdowns of the stopped and the resident populations. 

that the resident population is several steps removed from 

populations and is likely to be quite different for reasons 

independent of police activity as well as for reasons that 

police policies and behavior. 

Reasons for differences in the racial compositions of the 

populations in the figure can be considered in more detai

from left to right in Figure 1, it can first be seen that the rac

of the available driving population may be different from 

composition of the city’s population for two reasons. Firs

own driving population may not have the same racial com

city’s total population. This will be true if race breakdow

driving age or car ownership. Second, if the non-resident 

population is sufficiently large, then differences between 

compositions of non-resident drivers and the city’s popul

cause the driving population to be racially different from 

population. If the race mix of the driving population is di

that of the city’s population for either of these reasons, th

that all the populations that are “downstream” from the dri

will also be racially different from the city’s population. 

differences will occur independent of any action on the p

officers, they cannot be the result of racial profiling. 
4 COPS Innovations 

Figure 1. How members of a city’s total population become 

city’s driving and stopped populations 

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Next, consider sources of differences between the racial c

the driving population and the violator population. The m

is whether people of different races commit infractions at 

If they do, then the race distributions of the two groups w

they don’t then the race distributions will not match. Aga

difference will occur independent of police activity. 

Moving from the violator population to the observed viol

is the first place where police department policies can co

differences between the stopped population and the city p

Specifically, the racial make-up of the observed violator p

differ from that of the total violator population due to var

police deployments across geographic areas and times of 

differences are legitimate if deployment patterns are dete

respect to race. Next, differences in the race breakdowns 

violator and stopped populations can arise due to the inte

racial differences in violation rates on the one hand, and p

and procedures regarding which violations should be sing

stops, on the other. Again, if such policies are determined 

to race, then differences between populations can be consid

Therefore, an important corollary to recommendations on 

using administrative data to show whether a department p

profiling is that departments need to be able to document 

behind existing deployment patterns and other operationa
A Suggested Approach to Analyzing 

Sample Templates for Ana

Finally, individual officers will use their own judgment an

of department policies to decide whom to stop, and then 

or search among those they have stopped. Racial differen

the observed violator and stopped populations and racial 

between the stopped and cited (or searched) populations 

of the behaviors of both the officer involved and the stop

therefore, they can be the result of racial profiling or not. 

Following the flow from the city’s total population to the 

searched or cited populations clearly shows the problems 

with using comparisons between the racial compositions 

to prove or disprove that a police department’s officers ar

racial profiling. Therefore, such comparisons should be in

extreme care. 

Matching definitions of race across data sources 

Another problem with making simple comparisons betwe

compositions of the stopped populations and racial comp

cities’ populations is the fact that measuring race is not a

straightforward. The U.S. Census Bureau publishes self-r

data by one race alone, two races, and three or more race

also indicate self-reported Hispanic ethnicity. In contrast, 

collected information on a citizen’s race typically reflects 

assessment of the individual’s racial background based on 

person looks. Since it’s difficult for police officers to capt

of mixed races, stop or citation data usually just have one 

each race and don’t capture multiple races. Therefore, in 

U.S. Census race definitions may not match race definitio

data even when they have the same labels. In addition, in 

instances, stop or citation data are recorded as Caucasian 

American, omitting all other races and ethnic categories (
6 COPS Innovations 

Controlling for many factors at one time 

Finally, simplistically comparing race data for stops again

total populations can’t capture multiple correlations betw

factors associated with stops and violations. Therefore, a 

is to use multivariate techniques that allow researchers to 

the effects of several factors all at the same time. For exa

likely that more police officers are deployed and making 

crime, low-income neighborhoods. Unfortunately, in the 

such neighborhoods are also likely to be minority neighb

Therefore, it is possible that disproportionately high stop 

minorities may be the result of police deployment pattern

police bias. 

One way to explore the combined impact of multiple fact

investigate the direction of causality, is by the use of nest

Samples of these types of tables are provided later on (e.

