HomeMy WebLinkAbout10-22-2002 CommunicationMEMORANDUM
POLICE CITIZENS REVIEW BOARD
A Board of the City of Iowa City
410 East Washington Street
Iowa City IA 52240-1826
(319)356-5041
DATE: September 19, 2002
TO: PCRB 1
FROM: Marian K. Karr, City Clerk ¢d4
RE: PCRB Video
The City is exploring a video library within our City website. In the near future
you may be able to access the PCRB video on the internet using the City
website, icgov.org
POLICE CITIZENS REVIEW BOARD
A Board of the City of Iowa City
410 East Washington Street
Iowa City IA 52240-1826
(319)356-5041
October 8, 2002
Service Organization
Contact
Address
City, State Zip
Dear
The Police Citizens Review Board now has an informational video available to
interested persons or organizations.
The primary focus of the video is to inform and engage the citizens of Iowa City
regarding the origins, role, and function of the PCRB and the process by which
complaints against the police are reviewed. The video is approximately 11
minutes long.
There are two copies of the video available at the Iowa City Public Library for
viewing or they can also be checked out for use in presentations to community
and neighborhood groups, service clubs, City boards, commissions and
employees.
The video can be found on the New NF Video Shelf or someone from the
Information Desk can assist you.
Copies of the video may also be purchased through the City Clerk's office for
$13.00.
Sincerely,
John Stratton
Chair, Police Citizens Review Board
DRAFT
Altrusa Club of Iowa City
District 7
Ann Smothers, 2050 Prairie du Chien Road, NE, IC 52240
American Legion
Walter Johnson Post 721
Dorothy McCabe, 1015 - 8th Street, Coraville, 52241
Johnson County Bar Association
Lois Cox, 112 South Dodge, IC 52240
Business and Professional Women
Malinda Allen, 2756 Lake View Drive NE, Solon 52333
Business Women's Association
Golden Hawks Chapter
Linda Tompkins, 314 East Court Street Place, IC 52240
Business Women's Association
Grant Wood Chapter
Michele Branstatter, 1483 High Country Road, Coralville 52241
Downtown Association
Lisa Barnes, P. O. Box 64, IC 52244
Fraternal Order of Eagles 695
Howard Cook, 2128 South Riverside Drive, IC 52246
Order of Eastern Star
Jessamine Chapter
Doris Thompson, 170 Paddock Circle, IC
Elks Lodge # 590
Al Williamson, 637 Foster Road, IC 52245
Jaycees
Jay Honohan, 2122 J Street, IC 52240
League of Women Voters
Box 2251, IC 52244
Kiwanis, Golden K
Jack Yanaugh, 110 Montrose, IC 52245
Kiwanis, Old Capitol
Lee McCormick, 1725 E Street, IC 52240
DRAFT
Kiwanis, Noon
Al Seagren, 2949 Washington Street, IC 52240
Lions, Evening
Dave Smith, 710 Brookside Drive, IC
Lions, Noon
Judy Terry, 3575 Hanks Drive, IC 52240
Lions, UIHC
Mary Jo Piper, 1703 - 21 st Avenue Place, Coraville 52241
Masons, # 4, AF & AM
Robert Hibbs, 606 Reno Street, IC 52245
Masons, York Rite
Robert Woodburn, 3109 Court Street, IC 52240
Loyal Order of Moose
Mark Stimmel, 3151 Highway 6 East, IC 52240
Independent Order of Odd Fellows
Eureka Lodge # 44
Jim Bigelow, 2427 Petsel Place, IC 52246
Veterans of Foreign Wars
Van Eyck Post
Sheryl Cook, 2128 South Riverside Drive, IC 52246
Optimists, Morning Club of Coralville
Dave Wilderson, 950 Applewood Court, # 2, Coralville 52241
Optimists, Noon Club of Coralville
Jeff Rubel, 1040 - 20th Avenue, Coralville 52241
Optimists, Noon Club of Iowa City
Scott Means, 19 Ravencrest, IC 52246
Optimists, Sunrise Club of Iowa City
Bill Hubbard, 1445 Oaklawn, IC 52246
Optimists Club of North Liberty
Darlene Smith, P. 0. Box 106, North Liberty 52317-0001
PEO, Chapter JF
Janet Power, 1012 Estron Street, IC 52246
PEO, Chapter KIP ®RAFT
Anne Tanner, 427 Elmridge Avenue, IC 52240
PEO, Chapter OD
Ann H. Weber, 1731 Red Oak Drive, Coralville 52241
Pilot Club of Iowa City
Betty Ketchum, 2929 Cornell Avenue, IC 52245
Noon Pilot Club
Ann Weber, 1731 Red Oak Drive, Coralville 52241
Rotary, Noon of Iowa City
K. Hughes, P. 0. Box 684, IC 52244
Rotary, AM of Iowa City
Elaine Shalla, P. 0. Box 3166, IC 52244
Sertoma Club, Old Capitol
Chuck Lindemann, 4306 Dane Road SW, IC 52246
Sertoma Club, U of I
Maralee Dyson, 837 Kirkwood Avenue, IC 52240
Shrine Club
John 0. Cornelius, 1272 Oakes Drive, IC 52245
1 October 2002
TO: Members of the PCRB
FROM: Loren Horton/
RE: Police Traffic Stop Data Study
As you know, I will miss the meeting scheduled for October 22, at which you probably
will discuss the traffic stop study. Therefore I wanted you to be aware of my thoughts about
some of the factors involved. You are aware that I attended the work session of the City
Council at which the study was presented. There were 3 articles in the Iowa City Press -
Citizen about the study. We might keep in mind that the study was done by and for the Police
Department, and neither the Police Department nor the City Council have referred it to us for
comment.
In the study itself, I call you(attention particularly to four items. On page 10 it states
that city census figures should not be 6)`ed as a baseline for comparison because of the
number of commuters, visitors, and students here. On page 20 it lists factors that might be
present during a traffic stop but are not measured (such as demeanor of driver, anything about
passengers, weather, what is going on in town at the time, location, etc.). On page 24 it
notes that the racial distribution of stops does not necessarily have anything to do with the
racial distribution in the general population. I will have more to say about that later. On page
31 it states that census data does not provide an appropriate point of comparison for traffic
stops.
Present at the work session and at the City Council meeting the following night was
a person who had much to say about the report. He tried to speak at length at the work
session, but was not allowed to do so. He did speak for his allotted 5 minutes at the regular
council meeting. One of his points at the work session was that the study was flawed
because it did not test the data from traffic stops against the drivers who were not stopped.
I seriously doubt if any one can know who was not stopped nor how many were not stopped.
We have to work with data from drivers who are stopped. Who can know how many other
drivers were on the streets that day at that particular time ?
This study included data from April 1 to December 31. 1 believe that any comment
should be delayed until a full year's data is available for study, whether that be April 1 to
March 30, or January 1 to December 31.
The census statistics for Iowa City and Johnson County are not particularly helpful in
evaluating the data from traffic stops, UNLESS we know whether or not the stopped driver
actually is a resident of Iowa City or Johnson County. Otherwise the number of males and
females, the races of the drivers, etc. who are stopped are not comparable to percentages of
such categories in the general population, CENSUS FIGURES COUNT EVERYONE (at least
theoretically they do), WHETHER ORNOT THEY ARE DRIVERS. A SIGNIFICANT NUMBER OF
PEOPLE COUNTED IN THE CENSUS ARE T00 YOUNG TO DRIVE.
IOWA CITY POLICE DEPARTMENT
410 EAST WASHINGTON STREET, IOWA CITY, IA 52240
(319) 356-5275 ! FAX # (319) 356-5449
"An Accredited Police Department"
Date:
August 7, 2002
To:
City Council
From:
RJ Winkelhake
Ref:
Police Traffic Stop Data Study
The University of Louisville will present the results of the study of the Traffic stop
Practices of the Iowa City Police Department at the work session of the City Council on
the 1 gth of August 2002.
A copy of the report is in the Council packet.
Traffic Stop
Chief
Research Team
Terry D. Edwards, J.D.
Elizabeth L. Grossi, Ph.D.
Gennaro F. Vito, Ph.D.
Angela D. West, Ph.D.
University of Louisville
Department of Justice Administration
Brigman Hall, 2"d Floor
Louisville, KY 40292
(502)852-6567
June 13, 2002
ice Department:
*This report is confidential and is intended for the Iowa City Police Department to use as it deems
necessary. It is not to be distributed, quoted, or cited without the express written consent of the
authors, of Chief R.J. Winkelhake, or others that the ICPD may designate.
Executive Summary
This report summarizes the findings of a study conducted using data collected by the Iowa City
Police Department between April 1, 2001 and December 31, 2001. These data resulted from 9,702
interactions between law enforcement officers and citizens during traffic -related contacts.
Information was collected about the driver, the officer, and the stop event. Driver demographics
included race, sex, age, residency, and vehicle registration. The only information collected about the
officer was officer badge number. Finally, data collected about the stop event include the date, time of
day, "reason for stop," "search," "property seized," "force," and "outcome of the stop."
Data analysis was conducted with the aid of SPSS-11.0 (Statistical Package for the Social
Sciences). Analyses were conducted on two levels. First, descriptive analysis, using percentages,
summarized stop patterns, stop characteristics, and driver demographics. This information is useful only
to describe the existing state of affairs ("what is'), but not to explain them ("why") or to formulate
predictions about future events ("what if'). To address the complex relationships that exist among
different variables, a program called "chi-square automatic interaction detector" or CHAID was used to
evaluate the variables in terms of their relationships with one another (muhivariate analysis).
The greatest percentage of stops was made in the month of April (15%), with the fewest in June
(90/9). Interestingly, 41% of stops occurred between midnight and 3am, with the third shift (1 1pm-7am)
responsible for the greatest percentage (54a/u).
Stopped drivers were mostly White (84%), male (65%), young (median age of 23), Iowa City
residents (62%), with Iowa vehicle registrations (86.5%). Drivers were mainly stopped for moving
violations (69"/o), were not searched (95%), and were released with a warning (58%).
The descriptive analysis indicated some slight percentage differences among the races in certain
events (e.g., stopped for equipment/registration violations). These percentage differences, however,
cannot be used to infer correlation or causation ("racial profiling"). To make these types of inferences,
multivariate analyses using CHAID were conducted. CHAID segments the sample of traffic stops and
reveals the interrelationship between the potential predictors and the events involved in the stop. The
CHAID procedure generates a "decision tree" that identifies significant predictors of each decision in
question. In effect, the procedure "cross-references" each event with each potential predictor.
Results from CHAID analyses resulted in only three events (moving violation, being warned,
being cited) with significant predictors. Being stopped for a moving violation was significantly related to
the age of the driver, the youngest and oldest drivers were most likely to be stopped for this reason.
Warned drivers were those least likely to have been searched, and cited drivers were those least likely to
have been stopped for an equipment/registration violation. Race of the driver never appeared as an
independent predictor of any event.
These data provide no empirical evidence that the ICPD is systematically engaging in
discriminatory stop practices. Stops conducted by the Iowa City Police Department, as a whole, during
the study period, do not involve the race of the driver as a significant factor related to events and
outcomes. This does not mean, however, that no individual citizen ever experienced discrimination. It is
always possible that individual officers may engage in racially biased practices, both in determining
which drivers they will or will not stop and in determining what steps to take after the initial contact. To
detect discriminatory practices at this level, however, requires constant vigilance by the community, by
all the officers within the department, and by the departmental administration. Statistical analysis, while
valuable, cannot substitute for community involvement and effective management.
The full report notes some minor problems with the data entry process, provides a discussion of
the "baseline dilemma," makes recommendations for continued study to obtain a full year of "clean" data,
and suggests modifications of the data collection instrument to include more variables (e.g., warrant
check information).
Table Of Contents
Introduction1-3 - 3
Methods3-7 - 7
DataCollection..............................................................................................3
Variables...........................................................................................................4
Collection and Measurement Concerns.............................................................5
Analysesand Results.........................................................................................6
Descriptive Analyses and Results ............................................
....7 -16
Driver Demographics..................................................................7
StopEvent...............................................................................10
Summary of Descriptive Analyses.................................................................16
CHAR) Analyses and Results...................................................................................16 - 23
CHAIDResults.............................................................................................20
Reason for Stop.................................................................20
Outcome.........................................................................22
Summary of CHAID Analyses ........................................