4). For example, suppose that an investigator is examinin

minority classification. Rather than just look at stop rates 

with various race/ethnic groups, it is important to look at 

these groups within specific types of car stops (e.g., drivi

vehicle equipment), for different times of day (e.g., rush 

evening), or within different neighborhoods (e.g., central 

residential areas, shopping areas). Examining the basic m

stops in this way has the potential to provide substantially 

information about the multiple influences that may affect 

tables can be set up to explore these different influences b

calculating rates for each pertinent cell defined (e.g., stop 

ethnic group for driving related incidents at night in the c

In addition, other multivariate techniques (such as regres

that include neighborhood-specific data on income, race, 

of deployed police officers can measure the separate effe

these potentially correlated factors. One specific example 

controlling for type of stop along with race. Research has 

Black drivers are less likely than drivers of other races to 

belts.2 Therefore, if a large percentage of stops were relat

wear a seat belt, Black drivers would be likely to have a dis

2 See: “Achieving a Credible Health and Safety Approach to Increasing Seat Belt Use Am

Americans,” Department of Occupational and Preventive Medicine, Meharry Medical Co

“Blue Ribbon Panel to Increase Seat Belt Use Among African Americans: A Report to the 

2000, p. 11, DOT HS 809 185. 
A Suggested Approach to Analyzing 

Sample Templates for Ana

high share of the total. Thus, it would be important to con

violation or reason for stop in a racial profiling study bas

car stops. Regression analysis is well suited for this type 

investigation. 

Another aspect of controlling for many factors is to consi

factors are likely to be important in a given jurisdiction. D

are likely to have different problems, geographic conside

histories with respect to citizen-police interactions over ti

approach is to survey the citizens of a jurisdiction to dete

perceptions (e.g., of the police force, crime, police initiat

understand citizens’ views concerning various issues (e.g

profiling, neighborhood crime concerns, traffic congestio

parking). Using surveys is a productive way to determine 

of factors that are relevant for a particular city or jurisdic

Data collection 

What kind of data can be collected? 

There are at least four general categories of data, as descr

next page. This should not be considered a definitive or e

of data elements. Depending on the questions that are con

important, and the particular characteristics of a departme

surrounding city or county, some of these measures may 

There may also be data elements that would be important 

that are not included. 

Although this may appear to be a lot of information, mos

collected–often as part of standard practices. For example

making a traffic stop will usually record the year of the c

of stop. With this information, it is possible to create a va

describe the age of the car. In addition, characteristics of t

making the stop need not to be collected for each incident

a coded officer identifier for each stop, stop data can be m

from personnel files. Finally, information about the stop lo

derived from U.S. Census data and supplementary operati

3 See Deborah Weisel, Conducting Community Surveys: A Practical Guide for Law Enfo

178246, 1999, Washington, DC: U.S. Department of Justice, Office of Justice Programs. W

practical guide for law enforcement agencies, which includes a description of how survey

improve policing services, in addition to techniques for identifying survey goals and proc

administration and analysis. The guide reviews many practical aspects of using surveys, i

telephone surveys, tailoring surveys to fit different needs, sample size, probability samplin

and costs. 
8 COPS Innovations 

Categories of Data 

Citizen characteristics Officer characteristi

 Demographics  Demographics 

 Car age/type  Career and assignm

 Address  Behavior 

 Behavior 

Stop information Site information 

 Location  Demographics 

 Time of day  Urban/rural, busine

 Duration  Socio-economic da

 Reason  Traffic flow 

 Outcome  Officer deploymen

 Calls for service 

Deciding what data to collect 

Figure 2 provides a flow diagram of a process that can be 

data collection decisions. It is motivated by the most basic 

what do the department and the community want to learn

another way, which questions are analysts attempting to a

ability to articulate what is to be investigated should prec

of exploring data elements and selecting an analytical app

Following this process will allow departments to evaluate 

the data elements they are collecting in tandem with the m

available to them. One of the important aspects of this m

it allows investigators to explicitly consider what questio

answered using alternative approaches. For example, give

of available data elements, there will be some questions t

answered, but there are likely to also be some questions t

addressed by that data. The investigators need to know th

it in advance, so that they can determine whether it is imp

obtain additional data, or alternatively so that they can ex

certain questions will not be answered using the current d
A Suggested Approach to Analyzing 

Sample Templates for Ana

Figure 2. Steps to Guide Data Collection 

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Depending on what analytical method is eventually selec

advisable for police departments to consider the merits of 

with an analytically trained social scientist. 