The "Baseline" Dilemma ..........................
......23
.....................................23
Legal Issues Relating to Bias\Racial Profiling Data Collection and Analysis ........ 26 - 29
Overview................................................................................. 26
CivilLiability............................................................................26
Disclosure of Information/Records...................................................28
Conclusion................................................................................29
Conclusion and Recommendations.........
......................................30 — 33
Bibliography......................................................................................34
Appendices.......................................................................................35
Appendix A: Iowa City Police Department Policy on Racial Profiling
Appendix B: Iowa City Police Contact Sheet
Introduction
Racial Profiling
Accusations of discriminatory traffic stop practices ("racial profiling') have emerged as a
critical issue facing law enforcement. According to a 1999 Gallup poll and research conducted
by the American Institute of Public Opinion (2000), many believe that racial profiling is
widespread and disapprove of the practice of stopping motorists simply because the driver fits a
particular profile (Newport, 1999). In response to this growing concern regarding traffic stops
and a more general distrust of law enforcement personnel, many police departments across the
U.S. have begun to more closely examine their traffic stop policies and procedures. Further,
some police departments have begun collecting traffic stop data The collection of traffic stop
data initially may appear to be a rather straightforward process. In reality, however, the
collection and analysis of traffic stop data is far from simplistic. A number of concerns must be
addressed by any agency contemplating such an endeavor. These concerns range from defining
the issues, developing data collection instruments and procedures, training personnel to collect
data, and determining the most appropriate means to analyze the data.
Defining Racial Profiling
The precise definition of racial profiling is a matter of debate. While no universal
definition exists, racial profiling is generally regarded as any act by law enforcement, whether it
involves motorists or pedestrians, based solely on the race of the alleged violator (Ramirez,
McDevitt & Farrell, 2000). In expanding on this broad definition, the U.S. Department of Justice
considers racial profiling to be "any police action that relies upon the race, ethnicity or national
origin of an individual rather than behavior of that individual that leads the police to a particular
individual who has been engaged in or having been engaged in criminal activity" (Ramirez,
McDevitt & Farrell, 2000). Accordingly, police may use race and ethnicity to determine if an
individual matches a suspect description but police may not use stereotypes when deciding who
to stop, to search, or make subject to other stop — related actions.
Further, as Withrow (2002) notes, profiling by police can be further defined based on
specific factors used in profiling. MacDonald (2000) suggests that profiling can be considered
hard or soft. Hard profiling occurs when race is the one and only factor used in police decisions
to stop a particular motorist. Soft profiling occurs when race is one of several factors the police
use in determining whom they stop.
For this report, the Iowa City Police Department defines racial profiling as "the detention,
interdiction, exercise of discretion or use of authority against any person on the basis of their
racial or ethnic status or characteristics" (Racial Profiling, General Order 01-01). A copy of this
policy is contained in Appendix A.
Collecting Data
Many departments have, independently or in collaboration with others, undertaken the
task of analyzing traffic stop data. These agencies vary in terms of their structure and function,
as well as in the type of data they collect. In addition, some data collection efforts involve
sophisticated data analyses where others simply compare basic percentages. These differences,
on the surface, are not all that dramatic. When making conclusions about the practices of a
department, however, these methodological considerations take on more importance. In fact,
methodological considerations are considered paramount by prevailing judicial opinions (see
following discussion on legal issues). It should be noted; however, just as there are no widely
accepted standards for defining racial profiling, the methods of collecting and analyzing traffic
stop data are not universal.
Police departments across the country collect a variety of data elements in their analysis
of racial profiling. Some agencies collect a minimal amount of data such as the race, age, and
gender of the driver, along with the reason for, and outcome of the traffic stop. Other agencies
collect data pertaining to all passengers of the vehicle, key events that may occur during a traffic
stop (e.g. warrant check, search), and police officer demographics. There appears to be no
consensus regarding the most appropriate data collection elements across departments. The
National Institute of Justice (NM, however, recommends certain data be collected on a "routine"
basis (Ramirez, McDevitt & Farrell, 2000). These data elements include: date, time, and
location of stop, license number and description of vehicle, length of stop, and name and
identification number of the officer initiating the stop. The NIJ also recommends that certain
"study specific" variables be considered. These include the race, date of birth and sex of the
driver, the reason for stop; the outcome of the stop, and whether or not a search was conducted.
Methods
Data Collection
Data were collected about each traffic stop (N = 9,702) made by officers of the Iowa City
Police Department over the nine -month period between April and December, 2001. Officers
were required to enter data into mobile data terminals (MDTs) after each traffic stop interaction.
A copy of this form is contained in Appendix B.
When an officer would initiate a traffic stop, he or she would call that stop into the
dispatcher, who would document the contact. After the stop, the officer would fill out a screen
on the MDT located in the vehicle. These data were centrally stored in a Microsoft Excel
spreadsheet. Each stop became a case for analysis. The Excel file was subsequently transferred
into SPSS for analysis.
Variables
Information was collected about the driver, the officer, and the stop event. Driver
demographics included race, sex, age, residency, and vehicle registration. The only information
collected about the officer was officer badge number. More data about the officer, such as sex,
race, age, time in service, etc., can be entered at a later date. Several items of interest pertain to
the stop event, including the date and the time of day.
One broad category related to the stop event involved the `reason for the stop." These
were coded dichotomously (yes/no) and included the following: moving violation,
equipment/registration violation, criminal offense, other violation, call for service/suspect or
vehicle description, pre-existing knowledge or information, special detail, and other.
Information regarding any "search" that might have been requested or conducted also
was collected and included the following dichotomous variables: consent search requested,
consent search of vehicle requested, consent search of person requested, consent search
conducted, officer safety search conducted, search incident to arrest conducted, and probable
cause search conducted.
Data pertaining to any "property seized" also was collected and included the following
dichotomous variables: property seized, alcohol seized, weapons seized, money seized, narcotics
seized, evidence seized, other seized.
The `outcome of the stop" also was measured, and included the following dichotomous
variables: no action, citation, arrest, warning, and field interview. Finally, information also was
collected about whether any "force" was used during the stop and whether the force was against
the driver or a passenger.
Originally, 38 variables were measured and entered for analysis. Some of these were
recoded for analysis. For example, driver race was collected in 7 categories (Caucasian, Black,
Asian, Spanish, Native American, Other, and Unknown). These were collapsed into 3 categories
for the analyses (White, Black, Other). In addition, some new variables were created to obtain a
clearer picture of the data For example, it is logical to assume that a person stopped for multiple
reasons aught be more likely than a person stopped for only one reason to get a citation or to be
arrested. The dichotomous variable "multiple reasons" was created by distinguishing between
cases with `only one reason" for the stop and cases with "more than one reason"
Collection and Measurement Concerns
Glitches arise at the initial stages of any large data collection undertaking. This study
was no exception. Although the ICPD engaged in a series of training sessions to familiarize
officers with the data collection form, the use of the MDT, and the procedure, difficulties and
oversights still occurred. This most problematic of these became apparent when Excel entries
(from the MDTs) were cross-referenced with CAD entries. A discrepancy was noted between
the number of stops as indicated by the CAD system and the number of stops as indicated by the
Excel entries. In addition, stops were referenced by officer badge number. A routine check of
CAD and MDT entries appeared that some officers were calling stops into the CAD system and
not entering them into the MDT, or entering them into the MDT without calling them into the
CAD. After a series of inquiries and discussions with the officers, it was obvious that the
problem was related to training and/or to data entry. For example, in situations where 2 officers
were in the car during a traffic stop, one officer may call in the stop to the dispatcher, who enters
the stop under that officers badge number. The second officer in the car may take responsibility
for entering the data into the MDT, which then gets entered under his or her badge number. In
this way, it may appear as if an officer is either under- or over -reporting on the MDT system In
addition, some difficulties were noted with officers forgetting to "save" the data into the MDT
after they had entered it, resulting in some lost information. These problems were quickly
caught and corrected, but it is recommended that any conclusions drawn from this data keep
these difficulties in mind. A fiill year of "glitch -free" data collection is recommended for use as
a baseline for this department. Moreover, any close inspection of stopping behavior by
individual officers should not be undertaken until a full year of corrected data collection is
completed.
Analyses & Results
Analyses were conducted on two levels. First, descriptive analysis takes a broad look at
stop patterns, stop characteristics, and driver demographics. This information is useful for
descriptive purposes only. That is, this type of analysis is useful in understanding the existing
state of affairs (`what is"). Descriptive analyses are neither predictive nor explanatory. They
cannot explain "why" things are as they are and they cannot predict how things might be in the
future. Comparisons using descriptive analyses also are problematic given that descriptive
statistics do not consider relationships among different variables involved in any given situation.
To address the complex relationships that exist among different variables, multivariate
analyses also were conducted. Specifically, a program called "chi-square automatic interaction
detector" or CHAID, was used to evaluate the variables in terms of their relationships with one
another. For example, this type of analysis is able to determine whether the sex of a driver is
related to the reason for stop, given all the other variables that might interact, such as race or age.
A more detailed explanation of this process is contained below.
Descriptive Analyses & Results
Driver Demo agr Dhics
The variables related to the driver involved in the stop were the following: race, sex, age,
residency, and vehicle registration. Drivers stopped were mostly White (84%), male (65%),
younger, Iowa City residents (62%), and from Iowa (86.5%).
Race. As indicated below, 84% of the drivers stopped were White, 9% were Black, and
7% were Other. The "Other" category includes Asian, Hispanic, Native American, and Other.
There were 13 cases in which the race of the driver was coded as "unknown" and these were
counted as "missing" in the analyses (See Table 1).
Table l: Percentage of Stops by Race of Driver
White Black other
7
Sex: Sex by Race. Most (65%) stopped drivers were male (See Table 2). A higher
percentage of Non -White males than White males were stopped, whereas a higher percentage of
White females than Non -White females were stopped (See Table 3).
Table 2: Percentage of Stops by Sex of Driver
Female Male
Table 3: Percentage of Stops by and Race of Driver
White
■Black
■Other
Female Male
Age: Age by Race. The median age of drivers stopped was 23, with most stopped drivers
being 21. In fact, more than 7 in 10 drivers stopped were under the age of 30 (See Table 4).
Higher percentages of Non -White drivers than White drivers between the ages of 25 and 44 were
stopped. In general, younger (24 & under) and older (45 and over) White drivers than Non -
White drivers were more likely to be stopped (See Table 5).
Table 4: Age Categories of Drivers Stopped
5
4
3
0%
44%
0%
00/B
23%
0%
01 13%16
0%
4%
oo�
Under18 18-20 21-30 31-40 Over40
Table 5: Percentage of Drivers Stopped by Race and Age
S
Under 18-20 21-30 31-40 Over40
18
0
Residency and Vehicle Registration. Of all drivers stopped, less than two-thirds (62%)
were city residents (See Table 6). Another 11 % were Johnson County residents. An equal
percentage either was from other Iowa locations or from out of state (13.5% respectively). This
is probably characteristic of a city with commuters and a large college campus. Given this, city
census figures should NOT be used as a baseline for comparison to the overall stop data.
Table 6: Percentage of Drivers Stopped by ResidenoMegistration
70°
60°
50°
40
30°
20
10
0/
Stop Event
/o
o°
62.0%
/o
/o
to
/o
%
to
11.0% 13.5% 13.5%
M MWI
. .
Iowa City Johnson Other Iowa Non -Iowa
County
Temporal Distribution. The most active month for stops was April (15%), followed by
May (13%), and November (12%). June (9%) was the least active month (See Table 7). The
time distribution of stops was unusual, with 41% of all stops occurring between midnight and
3:00 am. In fact, the most active time was between 1-2am (17%) (See Table 8). This is likely
due to the fact that Iowa City is a college town with a high concentration of bars and restaurants
that close around that general time. Drivers probably get stopped as they are leaving a bar or
restaurant after closing time. Given this time distribution, it is no surprise that the third shift is
responsible for the highest percentage of stops (54%), followed by the second shift (25%), and
the first (21 %) (See Table 9).