CNAC research partnerships results 

General lessons learned 

CNAC established research partnerships with two law en

agencies that differed substantially in terms of characteris

surrounding communities and the data being collected. In 

there were separate challenges to evaluating the data; sur

however, some lessons learned were the same for both de

that similar patterns showed up in both data sets. Specific

findings held across both communities: 

 Nonresident drivers affect outcomes. 

 The race share of stops/citations varies significan

of day. 

 Race and gender interact with type of stop/citatio

 The race mix of stops/citations varies with the ra

stop location’s population. 
10 COPS Innovations 

General approach to interpreting data 

To investigate the presence or absence of racial profiling 

data can be analyzed at three levels: department or cityw

tract, and by individual officer. Generally, our approach w

data to identify patterns in the race mix of stops and stop o

might call for further investigation. More specifically, rat

the standard technique of making simplistic comparisons 

race mix of stops and the race mix of the cities, we focus

sample comparisons across race groups, and used both bi

multivariate analysis techniques. This approach allowed u

some of the pitfalls associated with the standard techniqu

Although the focus of this project was on using data to an

profiling issues, the same data can be used to help depart

operational efficiency and to learn more about what pract

effective in achieving operational goals (and what practic

effective). In the process of trying to understand why out

department can learn about many aspects of its operation

profiling. 

Sample standard templates to evaluate stop and citation 

In the following samples of standard templates, we prese

structures that include a selection of important variables a

structure of analysis. These are examples of how investig

templates, either in table or regression formats, to investi

citation data. In addition, these approaches can be used to 

associated search data, as well as officer efficiency metri

tables, we do not present actual numbers–while we show 

the table, all numerical entries are represented by XX. W

actual numbers, in part because we do not want to imply 

examples support findings that might indicate the possibi

profiling, or appear to negate the existence of racial profi
A Suggested Approach to Analyzing 

Sample Templates for Ana

If we were to use actual numbers in our tables, it would b

for readers to try to interpret the meaning of the results. T

be inappropriate for a number of reasons. First, we are no

models – just template examples. In the real world, there 

influences that should be considered, and these factors w

different jurisdictions. In addition, if we used numerical e

would need to describe how all of the variables are meas

scale), discuss the variables’ observed variations (standar

and correlations with each other, and motivate a discussio

testing and statistical significance. It is not our intent to a

overall topic of how to do analytical modeling or assess t

significance–these can be complicated issues and are bes

in the context of social science estimation or prediction te

We begin our examples of data templates by considering w

stop data. The most general way to expand the traditional 

stop data by race is to create a table structure that supplie

about additional factors, such as gender, time of day, or ty

Figure 3 displays a template for disaggregating data by ty

controlling for minority status. In this example, stop data 

separately for driving-related stops and vehicle equipmen

although other types of stops could be considered as well

analysis has the advantage of being very easy to display a

but has the disadvantage that only a limited number of fa

displayed in a single table. 

Figure 3. Disaggregate Data by Type of Stop 

Race Population All 

Driving 

related 

Car stops 

Minority XX.X% XX.X% XX.X% 

Nonminority XX.X% XX.X% XX.X% 

Total 100.0% 100.0% 100.0% 

 Ways to disaggregate: by gender, time of stop, location, etc

 This list of variables informs what data to collect. 
12 COPS Innovations 

When looking at aggregate stop data, operational experts 

alike begin to think about other ways to consider what th

telling them. For example, it may be that most officers ar

minority neighborhoods, or that minorities are more likel

urban neighborhoods with more traffic. These and a host 

considerations can be used to motivate the collection of a

elements that can be used to control for other influences 

the data findings. 

One of the most common considerations is the influence 

location, which can be mapped to a distinct neighborhood 

tract. Figure 4 offers a sample template of one way to exa

stop location. Narrowing down to census tract generally y

idea of the characteristics of the neighborhood and, theor

closer to the real candidate population that is available to 

However, it is still possible that many nonresidents may b

depending on the nature of the neighborhood and the typ

or streets that run through it. 

Figure 4. Analyze Data by Stop Location–Neighborhood or 

Tract 

number 

Number 

of stops Stops Population 

Minority share P

d

XXXXX XX XX% XX% X

XXXXX XX XX% XX% X

XXXXX XX XX% XX% X

XXXXX XX XX% XX% X

 Is the difference between the stops’ minority share and the 

minority share statistically significant? 

This type of tabular result allows a researcher to compare 

share of stops in a specific census tract with the minority 

population in that census tract. If there are differences, tw

ask are how large are the differences and would the comm

them to be meaningful? Again, most evaluators would als

census tract characteristics, such as the median income for 

through streets, presence or absence of shopping centers, 
A Suggested Approach to Analyzing 

Sample Templates for Ana

The effort to control for many factors simultaneously wil

using regression analysis to investigate the issues. Multip

have the advantage of being able to estimate the separate 

several factors at once. This allows the independent effec

to be considered while holding constant, or taking into co

effect of a range of other factors. Figure 5 is a sample tem

results of a multiple regression to evaluate stop rates by c

holding constant several important factors like the minori

tract’s population. (The variables or factors controlled for 

illustrative of the estimation technique, but should not be 

exhaust the possible list of variables.) 

Figure 5. Explaining tract-specific stop rates as a function o

characteristics 

Observation unit = Census tract; Dependent variable = S

Relative ris

Explanatory variable Coefficient ratio 

Minority population share XX XX 

Unemployment rate XX XX 

No. of officers deployed XX XX 

Calls for service XX XX 

Average income XX XX 

Male population share XX XX 

Urban/rural XX XX 

Business/residential XX XX 

 Controlling for other tract characteristics, is the minority p

a significant determinant of stop activity? 

This regression approach can offer compelling informatio

often argued that differences between the minority share 

minority population share at the aggregate level are due t

distributions of officers across areas/neighborhoods in the 

is the case, evaluating at the census tract level should yie

information regarding how stops differ across these areas 

particular, it is also often argued that the correlation betw

presence in a neighborhood and the race mix of the neigh

really a result of the correlation between race and income
14 COPS Innovations 

in U.S. cities. If this is true, then it is important to includ

and race in the analysis (in our terminology, hold constan

effects of income and race). 

Another way to use the information shown in figure 5 is 

estimated model to generate predictions of the amount of 

that would be expected in each census tract, given the lev

variables or factors observed in each tract. Figure 6 show

between predicted stop activity and actual stop activity b

In this example, the expected rates are used as benchmark

rates. 

When the actual rates are substantially different from the 

rates (i.e., when there are outliers), it may be useful to co

supplementary information might explain the differences

if actual stop rates are substantially lower than the predic

might be that red light cameras are being used to monitor 

and that, consequently, officers are turning their efforts in o

Figure 6. Use Predicted Stop Rates for Benchmark Compa

Tract 

number Actual Predicted 

Stop rate 

Actual 

Numb

XXXXX XX% XX% XX 

XXXXX XX% XX% XX 

XXXXX XX% XX% XX 

XXXXX XX% XX% XX 

 Are actual numbers of stops and stop rates significantly diff

predicted? 

 Can supplemental data explain differences? 
A Suggested Approach to Analyzing 

Sample Templates for Ana

Another way to disentangle stop data is to look for patter

outcomes, such as conducting a search or issuing a citatio

to analyze search outcomes, the first step is to explore wh

that the stop has occurred, minorities are more likely than 

searched. In addition, it is interesting to compare the resu

by race (and possibly by other factors as well). 