Table 7: Percent of Stops by Month
20%
18%
16%
14%
12%
10%
8%
6"/o
4%
2%
0%
PQ01 A50 A* Nola w���c
�Q 'op
jl ■ sm
�I ■ r"t t ■ ■ ■ t �I
�I ■ ■ ■ ■ ■ ■ ■ ■ 1I
Table 8: Percent of Stops by Hour of Day
i
1
1
1
i
�
III
11
'
ill
w
'
I
I
I
!1
r'i'r'�
From
11
,
i
11
1
1 Imm
I
I-1
I I iI
I
I_I_I
11.1_I
„'
I
I
�. �QF Q� Q� QtiQ� . .
11
Table 9: Percent of Stops by Shift
15t Shift 2nd Shift 3rd Shin
In general, drivers were stopped for moving violations (69%) or equipment/registration
violations (26%), were not searched (95%), and were released with a warning (56%). Only 10
cases involved use of force, so this variable was not used in any analyses. Likewise, only 147
(1.5%) cases involved any type of property seizure (mainly narcotics) so this variable is not
considered further. Only 5% of the cases involved a search, and these were mostly (75%)
incident to arrest.
Reason for Stop by Race. The three most -cited reasons for stops were 1) moving
violations (69%); 2) equipment/registration violations (26%); and 3) other violations (6%). Stops
of `other" drivers (71%) were more likely than stops of white (69%) or black drivers (63%) to involve a
moving violation. Twenty-six percent (26%) of all stops were for equipment/registration violations; stops
of black drivers (31 %) were more likely than stops of white (25%) or other drivers (24%) to involve this
reason. Other violations (6%) involved white drivers (6%) more often than black (5%) or other drivers
(5%) (See Table 10).
12
Table 10• Percentage of Stops by Reason and Race of Driver
■ TOTAL
O White
■ Black
■ Other
O�
L
04 O
Searches by Race. Most searches (75%) were conducted incident to arrest; "Other"
drivers (85%) and White drivers (770%) were more likely than Black drivers (64%) to be
searched for this reason. Consent to search was given in 23% of cases, more so by Black drivers
(28%) than by White (23%) or "Other" drivers (8%) (See Table 11).
There were 359 searches incident to arrest (< 4% of all stops) out of 479 total cases in
which a search was conducted (75%). Of all drivers stopped, 3% of White drivers, 7% of Black
drivers, and 3% of "Other" drivers were searched incident to arrest. Of all the drivers searched
for this reason, 79% were White, 15% were Black, and 6% were "Other" (See Table 12).
Out of all drivers stopped, there were 83 consent searches (less than 10% of all cases):
2.4% of all Black drivers were involved in this event, compared to .7% of all White drivers, and
.4% of all `other" drivers. Of the consent searches (n = 83), white drivers comprised 72%,
Blacks were 24%, and other drivers were 4% (See Table 13).
13
Table 11: Percentage of Searches (n = 479) by JiM and Race of Driver
Off'
t�0
CP
■ TOTAL
0 White
■ Black
■ Other
Table 12: Percentage of Searches Incident to Arrest (n = 359)by Race of Driver
White Black Other
14
Table 13: Percentage of Consent Searches (n = 83) by Race of Driver
White Black Other
Outcome of Stop by Race. Other drivers (65%) and black drivers (61%) were more likely
than white drivers (57%) to be issued a warning as a stop outcome (See Table 14). White drivers
(41 %) were more likely than black (37%) or other drivers (34%) to be issued citations. A much
higher percentage of black drivers (13%), however, had arrest as the outcome of their stop. Only
7% of white drivers and 6% of other drivers were arrested. These percentages do not include the
431 cases in which the outcome was "no action."
Table 14: Percentage of Drivers Stopped By Outcome and Race
■TOTAL
❑White
■Black
■Other
oa
P
15
Summary of Descriptive Analyses
At first glance, one might be tempted to conclude that race is a factor in some events. For
example, higher percentages of "Other" drivers were stopped for moving violations while higher
percentages of Black drivers were stopped for equipment/registration violations. Similarly, the
sex and age of the driver also appear to be factors given that higher percentages of White females
were stopped, as were higher percentages of Non -White males.
Descriptive statistics are very superficial and only give the broadest picture of the data
This type of analysis lacks inferential ability. One cannot use it to predict events or to describe
the relationships among characteristics and events. Descriptive statistics only should be used to
describe the state of affairs. They will not help to: 1) understand why the percentages are the
way they are; 2) determine the relationships among the characteristics and events; 3) predict one
outcome or event over some other outcome or event.
Providing a description of the data should only be the first step in a thorough analysis.
More comprehensive multivariate analysis is required to understand the relationships between
and among variables, and to understand how these variables interact with one another to produce
a certain reality, as portrayed by the descriptive statistics. In this case, a procedure called chi-
square -automatic interaction detector (CHAID) was used to more fully explore the relationships
between and among the various variables.
CHAID Analysis & Results
This portion of the report examines the relationship between three demographic
predictors (age, race, sex), vehicle registration (Iowa/non-Iowa) and several events related to the
traffic stop. These events involve the following questions:
16
1) Reason for the Stop? (moving violation, equipment/registration violation, pre-existing
knowledge, other violation, crime, special detail, other).
2) Search Conducted? (vehicle search or driver search).
3) Type of Search? (search incident to arrest or consent search).
4) Property Seized?
5) Outcome (warning, citation, or arrest).
Some of these decision points also were examined as predictors of subsequent events. For
example, whether property was seized might be related to whether a driver was warned, cited, or
arrested.
CHAID is based on an analytical technique called chi-square. Chi-square analysis
demonstrates whether a particular observed proportion within a sample is statistically different
from a particular expected proportion within that sample. The expected proportion is based on
the premise that there is no relationship (i.e., one has no impact on the other) between the two
variables in question within the population from which the sample under study was drawn. It is
calculated using information from the entire group.
For example, if we were interested in whether race (White, Other) and being arrested
(Yes, No) are related in a population, we would use chi-square analysis. The chi-square
procedure would determine that 25% of all the persons (regardless of race) were arrested and
75% were not. Then, the chi-square procedure would determine that, of all the "Other" drivers,
30% were arrested. Chi-square analysis would then conclude whether the 5% difference
between all persons arrested and "Other" persons arrested is attributable to chance, or whether it
is likely that there is a true difference in the population between White and Other drivers in being
arrested. If the chi-square value is "statistically significant," this 5% difference is not
17
attributable to chance and represents a true difference between White and Other drivers in being
arrested. By convention, statistical significance is reached when the probability of error in this
conclusion is less than .05 (Le., only 5 times out of 100 would one reach this conclusion in
error).
Race, however, is just one factor that could be related to any event in a stop situation.
Other variables may be more important. They may mediate, or even eliminate the influence of
race. This is why we use a "measure of association" called the "phi coefficient" with chi-square
analysis. The phi coefficient ranges in value from 0 (no relationship) to 1 (perfect or very strong
relationship). If chi-square analysis indicates statistical significance (that the 2 variables are
related), it is then necessary to determine the strength of that relationship. In the previous
example, the 5% difference in the proportion of Black drivers arrested and the proportion of all
drivers arrested was statistically significant. The question now relates to how strong the
relationship is between race and arrest. The chi-square analysis determines the phi coefficient
for this relationship to be .03. This indicates an extremely weak, almost non-existent relationship
between race and arrest because .03 is much closer to 0 than to 1. In fact, this means that very
little variation in arrest is explained by the race of the driver. Another variable or set of variables
is more influential in arrest than the race of the driver. This is where it becomes necessary to
conduct multivariate analysis.
CHAID is a muhivariate technique that segments the sample of traffic stops and reveals
the interrelationship between the potential predictors and the events involved in the stop. The
CHAID procedure generates a "decision tree" that identifies significant predictors or each
decision in question. In effect, the procedure "cross-references" each event with each potential
predictor.
18
CHAID simultaneously considers the impact of several independent variables (age, race,
sex) upon a particular event in question (arrest, in the example above). The CHAID results
indicate the strongest predictor of the event, while taking the other variables into account. It may
be that no variable or set of variables is a predictor of the event when the other variables are
considered. This means that any original relationship (e.g., between race and arrest) is so weak
that when other independent variables are considered (age, sex), nothing predicts the event.
In the arrest example, the program examines all the cases in which individuals were
arrested. It then examines all the factors associated with each case and determines the ones that
keep occurring in conjunction with an arrest. Then, the program compares that state of affairs
with the cases in which drivers were NOT arrested. In this way, it is possible to determine
whether factors are really predictive of an event or whether observed differences between those
arrested and those not arrested occurred purely by chance.
For example, if descriptive analysis determines that 30% of the drivers arrested were
White and 70% were Black, one might be tempted to conclude that there was a racial bias in
arrests. However, the CHAID analysis would examine the cases and simultaneously consider all
the other potential factors involved in an arrest. The decision tree that it generates might indicate
that the most significant factor related to arrest is a stop for "pre-existing knowledge." The
analyses demonstrate which of the potential predictors (if any) had the strongest and most
important relationship to the events or outcomes. In this case, the potential predictors were used
to examine the five events listed above to determine if they were actually related or whether any
observed differences occurred purely by chance.
The advantage of multivariate analysis is that it reveals the strongest predictors of the
event in question. In other words, if race is a factor, it will emerge independently of the other
19
factors. If race is not a factor, then the one or more of the other predictors will emerge, or none
of the selected predictors will emerge as related to the events/outcomes. If no significant
predictor emerges, it either means that the analyses did not include the most relevant predictors
or that no measured factor is related to the event.
This attribute is particularly relevant for a traffic stop situation in which many things go
unmeasured. For example, one cannot measure the quality of the personal interactions between
an officer and the individuals stopped One cannot measure the demeanor of the driver. In this
case, one cannot measure any information about the passengers in the vehicle. Finally,
extraneous factors such as the weather, the time of year, the social environment, and the location
are not measured in this study.
CHAID Results
Results from CHAID analyses using the 5 event categories and the potential predictors
outlined above resulted in only three events that had significant predictors. Within "Reason for
Stop," being stopped for a moving violation was significantly related to one or more of the
potential predictors. Having a search conducted (vehicle search or driver search), type of search
(search incident to arrest or consent search), and having property seized had no significant
predictors. Two outcomes (warning and citation), however, did have significant predictors.
Arrest, on the other hand, did not.
Reason for Ston
Reasons for stop included the following: 1) moving violation; 2) equipment/registration
violation; 3) other violation; 4) pre-existing knowledge; 5) criminal offense; 6) special detail;
and 7) other. A stop for a moving violation was the only variable that had significantly related
predictors.
20
Moving Violation For the entire group, 68.6% (6656/9702) of the drivers were stopped
for a moving violation This is the base rate for moving violations. The question is whether any
subgroups formed based on the potential predictors had moving violation rates significantly
different from (greater than or less than) that of the entire group.
The most significant predictor of a moving violation was the "age" of the driver. Second -
order predictors were "sex of driver" and "vehicle registration" Sex (being female) was a
predictor for the 10-17 age group. Vehicle registration (non -Iowa) was a predictor for the 18-20,
21-30, and over 40 age groups. Finally, "race" and "sex" emerged as third -order predictors.
Race (being an "other person of color') was a relevant factor among the 18-20 and 21-30 year
old Iowa residents. Sex (being female) was relevant among the over 40 year old Iowa residents.
Apart from order of significance, the subgroups also can be described in terns of their
proportion, remembering that 68.6% is the base to which comparisons are made. The subgroups
receiving moving violations, in order of proportion are:
1. Persons over 40 with non -Iowa registration: 84.5%
2. Persons 10-17 who are female: 81.8%
3. Persons over 40 with Iowa registrations who are female: 77.8%
4. Persons 10-17: 75.0%
5. Persons over 40: 74.6%
Thus, age (being in the youngest group or being in the oldest group) was the strongest
predictor of a moving violation stop. Sex of the driver, registration, and race only emerge in
combination with the other variables, not as independent predictors. There is no evidence of
racial bias in drivers being stopped for a moving violation.
21
Outcome
Warning. Nearly two-thirds (65.5%, or 5383/9702) of the entire group was given a
warning. The most significant predictor of being given a warning was whether a search was
conducted. Those who were not searched were significantly more likely to receive a warning.