Looking at the search results can indicate how effective, 

searches are across racial subcategories, and can also ind

of illegal product was most frequently discovered (e.g., w

A sample template for this type of analysis can be seen in 

Figure 7. Use Data on Search to Analyze Profiling as Well a

Race All Search 

Stops 

Nothing Weap

Search re

Minority XX% XX% XX% XX% 

Nonminority XX% XX% XX% XX% 

Total 100% 100% 100% 100% 

 Conditional on being stopped, are minorities significantly m

searched? 

 Conditional on being searched, are minorities significantly m

have contraband? 

 Do searches have high overall yield rates? 

Some authors have focused especially on stop outcomes 

on stop outcomes are considered to be cleaner than data o

Specifically, since it is usually not possible to identify the 

violator population (see figure 1), analysts don’t have any 

about drivers who could have been stopped, but weren’t. 

since stop outcomes are conditional on the occurrence of 

possible to identify who experienced each outcome, and 

important, who did not. In this case, therefore, there is no 

about having identified the denominator correctly. 
16 COPS Innovations 

Knowing which drivers experienced a specific outcome a

not also enables researchers to use regression analysis to 

likelihood that a specific event will occur as a function o

characteristics and characteristics of the stop. In other wo

techniques allow us to look at a number of different varia

be influencing an outcome, and figure out which of the v

actually have an important and substantial impact. 

Figure 8 shows a template for reporting the results from a 

regression technique that estimates the separate impacts o

factors that influence whether a person who has been stop

subsequently searched.4 The citizen race variable in the list 

(or influencing) variables is the one of interest with respe

profiling questions. In this case, the model estimates the 

driver’s race on the likelihood of being searched, holding 

separately taking into account, the influence of other fact

citizen gender, car age, reason for stop, time of day, and v

characteristics. 

Figure 8. Control for Multiple Factors Using Regression An

Observation unit = Stop; Dependent variable = Search/no 

Relative ris

Explanatory variable Coefficient ratio 

Citizen race XX XX 

Citizen gender XX XX 

Car age XX XX 

Officer race XX XX 

Officer’s tenure XX XX 

Officer’s unit XX XX 

Reason for stop XX XX 

Time of stop (day/night) XX XX 

 Is citizen race significant even when controlling for other a

stop? 

4 The statistical model used here is a logit model. A logit is a non-linear regression mode

in analyses when the outcome variable being modeled is a probability (such as the probab

driver was searched or not), rather than a continuous variable (such as the number of cita

Logit models are better than linear models for predicting probabilities because the predic

logit always fall between zero and one. Logit models are often used for dependent variab

outcomes (denoted as 0 or 1 outcomes), but can also be used for more complex dependen

technical notes on the logit model, see Peter Kennedy, A Guide to Econometrics, MIT Pre

William H. Greene, Econometric Analysis, Prentice Hall, 5th edition, 2003; and David K

“Log Linear Models (Quantitative Applications in the Social Sciences), Sage Publication
A Suggested Approach to Analyzing 

Sample Templates for Ana

In figure 8, officer characteristics were included in the list 

variables as a suggestion rather than as an example of typ

variables. Although most departments seem certain that th

officer’s race does not influence the race of the stopped (

searched) citizen, very few departments have actually put 

However, it is important to consider using variables that 

officer characteristics. 

For example, minority officers might do racial profiling o

or they might do “reverse” profiling. The key is that diffe

officer race and experience level may tell different storie

terms, this notion is framed by asking whether profiling i

White vs. Black or a question of Blue vs. others. The prim

that officer data have not been included in analyses are c

privacy and liability. Therefore, the suggested method for 

officer information is to use identification codes so that d

matched to specific officers without identifying them by 

Another way officer identification can be used to analyze 

is to examine officers’ stops and compare them with som

benchmark can be externally provided, by taking a standa

departments, for example. Another approach is to create a

department-specific benchmark by calculating an average 

among a department’s own officers, and then comparing 

officer data with the average for the department, unit, bra

or neighborhood, depending on what exact behavior is be

Using hypothetical data, Figure 9 shows an example of th

analysis that can be performed. This example allows an e

consider whether there are officers who stop significantly 

minorities than the unit’s overall average. It is important 

although this approach identifies officers who are perform

below an expected average range, this does not necessari

whether they are doing anything wrong. There may be m

would easily explain why a particular officer had an unus

low minority share of stops over a given period of time, s

month. However, looking at such a metric allows departm

to consider these factors, and determine whether any unu

might merit additional investigation or consideration in th
18 COPS Innovations

Figure 9. Analyze Data at the Officer Level and Compare w

0 

0.2 

0.4 

0.6 

0.8 

1 

Share of Stops 

12 3 4 5 6 7 8 9 10 11 12 13 14 

Officer 

              

Minority share of officer’s stops 

 

 Which officers stop significantly more minorities than the 

 Can supplemental data explain differences? 

The key to this type of evaluation is picking the right ben

as a comparison, and then identifying outliers and using o

examine and explain them. For example, the race mix of 

officer is likely to depend on the area he or she patrols, a

area, on the time of day during which he or she is on duty

the benchmark should incorporate as many relevant facto

It is also possible to use officer identification codes to ex

other than racial profiling. Figure 10 provides a very gen

example of the potential use of stop data to explore office

For a group of hypothetical officers, the figure compares 

which each officer issued citations after making stops to 

average citation rate. Thus, in this case the group average 

designated internal benchmark. 

5 Some areas or neighborhoods have very different nighttime and daytime populations. 
A Suggested Approach to Analyzing 

Sample Templates for Ana

Figure 10. Using Stop Data to Analyze Officer Efficiency 

Share of driving related stops for which a citation 

0 

0.2 

0.4 

0.6 

0.8 

1 

Share of Stops 

             

 Officer 

Unit ave

12 3 4 5 6 7 8 9 

Officer 

10 11 12 13 





Which officers give fewer citations than the unit average? 

What is the relevant benchmark? It should reflect departm

goals. 

Again, the key is to select the correct benchmark and the

outliers and use other data to explain them. Officers who 

citation rates may or may not have excellent reasons that 

their rates are unusually high or low. For example, differe

have different operational mandates. The traffic unit is go

traffic and ensure traffic safety. So, officers in this unit ar

to be looking for specific types of violations that are asso

danger and may be more likely to issue tickets. In contra

other units will make car stops and traffic stops more opp

and potentially for different reasons. These and many oth

time of day officer is on duty) must be taken into conside

comparing across officers. 
20 COPS Innovations 

Summary 

The methods of analysis shown in this report are only a s

types of analysis that can be performed in the context of 

or officer evaluations. As indicated before, the type of an

should be done depends on the nature of the department, th

of the surrounding population, and the specific questions 

department and citizen groups may be posing. The questi

asked will determine what data should be collected and h

be analyzed–both with respect to modeling technique and 

detail that is maintained (e.g., stops, searches, officer iden

Generally speaking, multivariate analysis will require car

properly control for the various factors that influence stop 

and searches. A productive way to approach this type of a

through a partnership between local police operational ex

science researchers versed in applying analytical method

partnership can greatly enhance the processes of data col

analysis, and interpretation. 
A Suggested Approach to Analyzing 

Sample Templates for Ana

Additional Resources 

Fridell, Lorie. By the Numbers: A Guide for Analyzing Ra

Vehicle Stops. Washington, DC: Police Executive Researc

Fridell, Lorie, Robert Lunney, Drew Diamond and Bruce 

Michael Scott and Colleen Laing. Racially Biased Policin

Principled Response. Washington, DC: Police Executive 

Forum, 2001. 

McMahon, Joyce, Joel Garner, Ronald Davis, and Amand

to Correctly Collect and Analyze Racial Profiling Data: Y

Depends On It! Washington, DC: Government Printing O


www.c

FOR MORE INFORMATION 

U.S. Department of Justice 

Office of Community Oriented Policing Se

1100 Vermont Avenue, N.W. 

Washington D.C. 20530 

To obtain details on COPS programs, call t

COPS Office Response Center at 800.421.

Visit the COPS internet address listed belo

Cre