The second -order predictor was being stopped for an equipment/registration violation, and the
third -order predictor was having a non -Iowa registration.
Those most likely to receive warnings were:
1. Persons who were not searched, who were stopped for equipment/registration
violations, and who were non -Iowa registrants: 77.1%.
2. Persons who were not searched, who were stopped for equipment/registration
violations: 70.2%
The strongest predictor of a warning was whether a search was conducted. These finding
are logical in that drivers who were searched would be more likely to have been stopped for a
more serious violation and would therefore not receive a warning. Also, drivers stopped for only
having an equipment/registration violation and/or to be from "out of town" might be less likely
to be issued citations. Overall, no bias was detected in the issuance of warnings.
Citation. With this event, the base rate for the entire group was 38.7% (3753/9702). The
most significant predictor of a citation was whether the driver was stopped for an
equipment/registration violation. Being stopped for something other than an
equipment/registration was the most significant predictor of receiving a citation. The second -
order predictor for this group was age (10-17), and the third -order predictor was having an Iowa
vehicle registration (among those over 30 years old not stopped for E/R violations).
Describing the subgroups in terms of their proportion (compared to the 38.7% base), the
subgroups receiving citations are:
22
1. Persons not stopped for equipment/registration violations who were over 30 years old,
with Iowa registrations: 50.4%
2. Persons not stopped for equipment/registration violations who were between 10-17
years old: 50.2%
Therefore, not having an equipment/registration violation was the strongest predictor of a
citation. This is logical given that the other biggest category of stops involved moving violations.
This is the type of situation in which an individual is more likely to be issued a citation. Age and
registration were related to receiving a citation in combination with equipment/registration
violation, not as independent predictors. There is no evidence of racial bias in drivers being
issued a citation.
Summary of CHAID Analyses
Only three events involved significant relationships to tested predictors. Receiving a
moving violation (being in the youngest or the oldest age groups), receiving a citation (not being
stopped for an equipment/registration violation), and receiving a warning (not being searched)
were the only events that CHAID analysis determined to have significant relationships with
predictor variables. Sex of the driver and the vehicle registration also were related in
conjunction with the significant predictors in some situations, but not as independent predictors
of any given event. Race of the driver (being an "other person of color") only appeared once, as
a third -order predictor among certain age groups of Iowa residents in being stopped for a moving
violation.
The "Baseline" Dilemma
The most problematic part of any study of this nature is determining the baseline to
which collected data should be compared. We want to look at `what is" and compare that state
of affairs to `what should be." However, determining `what should be" is troublesome. In
23
theory, the racial distribution of drivers stopped should represent the racial distribution of drivers
doing something that makes them eligible to be stopped. For example, if 20% of the drivers
doing something that makes them eligible to be stopped by the police are Black and 80% are
White, one would expect that 20% of the drivers stopped are Black and 80% are White. This
comparison has very little, if anything, to do with any racial distribution in the city or county
population. It has everything to do with the racial distribution of drivers on the roadways and the
driving behaviors or characteristics that they exhibit.
Making decisions as to whether a department is engaging in discriminatory stop practices
depends on the ability to identify the racial distribution of stops that would exist in the absence
of discriminatory stop practices. That is, one must know the true racial distribution of drivers
eligible to be stopped (i.e., doing anything that could get them warned, cited, or arrested —
anything that creates reasonable suspicion or probable cause). Stops in the absence of
discriminatory practices, then, would be the `right" proportions. One could then compare the
research findings to the `right' proportions to determine whether discrimination exists.
Unfortunately, we cannot measure this objective reality. Determining the `right' proportion of
stops is impossible because of the infinite variations in driving behaviors and police response
within various locations at various times on various days in various months during various years.
Also missing is a measure of the interactions between those stopped and the officers. Demeanor
is thought to significantly contribute to stop outcome as well as to other law enforcement
outcomes such as warning, citation, and arrest.
This reality, however, is extremely difficult, if not impossible, to measure. We cannot
know the racial distribution of drivers doing something that makes them eligible to be stopped.
Some research has attempted to measure this, but the methodology employed is often seriously
24
flawed. The most common method involves posting trained observers at strategic locations
armed with stopwatches to determine the racial distribution of speeders. Obviously, this method
is extremely limited, relying on split second judgment by observers as to the race of drivers. In
addition, this method rests on the assumption that speeding is the only thing for which drivers get
stopped. In the current study, moving violations were the most commonly cited reason for a
stop, but equipment/registration violations and other violations accounted for about 3 in 10 stops.
Given that comparison to population data is invalid, we suggest that the current data become the
baseline from which to evaluate future practices.
The initial analysis of a law enforcement agency's traffic stops does establish a
benchmark for that department. Once an initial study is completed, a department has an
empirical basis for comparison in the finure. If an initial study indicates the possibility of bias
(race appears as a significant predictor of some event), future research will provide data for
comparison to help determine whether the relationship previously observed between race and
some outcome persists or whether it has disappeared. If an initial study shows no evidence of
bias (race does not appear as a significant predictor of any outcome), the department in question
should attempt to maintain this desirable result.
These data, collected from traffic stops made by the Iowa City Police Department
between April 1 and December 31, 2001, provide no evidence that the ICPD is systematically
engaging in discriminatory stop practices. Stops conducted by the Iowa City Police Department,
as a whole, during the study period, do not involve the race of the driver as a significant factor
related to events and outcomes (e.g., arrest, search, etc.). This does not mean, however, that no
individual citizen was ever discriminated against. There is always the possibility that individual
officers may be engaging in racially biased practices, both in determining which drivers they will
25
or will not stop and in determining what steps to take after the initial contact. This is a serious
possibility that is not likely to be revealed with statistical analysis. To detect discriminatory
practices at this level requires constant vigilance by the community, by all the officers within the
department, and by the departmental administration_ Statistical analysis, while valuable, cannot
substitute for community involvement and effective management.
Leval Issues Relating to Bias/Bacial Profifine Data Collection and Analysis
Overview
The findings and conclusions of any study involving bias/racial profiling are often used,
or interpreted, in a number of ways, for a variety of purposes, by many factions. These studies
often raise issues related to the management and administration of the agency, issues relating to
the recruiting, training and attitude of the officers, and issues related to the community, just to
name a few. This section focuses strictly on the legal issues involved with this, or any, study of
bias/racial profiling.
Civil Liability
Without a doubt, the central legal issue relating to any study of bias/racial profiling by a
law enforcement agency is the degree to which the agency, or the individual officers employed
by the agency, may be subject to civil liability for their actions. While the terms "bias profiling"
and "racial profiling" are of relatively recent origin, and neither are legal terms, the practice of
bias/racial profiling, if substantiated, allows victims to pursue civil claims against an offending
agency, or officer, under a variety of legal theories. Although each legal theory has its own
strengths and weaknesses, for a number of reasons, the theory employed by most plaintiffs, and
the one that is arguably the most difficult for plaintiffs to obtain evidence and prove, is that of a
Constitutional violation of the 140' Amendment's Equal Protection Clause. Generally speaking,
IFE
the standard required for a plaintiff to win in an Equal Protection claim is that the plaintiff must
prove that other similarly situated individuals, of a different race, were treated differently.
Likewise, proving, or disproving, disparity of treatment based on race should also be the focus of
any study of bias/racial profiling. Thus, the key importance of any study on bias/racial
profiling, from a legal perspective, is that the study's findings and conclusions can become the
evidentiary basis for supporting, or defending, such claims. In short, the data, and more
importantly the findings and conclusions of the evaluators, of bias/racial profiling studies serve
as the statistical evidence used by plaintiffs or defendants to support or defend the legal claims.
Several courts have addressed the issue of civil liability under the 14te Amendment based
on a claim of bias/racial profiling and the evidentiary requirements needed to support such a
claim These courts repeatedly emphasize the need for both plaintiffs and defendants to
introduce valid and reliable statistical evidence establishing, or disproving, disparate treatment
based on race. Evidence taking the form of statistics based on anecdotal sources, or data
evaluated using unacceptable methodology, are universally rejected by the courts.
In Chavez v. Illinois State Police, 251 F.3d 612 (76 Cir. 2001), a typical Equal Protection
lawsuit, the court went to great lengths to outline the validity and reliability standards required of
evidence relating to the collection and/or analysis of data regarding bias/racial profiling. The
court noted that statistical evidence may be used to establish that other similarly situated
individuals, of a different race, were treated differently; however, to be admissible and of any
relevance to the issues before the court, such statistical evidence must be collected and analyzed
in a universally scientifically acceptable manner. Further, the court noted that the statistical
evidence must be subject to rigorous methodological procedures and evaluated by persons with
the academic credentials and practical experience to qualify as experts. The court specifically
27
noted the inherent problems with statistical evidence relating to bias/racial profiling with regard
to the following: establishing base lines, determining the quantity and quality of the data being
collected, sample groups, and interpretation. Accordingly, if the statistical analysis and findings
and conclusions of this, or any, study of bias/racial profiling are to be of any value from a legal
perspective, the study should comply with the evidentiary requirements currently being imposed
by the courts.
This study seems to satisfy the admissibility requirements for evidence relating to
disparate treatment based on race, currently being imposed by courts in bias/racial profiling
cases. This study employed sound methodological techniques with regard to the collection and
analysis of data and was performed by individuals with nationally recognized expertise in
statistical analysis.
Disclosure of Information/Records
Although generally not rising to the level of concern as civil liability, law enforcement
agencies engaged in the collection of information and analysis of data, whether related to bias
profiling or some other topic, must be familiar with the applicable statutes and/or ordinances
governing the release of public records. Typically referred to as "Open Records Acts", virtually
all jurisdictions have enacted laws requiring certain records in the possession of police agencies
to be released to the public. These "Open Records Acts" vary tremendously from jurisdiction to
jurisdiction; however, in all jurisdictions, to some degree, the data collected as part of a bias
profiling project will be subject to disclosure to the public, and to the media. Ideally, agencies
will address this legal issue before initiating any data collection to ensure they know, going into
the project, what records, if any, will be subject to disclosure, and under what circumstances.
28
The fundamental questions to be resolved relating to the release of data and information
collected as part of a bias profiling project are:
1) Who, exactly, is the custodian of the data and information relating to the project?
[This can become very complex in situations where agencies contract all, or part, of
the project out to a consultant.]
2) What records are, and are not, subject to disclosure?
3) Can any of the information collected be "masked" or otherwise shielded from
disclosure? Must any information be shielded from disclosure?
4) If large data sets are subject to disclosure, what format is required?
5) Where disclosure of large, bulky, data sets is required, what costs, if any, may be
recovered by the agency?
6) Is the analysislinterpretation of the data subject to disclosure also?
7) When must data/information be released? [This can pose difficulties in muhi-year,
on going, projects.]
8) How long must the data/information be retained and who had responsibility for
archiving the materials?
Conclusion
It is imperative that agencies practice proactive risk management with regard to the
collection and analysis of data relating to biastracial profiling. In addition to serving as the basis
for addressing a host of management, administration and personnel issues, bias/racial profiling
studies can also serve as useful tools for developing statistical evidence for defending against
lawsuits alleging civil rights violations. However, experts in statistical analysis must conduct
any study using scientifically acceptable methodology. The statistical analyses involved in this
study appear to satisfy the legal requirements currently being imposed by the courts and the
findings and recommendations should serve as valid evidence relating to allegations of
biastracial profiling. Finally, a determination should be ascertained as to what degree the
information/records will be subject to disclosure under the applicable Open Records laws.
29
Conclusion and Recommendations
The Iowa City Police Department, as a whole, does not appear to be systematically
stopping drivers based on their "racial or ethnic status or characteristics" as defined by
departmental policy (Racial Profiling, General Order 01-01). While the percentages of races
were not always equal in some categories, the discrepancies are most likely explained by factors
other than the driver's race. For example, the age and sex of the driver were important
explanatory factors in many events. This makes sense given that we know driving behavior to be
different among various ages and between the sexes; younger drivers drive differently than older
drivers and males drive differently than females.
This study used a fairly comprehensive set of data collected about a population of stops
over an 8-month period. The data were collected in a consistent manner, with only minor
problems pertaining to entry and recording that were addressed as they were discovered. The
statistical analysis used to evaluate the data was rigorous, thorough, and conducted by
academicians with expertise in the collection, analysis, and interpretation of such data. Further,
this analysis was conducted on a contractual basis with researchers from the University of
Louisville in Louisville, Kentucky, providing a level of objectivity that is necessary to avoid any
conflicts of interest or appearances of impropriety. These factors have yielded valid data,
making valid conclusions highly likely. The only caveat is that one full year's worth of data
should be collected and analyzed to provide a baseline from which to evaluate future stop
practices.
Moreover, the legal considerations set forth by the courts have been met, making legal
actions against the Iowa City Police Department based on accusations of "racial profiling" very
unlikely. However, the Department must still recognize that this does not preclude the actions of
30
any one officer becoming suspect. Our findings do not conclude that such profiling might not be
occurring against individual citizens by one or more individual officers. This type of
discrimination on an individual level, however, is virtually impossible to detect or to prove given
the type and amount of discretion that officers must use in the completion of their duties. These
matters are more likely to be discovered through administrative and supervisory vigilance, and
through community awareness, rather than through the collection and analysis of traffic stop
data.
The Iowa City Police Department can enhance their collection of traffic stop data The
recommendations offered here involve both process and content elements of the project. First, it
is suggested that a full year of data be collected and subsequently used as a baseline for
analyzing future department practices. The data in the study covers only 8 months of the year
2001 and may not fully reflect the traffic stops practices of the department on an annual basis.
Second, census population data should not be used as a baseline. As previously discussed,
census data does not provide for an appropriate point of comparison and should only be used
when nothing else is available. Clearly, with the adoption of the recommendation for a full year
of data collection, the use of census data can be avoided.
Third, data collected for the year 2001 (April -December) should be viewed carefully as
the department experienced considerable challenges in refining the data entry process.
Throughout the course of this project quality assurance checks were employed to ensure that the
data collected was valid although it is suggested that the validity of the data may continue to be
somewhat suspect. Continued monitoring of date entry and fine-tuning of the department's
quality assurance mechanisms, however, must be a priority. A fourth recommendation involves
the training of all officers in regard to departmental policy, data collection procedures, and the
31
results of the analysis. Officers collecting the data must have a thorough understanding of the
project in order to ensure more accurate and complete data collection and entry. In a similar
vein, supervisors must be proactive in ensuring line officers understand the policies and
procedures related to the project. Supervisors also should identify officers who require
additional training or closer supervision to ensure adequate understanding of the data entry
procedures as well as policy compliance.
Fifth, it is imperative that the department establish clear, written guidelines regarding the
entry of traffic stops into both the CAD and MDT database systems. These guidelines should be
made available to all personnel involved in the data entry process and should be incorporated
into departmental training as required. Further, dispatchers should receive guidelines and
training regarding recording calls when more than one officer in involved in a traffic stop. This
will allow for more timely and accurate quality assurance checks as well as enhance the validity
of the data
In terms of the content of the data collection forms, several data elements could be added
to the form First, in attempt to control for variations in traffic stop practices by location, the
quadrant in which the stop occurred could be added to the form allowing for traffic stop
identification. Also, the form should contain information about warrant checks. First, there
should be a question that asks whether a warrant check was performed during the stop.
Secondly, the form should contain a section addressing the outcome of the warrant check.
Currently, information about outstanding warrants is obtained through a plate and/or license
check. These types of checks, however, are not performed routinely. Finally, the form should
include an item that indicates whether the driver was asked to exit the vehicle. These additions
are consistent with data collection efforts throughout the country, require minor modifications to
32
the form, and would aid in the development of a more accurate understanding of the key events
that are likely to occur during traffic stops.
These recommendations are offered to improve the data collection process and to
enhance the quality of the data. Several of these recommendations were communicated to the
Department as the study progressed and have been addressed. Others are currently being
implemented. Overall, the departmental administration has been receptive to recommendations
for the improvement of their data collection and analysis, and seems genuinely concerned about
the accurate measurement of traffic stop practices. Again, the only major concern is that this
study is based on only 8 months of data with which some minor collection and entry problems
were noted Therefore, it is necessary that a full year's worth of "clean" data be collected and
analyzed to provide the best baseline from which to evaluate the future stop practices of the
department. Although no evidence of departmental discriminatory stop practices may be
welcome news, the department now is faced with the responsibility of continual monitoring to
maintain these practices for the continued benefit of both the department and the community.
33
BibGoL,mybv
MacDonald, H. (2000). The burden of bad ideas: How modem intellectuals misshape
our society. Chicago: Ivan IL Dee.
Newport, F. (1999). Racial profiling is seen as widespread, particularly among young
Black men. Srallup Poll. December 1999, #411, 18-23.
Ramirez, D., McDevitt, J. & Farrell, A. (2000). A resource guide on racial profiling data
collection systems: Promising practices and lessons learned.
Simms, J. (2000). The Maryland I-95 corridor study. University of Washington in
Missouri. (http://www.artsei.wustl.edu/—focus205/supreme/stats_i95.htmi).
Smith, M. & Petrocelli, P. (2000). Racial profiling: A multivariate analysis of police
traffic stop data.
Withrow, B.L. (2002). Race based policing: An initial analysis of the Wichita Stop
Study. Paper presented at the meeting of the Academy of Criminal Justice Sciences, Anaheim,
CA.
Zingraff, M. Warren, P., Tomaskovic-Devey, D. Smith, W., McMurray, H., Mason, M &
Fenlon, C. (2001). Evaluating North Carolina State Highway Patrol Data: Citations, warnings
and searches in 1998. North Carolina Department of Crime Control and Public Safety. (On-
line). Available: www.nccfinecontrol.org/shp/ncshreport.htm
9M
APPENDIX A
Iowa City Police Department Policy on Racial Profiling
General Order # 01-01
Section Code OPS-17
M
OPS-17.1
RACIAL
PROFILING
Date of Issue General Order Number
January 10 2001 01-01
Reevaluation Date Amends / Cancels
December 2001 1 New
C.A. L E.A. Reference
1.2.4,1.2.9,41.3.8,61.1.2.9
INDEX AS:
Racial Profiling Search and Seizure
Complaints Traffic Stops
Supervisor Responsibilities Arrests
Warrants Discipline
I. PURPOSE
The purpose of this order is to unequivocally state that racial and ethnic profiling by members of this
department in the discharge of their duties is totally unacceptable, to provide guidelines for officers to
prevent such occurrences, and to protect officers from unfounded accusations when they act within the
parameters of the law and departmental policy.
II. POLICY
It is the policy of the Iowa City Police Department to patrol in a proactive manner, to investigate
suspicious persons and circumstances, and to actively enforce the laws, while insisting that citizens will
only be detained when there exists reasonable suspicion (i.e. articulable objective facts) to believe they
have committed, are committing, or are about to commit an infraction of the law. Additionally, the
seizure and request for forfeiture of property shall be based solely on the facts of the case and without
regard to race, ethnicity or sex.
III. DEFINITIONS
Racial profiling - The detention, interdiction, exercise of discretion or use of authority against any person
on the basis of their racial or ethnic status or characteristics.
Reasonable suspicion - Suspicion that is more than a "mere hunch" or curiosity, but is based on a set of
articulable facts and circumstances that would warrant a person of reasonable caution to believe that an
infraction of the law has been committed, is about to be committed or is in the process of being
committed, by the person or persons under suspicion. ("Specific and articulable cause to reasonably
believe criminal activity is afoot.")
OPS-17.2
IV. PROCEDURES
The department's enforcement efforts will be directed toward assigning officers to those areas where
there is the highest likelihood that vehicle crashes will be reduced, complaints effectively responded to,
and/or crimes prevented through proactive patrol.
A. In the absence of a specific, credible report containing a physical description, a person's race,
ethnicity, or gender, or any combination of these shall not be a factor in determining probable cause
for an arrest or reasonable suspicion for a stop.
B. Motorists and pedestrians shall only be subjected to investigatory stops or brief detentions upon
reasonable suspicion.
C. Traffic enforcement shall be accompanied by consistent, ongoing supervisory oversight to ensure
that officers do not go beyond the parameters of reasonableness in conducting such activities.
1. Officers shall cause accurate statistical information to be recorded in accordance with
departmental guidelines.
2. The deliberate recording of any inaccurate information regarding a person stopped for
investigative or enforcement purposes is prohibited and a cause for disciplinary action, up to and
including dismissal.
D. Motorists and pedestrians shall only be subjected to investigatory stops or brief detentions upon
reasonable suspicion that they have committed, are committing, or are about to commit an infraction
of the law. Each time a motorist is stopped or detained, the officer shall radio to the dispatcher the
location of the stop, the description of the person detained, and the reason for the stop, and this
information shall be recorded.
E. If the police vehicle is equipped with a video camera, the video and sound shall be activated prior to
the stop to record the circumstances surrounding the stop, and shall remain activated until the
person is released.
F. No motorist, once cited or warned, shall be detained beyond the point where there exists no
reasonable suspicion of further criminal activity.
G. No person or vehicle shall be searched in the absence of a warrant, a legally recognized
exception to the warrant requirement as identified in General Order 00-01, Search and Seizure,
or the person's voluntary consent.
1. In each case where a search is conducted, information shall be recorded, including the legal
basis for the search, and the results thereof.
2. A cursory "sniff' of the exterior of a vehicle stopped for a traffic violation by a police canine
may be recorded on the department's canine action report form.
TRAINING
Officers shall receive initial and ongoing training in proactive enforcement tactics, including
training in officer safety, courtesy, cultural diversity, the laws governing search and seizure,
and interpersonal communications skills.
1. Training programs will emphasize the need to respect the rights of all citizens to be free
from unreasonable government intrusion or police action.
COMPLAINTS OF RACIAUETHNIC PROFILING
Any person may file a complaint with the department if they feel they have been stopped or
searched based on racial, ethnic, or gender -based profiling. No person shall be discouraged
OPS47.3
or intimidated from filing such a complaint, or discriminated against because they have filed
such a complaint.
1. Any member of the department contacted by a person, who wishes to file such a
complaint shall refer the complainant to a Watch Supervisor who shall provide them with
a departmental or PCRB complaint form. The supervisor shall provide information on
how to complete the departmental complaint forth and shall record the complainants
name, address and telephone number.
2. Any supervisor receiving a departmental complaint form regarding racial/ethnic profiling,
shall forward it to the Commanding Officer Field Operations and all such complaints
shall be reviewed and the complaint acknowledged in writing. The complainant shall be
informed of the results of the departments review within a reasonable period of time.
The report and the reviewers conclusion shall be filed with the Chief of Police, and shall
contain findings and any recommendations for disciplinary action or changes in policy,
training, or tactics.
3. Supervisors shall review profiling complaints, as well as periodically review a sample of
in -car videotapes of stops of officers under their command. Additionally, supervisors
shall review reports relating to stops by officers under their command, and respond at
random to back officers on vehicle stops.
4. Supervisors shall take appropriate action whenever it appears that this policy is being
violated.
REVIEW
1. On an annual basis or as requested by the Chief of Police, the Commanding Officer
Administrative Services, shall provide reports to the Chief of Police with a summary of
the sex, race, and/or ethnicity of persons stopped.
2. If it reasonably appears that the number of self -initiated traffic contacts by officers has
unduly resulted in disproportionate contacts with members of a racial or ethnic minority,
a determination shall be made as to whether such disproportionality appears department
wide, or is related to a specific unit, section, or individual. The commander of the
affected unit, section, or officer shall provide written notice to the Chief of Police of any
reasons or grounds for the disproportionate rate of contacts.
3. Upon review of the written notice, the Chief of Police may direct additional training
towards the affected units/sections or to individual officers.
4. On an annual basis, the department may make public a statistical summary of the race,
ethnicity, and sex of persons stopped for traffic violations.
5. On an annual basis, the department may make public a statistical summary of all
profiling complaints for the year, including the findings as to whether they were
sustained, not sustained, or exonerated.
6. If evidence supports a finding of a continued ongoing pattern of racial or ethnic profiling,
the Chief of Police may institute disciplinary action up to and including termination of
employment of any involved individual officer(s) and/or their supervisors.
R, J. Winkelhake, Chief of Police
WARNING
OPS47.4
This directive is for departmental use only and does not apply in any criminal or civil proceeding. The
department policy should not be construed as a creation of a higher legal standard of safety or care in an
evidentiary sense with respect to third -party claims. Violations of this directive will only form the basis
for departmental administrative sanctions.
APPENDIX B
Iowa City Police Contact Sheet
gm
IOWA CITY POLICE CONTACT SHEET
Time
r hour minute
0000
1 1 1 1
2 2 2 2
tj 3 3 3
4 4 4
5 5 5
6 6
7 7
8 8
9 9
Bade
0 0
1 1
2 2
3 3
44
5 5
6 6
7 7
8 8
9 9
A e
0 0
1 1
2 2
3 3
44
5 5
6 6
7 7
8 8
9 9
Driver Into
Male
Female
Unknown
Resident
Iowa City
Johnson County
Other County
Out of State
Other
Vehicle Registration
Iowa
eNon-Iowa
rDaW,ofConOtact
Consent Search
Requested?
BYes BVehicie
No Person
HHURN
Race/Ethnicity
Caucasian
Black/Negro/African
AsiaNPacific
Spanish/LabnotHispanic
Native American
Other
Unknown
American
IslanderConsent
Indian
Kill
EJE113
T
pe of Search
Officer Safety
Incident to Amest
Probable Cause
Reason
for Contact?
Moving Violation
Equipment or Registation Violation
Criminal Offense
OtherYalation
for ServiceSuspect Desc.Nehicle Desc.
Pre-e)assting knowledge or information
Spekiaf Detail
Other
Property Seized
None
Akrohol
Weapons
CurrencyCall
Narcotics
Evidence
Other
Use of Force?
None
Driver
Passenger
Outcome
No Action
Citation
Arrest
Warning
Feld Interview
Comments
K u add an
comments to the area listed
below, u must
darken the circle to the left.
Maio-01
00�0�0
0000�0
000aoo
000000
IOWA CITY POLICE CONTACT SHEET
Date of Contact Time
Badge
AQe
Driver Info
Resident
Vehicle Registration
Male
Iowa City
Iowa
m
Female
Johnson County
BNon-Iowa
month
day_ year hour minute
Unknown
Other County
Consent Search
0 0 0 0 0 0
0 0
Out of State
B
000�0
ICPD TRAFFIC STOP PRACTICES
Lehman/ Mr. R.J. Sir,
R.J. Winkelhake/ We're sorry to keep you here this late.
Lehman/ You aren't keeping us. We have kept you and we apologize for that, but this is the
people's republic.
Winkelhake/ I live here. I wouldn't mind staying.
Lehman/ You take a month off too many things on the agenda.
Champion/ But we've had a month off.
Lehman/ Okay.
This represents only a reasonable accurate transcription of the Iowa City Council Meeting of
Augusf 14, 2002
August 19, 2002 Special Work Session Page 102
Winkelhake/ What you're going to hear tonight is a report about traffic stop practices for the
Iowa City Police Department. Just give you a little bit of reminder we started doing data
collection quite a number of years ago. We were the only police department in the State
that was doing that. We began doing this particular type of data collection. We're the
only police department in the State doing it. The reason we started doing it was because
we wanted to know how we were interacting within our traffic stops and the results of
those. We had the opportunity to send the Supervisors to an extension administrative
officer's course at the University of Louisville and Lieutenant Jackson who is in the back
here when we was there he was asked to do a research project which I get to pick the
topic and it was profiling. And he did quite an extensive research paper on that. I don't
know if you've ever seen that or not.
Vanderhoef/ I have.
Winkelbake/ However, he made a lot of recommendations in there and the recommendations that
were made were also some that we got from different national symposiums where the
same kind of recommendations for the data to be collected and the way to collect it. So
that was done. We choose to send data from April I" thru the December 3 1 " of 2001 to
the University of Louisville to be able to do a traffic analyzation of what we were doing.
And what you have is a report — I think you got it about a week and a half ago — and we
have Dr. Angela West from the University of Louisville here tonight to give you the
report on the data that we have collected. And when she's done with it and you're done
with answers I got just a couple little things that we're going to continue on to leave you
know about after you're done with the report. So this is Dr. Angela West.
Lehman/ Thank you.
Angela West/ Well thank you for having me here. I'm having a little trouble with my voice this
evening so I'm going to try to be brief. What I'm going to do is basically just kind of
give you an overview of what we've done and you have the report and hopefully you've
read the report and have had access to the report. This is just a summary really of what
the full report says. We looked at —because I anticipate several questions so I'm just
going to fly thru this — we looked at 38 variables — driver demographics, stop
information, officer badge number and we can also put more information in later on the
individual officer as that need arises based on the badge number we can obviously get the
race, the sex, the age of the officer time and service and that type of thing if that becomes
a need of the Department. We looked at two different types of analysis. We had
descriptive analysis which is basically just percentages. Everybody's familiar with
percentages. That's a very superficial look at what's going on. It's only used to describe
events and representations. We also looked at multi -varied analysis to try to provide
inferential ability to look in the why. Why things are happening. Why things are the way
they are. And also predictive abilities to help us predict future outcomes based on what
we see currently, These types of analysis also help us to understand relationships and
interactions among all the various events that occur during a stop. A stop does not
happen in a vacuum. When someone is stopped there's several different things going on
at the same time that impact what happens... that may impact the actual stop itself— the
weather, the location, any events that are going on in the community, the officer's mood
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August 19, 2002
August 19, 2002 Special Work Session Page 103
at the time even though... and the reason that happens is because officer's are given a
great deal of discretion primarily because they can't fully enforce every single law. So
they have to pick and choose the most severe infractions. Excuse me. So and along with
the officer and the time of day and the environment and the weather there's driver
characteristics and auto characteristics that are going on at the same time. And the type
of multi -vaned analysis that we used is called CHAID or Chi -Square Automatic
Interaction Detector and I attached a printout from one of those processes looking at the
variable citation whether or not a citation was issues in the stop. And I'll get to that in
just a second, but that's just a printout form the CHAID. And what the CHAID analysis
does is it looks at each point, each decision point... now it can't look at the initial decision
point because that's something that we will never know exactly why the officer stopped
the person initially. We have the reason they put on the form, but they may, in some
cases, have put that reason just to put down a reason. So the initial traffic stop we will
never know whether an officer is discriminating on that very first contact because we
have what the officer puts on the form so we cannot analyze the reason for the initial stop
as far as whether there's racial discrimination going on there. So we look at each
decision point after that and the reason that the officer gives for the stop — whether it was
a moving violation or an equipment registration violation. And what CHAID does is it
looks at each of those decision points and throws that decision point into a big pot with
all the other things that are going on in that stop at that time — the demographics of the
driver, the age, sex, race of the driver and any other events and characteristics that we can
say might predict any outcome. And it results in the decision tree which you can see why
it's called the decision tree on your printout there arranged in a tree -shaped format.
Excuse me. And that tree orders these predictors in order of their strength or their
importance in predicting whatever event we're looking at. In this case it's a citation.
Okay. Excuse me. We looked at five outcomes of interest, the reason for the stop that
the officer gave, moving violation, equipment registration violation were the two most
prevalent, whether there was a search conducted, the type of search if there was a search
conducted, property seized, and the outcome of the stop. And there were three potential
outcomes/ warning, citation, or arrest. Our results — you've read those — basically
nothing predicted equipment registration violation. There were no significant factors that
came to the surface when you threw all that stuff into a pot. Age was the most significant
predictor of receiving a moving violation, but it had significant interactions with sex and
residency. So... Excuse me. The base rate for moving violations was 68.6% meaning
that out of all the stops 68.6% were because of a moving violation. So what the CHAID
does is it takes that base rate — 68.6% - and compares the rate for other groups in other
situations to that base rate. It should be fairly similar okay across characteristics. It was
not in certain cases. Most likely to be stopped for equipment... or moving violation were
those over 40 with non -Iowa registrations. So they had a rate of 84.5%. So 84.5% of all
the people over 40 with non -Iowa registrations received a stop for a moving violation
compared to that 68.6% it's significantly higher. That's why the CHAID says it's a
predictor. Okay? The next most likely were those under 18 who were female — almost
82% of those were stopped. Whether there was a search conducted had no significant
predictors. The type of search conducted there were no significant predictors. Whether
there was property seized there were no significant predictors meaning that no particular
group or characteristic was higher than the base rate for the entire overall group. For
This represents only a reasonable accurate transcription of the Iowa City Council Meeting of
August 19, 2002
August 19, 2002 Special Work Session Page 104
outcome of stop there were no factors that predicted arrest, but receiving a warning and
receiving a citation did have significant predictors. Receiving a warning ... you were
more likely to get a warning if you were not searched which kind of makes sense from a
practical standpoint. You're not going to get a warning if you're searched hopefully.
And equipment registration violation. So most likely to be warned were those who were
not searched who had equipment registration violations and who had non -Iowa
registrations and that might be an out-of-towner phenomenon. We're going to give you a
warning sense you're out of town, sense you're from out of town. Thank you. Whether a
driver was stopped for equipment registration violation was the most significant predictor
of getting a ticket or a citation. Again age and residency came up as related to that. The
base rate was 38.7%. Those most likely to receive a citation were those not stopped for
an equipment registration violation. So you go again with you're not going to receive a
ticket just for equipment registration violation. Who are over 30 years old and who had
Iowa registrations. Excuse me. Race was never the factor that was the most influential in
any of the outcomes of the stop. Excuse me again. The next page outlines what we call
the baseline dilemma. And what that refers to is that in prior studies of this issue the
tendency is for the percentage of stopped drivers — the racial distribution of stopped
drivers to be compared to the racial distribution in the community. That's the tendency.
There are several problems with that that I outline here and I'll get to that in just a
second. But the whole issue revolves around comparing what is to what should be and
that's a problem. To determine what should be one has to get a measure of the racial
distribution of drivers who are doing something that would make them eligible to be
stopped. You have to know who's out there driving in a way that will make them eligible
to be stopped. And I call these people the violators. And your proportion — you're racial
distribution of stops should mirror the racial distribution of people doing something
wrong. Does that make sense? So that's what the baseline should be. Actually
measuring that is... nobody's been able to do that yet - to find out who's driving in a way
that will make them eligible to be stopped. There've been attempts to measure this, but
mostly that consists of posting observers either by the side of the road or driving on the
road and counting the number of speeders going by and trying to document their race.
Okay? So that's the way it's been tried... attempts have been made to measure that. One
of the biggest problems I see with that is speeding is the only behavior that they're
looking at in those types of studies. Well speeding is a minority reason for a stop. Okay?
We did a study in Louisville, Kentucky and only 37% of all the stops were for speeding.
So you're missing 63% of the reasons why somebody might be stopped just sitting there
looking at speeders going by. And not to mention the. difficulty of measuring races of
drivers as they're driving past — speeding past. Excuse me. Comparisons to the
population — the census data — are invalid for the reasons that are outlined here. Census
figures include the entire population. The population of drivers to be stopped is generally
over the age of 15. Driving populations and police stop practices fluctuate depending on
several factors. It ignores the fact that a significant proportion of drivers stopped are not
city residents. That's my biggest point. It's hard to compare a population is a city
when ... with the population of driver stopped when 38% of the drivers stopped aren't
from the city. And there's also no theory to back the belief that the population of drivers
stopped should reflect any resident population. Any there's no theory to back the belief
that driving characteristics or events should be equally distributed among populations.
This represents only a reasonable accurate transcription of the Iowa City Council Meeting of
August 19, 2002
August 19, 2002 Special Work Session Page 105
Different groups can have different driving patterns and behaviors. That's why young,
male insurance rates are a lot higher than anyone else because we know younger people
drive differently and males drive differently. Excuse me. And the conclusions and
recommendations we found no evidence in the data that we had that there was systematic
discriminatory stop practices. Again in the events that happen after the initial stop and in
the reasons that the officers gave for the stop. Again this does not preclude the
possibility that an individual officer could be individually discriminating, but that's
another thing that's almost impossible to measure. You'd have to know exactly what
things were going on in the officer's mind in any situation. The best way to do this type
of thing is to use this type of information along with complaints from citizens and that's
one of the biggest measures of what police are doing wrong or right is to look at have
there been complaints that I was stopped for an unnecessary purpose. And also use of
force reports to go along with this. The age and the sex of the drivers as I just mentioned
young or males typically have the riskiest driving behaviors. And those were the two
things that were most predictive of stop outcomes. We've made recommendations to
ICPD and Chief Winkelhake is taking those to heart and there's been an on -going
renovation process, revision process to the data collection and to improve the quality of
the data. Any questions?
Champion/ You brought up a ... I just want to ask (can't hear) or not? The percentage of out-of-
towners that were stopped was higher than people with in -town registration, but did you
take account of the fact that we have thousands of students here with out of town cars. Is
that considered at all or that just random?
West/ That would come into play in the analysis. Yes.
Lehman/ I think you said 38% were non-residents were they actually non-residents or people
with out of town plates?
West/ Well let's see. We did both looking at vehicle registration Iowa versus non -Iowa and
then looking at city, county other county within Iowa.
Lehman/ So if I were driving a car with an Illinois plate and I was a student at the University of
Iowa I would be considered a resident?
West/ No.
Champion/ No. You'd be an out-of-towner.
Lehman/ So the 38% many of those could be residents of Iowa City who are here to go to the
University.
Kanner/ It doesn't look at address?
West/ But they're really not counted in the census population for the City are they?
Kanner/ Yes they are.
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August 19, 2002
August 19, 2002 Special Work Session Page 106
Lehman/ Well they are, but they would also certainly included in any measure of profiling
because they are residents of Iowa City even though they may not be permanent resident
— they live here.
West/ Okay. That's one reason that we look at vehicle registration — Iowa versus non -Iowa and
then looked at city resident versus county resident versus ... there were two measures of
residency that we used.
Lehman/ That probably would have skewed it anyway.
Vanderhoef/ And you were talking about, if I'm reading this correctly, over — I'm presuming —
40 when you say that you're talking age.
West/ Yes.
Vanderhoef/ So that is not our typical student population.
West/ Right.
Vanderhoef/ In the age 40 part of it.
Lehman/ No.
Vanderhoef/ I have been thinking the same thing that you were Connie about our out-of-town
registrations that the students...
Champion/ I think some of my kids have been in school long enough to be 40.
Vanderhoef/ Going back for the second time.
Lehman/ Questions? Comments?
O'Donnell/ I think it's very good.
Wilburn/ Can you comment in general just — I realize we're one of the few if not the only
departments in Iowa doing... collecting this information — but from what you've seen
with some other parts of the country can you compare (can't hear). We don't have or we
haven't ... this is our first go at this. We haven't standardized tested the reliability of the
instrument, but can you just in general comment on information that may have appeared
on other...
West/ On the data collection instrument?
Wilburn/ Yeah on the instrument itself. On other areas that they may have included.
West/ Yes. Well we came in after the fact on this data collection form.
Wilburn/ Right.
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August 19, 2002
August 19, 2002 Special Work Session Page 107
West/ But it's fairly comprehensive compared to prior studies into current studies. Many, many
studies only include the basic demographic information of the driver, some officer
information, and maybe the time and location of the stop and the date that type of thing.
This has several other factors on here.
Wilburn/ Is there other information that you might include?
West/ Yes. One of the recommendations that we made was to include information on some
other events that happened. Of course that's a departmental issue as far as what the need
is. For example some other departments ask whether a driver was asked to exit the
vehicle. And that might be an indication of discrimination is minority drivers are asked
to exit more frequently than white drivers are. Or whether or not a warrant check was
requested or conducted on the driver — that's also another one that other departments do.
Wilburn/ And have you had a chance a conversation with the Department here about perhaps
including those types of things or the benefit? Have you had those conversations yet?
West/ Yes. We've made recommendations.
Wilburn/ Okay.
Vanderhoef/ And have you looked at these in terms of what standard operation procedures are -
whether all drivers are asked to get out of the car for instance? To put that into the data?
West/ Right. Right. That's a ... that's a very specific thing to the department that's doing the
study. For example in Louisville we just finished a whole year of their data analysis.
One of their standard procedures that their policy is to do is to conduct a warrant search
on everybody they pullover just as a matter of practice. That's not standard procedure in
every department. But then we found the rate of doing that was not 100% as the chief
would have liked. It was only around 86% which is still fairly high, but not consistent
enough.
Wilburn/ I'm glad you pointed out the limitation in the fact that we given what we're doing and
the way that we're going about it we cannot know ... we can't get into the mind of an
individual officer. And I've always been of the belief that one would hope not but any
racism stereotypes in the general population, in my opinion, there's no reason to believe
that they may not be, you know, close to or similar to what's on any city department and,
you know, some folks that I've talked to about the issue and just with racism in general
it's good to know that there's no systemic, you know, given the limitations or the
constraints that we're aware of in the department. There may be anecdotal — and there
will be anecdotal information.
West/ Yes.
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August 19, 2002
August 19, 2002 Special Work Session Page 108
Wilburn/ But with folks that I've talked to their concern it's, you know, we know that you can't
get some of those ideas or thoughts out of people's heads, but what you do with that is a
different story. And you can believe what you want to believe, but you're not going to
comb at any act against because of your racist, sexist, etc., etc. beliefs and that's why it's
important to have ... I look at this and I think it's important that we continue to take a look
at this because just with our participation in this for any individual officers that may, you
know, may use some type of profile whatever their motivation for stopping someone if
it's something other than legitimate, you know, police business that's it's being watched
and they need to explain their stops, their behavior. I look at it as a tool.
West/ Exactly.
Wilburn/ And a piece along with what we have with our, you know, our other complaint process
- our Human Rights Commission complaint process. I don't know if there's ... I don't
know if we have... if our vehicles have video on them. Okay so that's another piece that
adds to the puzzle so I look at this as another bit of information in terms of everything
that we're doing.
West/ And it's meant to be used as a tool — an administrative tool.
Lehman/ Since you're negotiating a second contract for a full year of data with Iowa City...
West/ Yes.
Lehman/ ...are we going to be a little ... is it possible to get better or more sophisticated data than
we have now?
West/ Yes. They've already made some changes I believe to the ... maybe Chief Winkelhake
can speak more to that as to what they have done.
Pfab/ I have a comment and kind of...
Lehman/ Well let R.J. answer first for the data question.
Winkelhake/ (Can't hear) what we're doing.
Lehman/ Microphone please.
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August 19, 2002 Special Work Session Page 109
Winkelhake/ The question about what we're doing. At one point we tried to correlate between
traffic stops and the data we were collecting we found like an 89% correlation rate and
that's not good enough. We've taken some steps after talking with the people at
Louisville and Lieutenant Jackson particularly is reviewing all that data and we're
running now at about a 99% to sometimes 100% correlation. And we found a number of
glitches that were computer generated. For instance if a community service officer went
to a traffic stop to stand by while the car is going to get towed, it would show that that
stop was made by the community service officer which is not the policy of the Police
Department. Lieutenant Jackson's been working with the people that take care of the
computers to make sure that doesn't happen. So our correlation has gone from 89
something to very close to 100 and that's month after month after month. And the
process is going to continue. If I could just one thing that she talked about the warrant
checks for instance in Louisville they ... it was a policy to go ahead and do that. The way
the computer is set up for the State of Iowa anytime an officer runs a license plate or runs
your driver's license it comes back whether there is any warrants on you. So that's an
automatic. So we never have taken it as an issue to put into policy to check everybody
for a warrant because you could do either one of those you're going to get it back
automatically. So that's always being done.
Pfab/ One of the points that I think Ross related to, but there's a possibly another side to that
that is does the officer out there when he knows he's entering his data entry person does
it ever cross his mind that I've stopped a lot of minorities here and maybe I should lay
off. Is that ever...?
West/ I'm not sure I'm ... it may, it may not.
Pfab/ So it might work against minorities too if they know they're being tracked just to say
well.
West/ Generally when a behavior like that is being studied when any kind of study is imposed
on a law enforcement agency there's an initial adjustment period right after it goes into
effect but then people revert back to their normal behaviors.
Pfab/ Okay. That's a good point.
Winkelhake/ As soon as we started saying we're going to start collecting it obviously that puts a
flag up say somebody's looking at this. We did take a look at the number of traffic stops
that were made last year. We took the eight month data the April till the end. We
decided how many ... on average how many and then multiplied it by 12 to get a number
and it came out 14,212 and we did the same thing with the data we've collected so far
year to date and projected that out to the end of December and that number comes out to
14, 050. So we're very close. There isn't really anybody playing games that we can see
just from that number.
Kanner/ Dr. West thanks for coming up here. What's your recommendation on having a peer
review of this study that you've done?
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August 19, 2002 Special Work Session Page 110
West/ Excuse me.
Kanner/ Especially you've been hired by a department and is there a common accepted practice
in the statistical field, scientific field to have in this situation especially to have a peer
review of your results?
West/ Well we were contracted to do this study by the Police Department. It never was meant
to be a research project that was publishable. Of course when we go...
Kanner/ I say peer review in a more general sense since I lack of a better word. But you get my
sense of having someone else hired. Do you recommend that someone else be hired to
look over the data to do a second review to see if they come up with the same results?
West/ No. What normally is done is you do the review on the front end to make sure you're
doing the right things in the first place so that that's not necessary on the back end. So
what we did was reviewed every shop practice, publication to the ... at the point at which
we started doing this type of work collected a big library full of publications and studies
from across the country and reviewed their practices, reviewed their data collection
forms, reviewed the government publications from the National Institute of Justice
recommendations and created our data collection forms when we had the input to do that.
We didn't have as much input in this situation in the data collection form, but as far as
practices go. And then we've, of course, improved on the methodology that had been
used in prior studies.
Kanner/ We got some information tonight — I'll give you a copy of this from Dr. Baldus. I don't
know if you have a chance to take a look at that. But there ... it did bring some concern to
my mind about you mentioned how hard it was to compare the no stops and.., but he's of
the opinion, I believe, that it is doable. That it is essential actually to make that
comparison to the best of our ability. And I would ask the Council that we give Dr.
Professor Baldus a chance tonight and if not tonight then another time set aside to
respond to this. We've gotten some written information.
West/ Did you want me to answer that question?
Kanner/ Yeah I do, but I also just wanted to continue with the Council just a second.
West/ Okay.
Kanner/ If ... since I need some help interpreting this I think it would be good to have Dr.
Baldus ... Professor Baldus perhaps tonight respond to some of this since he has gone to a
lot of effort. He has some expertise in this area.
Lehman/ Would you ... I mean you've received a copy of this. Would you look at it at your
leisure look this over and give us a response to it as well.
West/ Sure. Absolutely.
Kanner/ Anyone else like to hear from Professor Baldus tonight?
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August 19, 2002 Special Work Session Page I I I
Lehman/ I think the time is such tonight that it probably... it's almost 10/30.
Pfab/ Is it possible that maybe ... that maybe tomorrow night?
Lehman/ Certainly during public discussion or if we wish to have it... if the Council wish to have
it ... I would like to get your response to this and then make a decision on that.
West/ Would you like a written response?
Lehman/ Read it over and see what you think.
West/ Okay.
Kanner/ Yeah. Personally I don't know what the Council majority feels I would like a written
response. I think that would be helpful.
Champion/ Yeah.
Lehman/ Obviously this is something you understand far better than we and so if you would I
would appreciate that for the rest of the Council.
Kanner/ And if you could respond I'd appreciate it. Another question I had that was raised by
Professor Baldus is regards to people given, I think they were stopped and warning and
outcome warning and correlation with race on that.
West/ To what are you referring?
Kanner/ I think he might have it in a different form and again it would be better if he could
address it to you directly so we could see a little bit of question and answer.
West/ Is that addressed in his?
Kanner/ Yeah. I guess you have it.
West/ Then I shall...
Kanner/ Figure 1.
West/ ...address that in my written response tomorrow if that's...
Lehman/ It doesn't have to be that quick, but...
West/ Oh, okay I meant for your meeting tomorrow.
Lehman/ This isn't on our agenda tomorrow.
Champion/ No.
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August 19, 2002 Special Work Session Page 112
Lehman/ We're receiving the report. We appreciate your being here to go through it with us, but
I do think ... I have not read Professor Baldus' comments. I will take them home. My
sense is that you will understand them a lot better than we are.
West/ Okay. Sure.
Lehman/ So.
Pfab/ A possibility since it's not on the agenda tomorrow Professor Baldus could have five
minutes if he wants...
O'Donnell/ Ernie we're not going to get into a debate tonight about this Irvin.
Pfab/ No, no I said tomorrow.
O'Donnell/ I'd like to here her response...
West/ I'd like to make a comment to the question that you asked about the people who are not
stopped. I don't know how you measure that. And if someone would come up with a
way to measure the characteristics of the people who are not stopped I'd be more than
happy to do the analysis.
Lehman/ They're quicker.
West/ Apparently.
Lehman/ Okay. Any other questions.
Wilburn/ Just a couple comments in terms of that and we'll certainly hear comments on the
results of your study as we will from anyone and anyone with a research background can
look. One way off the surface is to observe the officers to have someone observe the
officers and you know that depends on how much money and resources you want to put
into that. But, you know, the other point about the peer review thing your point was well
taken about whether or not you're looking at publishing an article something researched
but the methodology is spelled out in the report if someone wants to take a look at it it's
apparently has happened or is going to happen they can send us a response as to want or
come to get their five minutes before us to respond.
West/ Right. One thing that when you send something off for publication consideration or in
this case it's a report for an agency that is not as in-depth as far as the statistical
procedures and numbers and results are. Had it been for publication consideration we
would have included a lot more in-depth on the statistical outcomes and probabilities and
chi-square values and degrees of freedom and all those kinds of things. But it was for the
use of the department, not necessarily for critique for that reason.
Champion/ R.J. was there any surprises in the outcomes? Did anything surprise you with the
percentages?
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August 19, 2002 Special Work Session Page 113
Winkelhake/ The thing that surprised me most was the age - that 41 the likelihood of getting a
ticket. I really didn't have any preconceived notions of what this was going to tell us
other than we started a long time ago. I personally didn't think that we were stopping
people because of race. It's nice to have this to be able to say there's some
documentation about what we do. One of the things you talked about was the Iowa plates
versus non -Iowa and we also looked at other Iowa. So you'd also have Iowa Johnson
County, other counties, and then out of state. And we did that mainly because of student
population. And a lot of the visitors with out of state plates that come in here. There was
also another thing that we put on the form was about the whether use of force — whether
there was any force that was used and whether it was the driver or the passenger. That
was something we wanted to know and we put it in there that way. So there were some
things that were done simply because we wanted to know what we were doing and get a
better handle. When I talk to my colleagues across the State and they say why in the
world are you doing this and my answer is at least why I can tell you what we're doing.
I'm not too sure you can tell us what you're doing. All the time no matter what the data
is going to be you're going to have people that are going to disagree with it. And that
was one of the reasons we choose to find an organization outside of our State.
Wilburn/ And I think the other piece to add to that is to people do draw different conclusions
about your methodology and your findings. It's what you do with that and we have other
— again we're continuing to look at this. We have other pieces of data we collect whether
it's looking at complaints filed against individual officers and/or video account and so it's
pieces of the puzzle that we need to have to try and prevent it from happening.
Winkelhake/ One of the things we're continuing to use (can't hear) we do look at use of forth.
We do look at citizen complaints. But what we're going to do is continue to gather data.
We will have it analyzed again. We are going to continue to look at it. We're going to
do more so we can get into individual officer's rather than just the Department as a whole
and start looking at it from that standpoint. We're fairly well satisfied at this point that
the data we're gathering is good data. I think the people who are doing the work agree
the data we're collecting now is better than was done before.
(End of Side 1, Tape 02-67, Beginning of Side 2)
Winkelhake/ ...report you see a sheet. Well that is actually on a computer, you know a laptop in
the car. And the officers can do this very quickly in the car now. And we did make some
adjustments to that. We include a five digit number from the dispatch center so we that
coalition coming almost 100%. Ross you had made reference to whether or not there are
videos in the car, there are. So we have the videos in the car, we have citizen complaints,
we have this data, we have a number of things that we can look at. What we are viewing
it as is kinda of an early warning system to see once what we are doing so that we have
some idea of how we have to respond to changing attitudes if that's necessary. And we
will be doing this, its going to continue. We're looking to getting a contract signed very
shortly and possibly in a year come back to you.
Wilburn/ Great.
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August 19, 2002
August 19, 2002 Special Work Session Page 114
Vanderhoef/ Then you would make an assessment, I would hope, that this is a good use of public
monies and does not create questions in the community that are not valid?
Winkelhake/ I think it's a good use of money simply because of the fact that I think it is a good
thing to do. I think it is something we should be doing. You ask some other Police
Chiefs and Sheriffs across the state they're going to tell you its probably not a good use.
Any time you have data there's going to be different interpretations of it. The one thing I
can do is say "Here this what we do". You're going to interpret it and you may have seven
different opinions right here but least wise I can say this is what we do.
Wilburn/ Well not just the data itself but the methodology used. If someone wants to say what
you're doing is flawed...
Lehman/ So be it.
Wilburn/ and then we can you're right or no we disagree.
Dr. West/ I've heard that a lot.
Wilburn/ Thank you.
Lehman/ Thank you.
Vanderhoef/ Thank you.
This represents only a reasonable accurate transcription of the Iowa City Council Meeting of
August 19, 2002
3 J(33)
August 18, 2002
City Council of Iowa City 2: 5 3
410 E. Washington St.
17
Iowa City, Iowa i � ;- i . ,,A ,y�
52240
Re: Terry Edwards et al. Traffic Stop Practices of the Iowa City Police Department:
April- December 31, 2001 (June 13, 2002)
Dear City Council Members:
The recently released study -- Terry Edwards et al. Traffic Stop Practices of the
Iowa City Police Department: April- December 31, 2001 (June 13, 2002) (hereinafter the
"study") — is of interest to us professionally because a primary area of our research is the
use of statistical evidence to test the validity of claims of discrimination in the
administration of the criminal law. Our resumes, which document our experience in this
area, are attached.' We routinely peer review for scholarly journals empirical studies that
are comparable to the Edwards et al. study of Iowa City police stops.
On the basis of its findings, the study states that the "data provide no empirical
evidence that the ICPD is systematically engaging in discriminatory stop practices." (p.l)
The study implies that it has affirmatively established that there is, in fact, no racial
discrimination in Iowa City police stop practices. In addition, the study asserts that for
the purposes of future research, the racial distribution of auto stop data presented in the
report should "become the baseline from which to evaluate future practices" (p. 25).
It may well be that there is no systemic discrimination in Iowa City's auto stop
practices and we hope that this is the case. However, the study fails to establish that fact
and is incapable of answering the question one way or another. Obviously our critique of
the study is merely the opinion of two people. One way to resolve the esoteric
methodological issues that our critique raises is to get other opinions. We strongly
recommend that you send this report out for peer review by other scholars working in this
field?
A. Two Decision Points
In considering what the study does and does not prove, its is useful to distinguish
between two parts of what the study refers to as "stop practices." They are shown in
Figure 1 attached to this letter. The first part, shown in Part I of Figure 1 is the decision
'Professor Baldus' publications are on page 1-3 of his resume and Professor Woodworth's publications are
on pages 3-9 of his resume.
z On a related issue, if the City supports more research on this issue, we suggest that it follow standard
practice on such matters and issue a request for proposals (RFP) and invite competitive bids from interested
researchers. Those proposals can then be peer reviewed before a contract to do the work is awarded by the
City.
to stop a motorist. The second part, which is shown in Part II of Figure , consists of a
series of decisions made after the stop is executed. r , 1 - q (>;.1 2; 53
B. The Decision to Stop CI
The decision to stop is the principal decision of interest. Tb te'st the extent to
which race may be a systemic factor in the exercise of officer discretion to stop motorists,
one would ideally have information on the racial characteristics of the people who were
not stopped. This would enable us to compare the racial composition of those stopped
with those who were not stopped.3
When a database contains no information on the persons who were not stopped, as
is almost always the case, researchers look for a proxy population that will enable them
"to identify the racial distribution of stops that would exist in the absence of
discriminatory stop practices" (p.24). With such a population, one can compare, for
example, the percentage of blacks among those stopped with the percentage of those who
would have been stopped in the absence of discriminatory stop practices. The issue is
whether and how this proxy population should be identified. The authors of the study
(hereinafter the "authors") are of the opinion that the identification of this population is
"extremely, if not impossible, to measure" (p. 24) and that a "comparison to population
data is invalid" (p.25).
Instead the study asks whether, among the drivers who were stopped, there is an
association between the driver's race and whether they were stopped for a moving
violation. The study concludes that there is no evidence of "racial bias in drivers being
stopped for a moving violation"(p. 21) 4 This is the sum and substance of the
"multivariate" results bearing on the stop issue. The results fail to discount the
possibility of racial discrimination in the stops. They do not even address the question of
whether among all the persons stopped the proportion of minorities is higher or lower
than the proportion of minorities among all the drivers who could have been stopped but
were not. In short, the analysis is irrelevant to the issue of racial bias in the decision to
stop a motorist.
We agree that the identification of a comparison population raises a number of
interpretive issues and that such results must be viewed with caution. However, the use
of comparison populations is essential and unavoidable in this kind of research.
Researchers routinely compare the racial distribution of the stopped drivers with the
racial distribution of comparison populations and compute the disparities. For example,
in 1998, we conducted such an analysis of the Iowa City stop data that were available at
that time. Specifically, we compared the racial distribution of Iowa City and Johnson
3 Note that with respect to the post -stop decisions in Part lI of Figure 1, we do have information that will
enable one to compare the racial composition of the two relevant groups, for example, those who were
searched and those who were not. This permits analyses of those decisions that is much more powerful
than what can be applied to the initial decision to stop a motorist.
The study reports that in this analysis race emerged as a third -order predictor' (among I8-30 year old
Iowa residents) (p. 21) but the details and possible implications of this finding are not reported. Good
practice calls for the presentation of the statistical results for key analyses.
r=flrr,
County residents with the racial distribution of persons stopped in Iowa CAy.1 The results
are shown in Tables 1 and IA attached to this letter.
.,. r o
Table I presents the racial distribution of Iowa City residents (Row A) and, the
racial distribution of people stopped during the eight months in 19I R"hd:.2QOb.;.,,' 4,
relevant comparison is between Row A, which presents the racial 9t?; b%u'tioii-,oh Wa
City citizens, and the racial composition of the motorists stopped by the police, which is
shown in Rows B.1 and B. 2. The only column, in which the citizens stopped are over-
represented in Rows B. 1. and B.2., compared to Row A, is Column D. It indicates that
blacks constitute 2.5% of the population of the city, but represent 8-9% of the people
stopped.
Table IA focuses on the "rates" that citizens are stopped given their.
representation in the City (Row B) and the County (Row Q. Stop rates provide a more
sharply focused measure of the comparative risk that different members of the
community face of being stopped while driving in Iowa City. For example, Row A,
Column A indicates that 5,028 motorists were stopped during the period of this study.
Row B indicates that among the population of Iowa City, the stop rate was .08
(5028/60272). Column D indicates that blacks are the group most at risk of being
stopped. Specifically, the black -motorist stop rate is .30 if the comparison population is
Iowa City and .19 if the comparison population is Johnson County. These rates are from
3 to 4 times higher than the rates experienced by the other racial groups.
We do not suggest that these data constitute definitive evidence of discrimination
since the police practices may have changed since 1999. Moreover, the introduction of
controls for other motorist characteristics may reduce the magnitude of the black motorist
effects documented in Figures 1 and IA. However, we believe that the results presented
in these two figures clearly indicate that the authors, in spite of their skepticism about the
validity of analyses that involve comparison populations, should have included
comparison data in their report. They could then have explained the limitations of this
methodology and left it to City Council and the people of Iowa City to assess the validity
of the comparisons.5
Furthermore, on the issue of stops, the raw data presented in the study raise
several questions that deserve exploration:
a. "Black and other" males are over -represented by about 25% among
drivers stopped in the 21- to 40-year old age range (Table 5, p.9). We would expect this
disparity to be even stronger among the males who were stopped. If racial profiling were
practiced, minorities in this age group, especially males, would be likely targets and
would be over -represented among the motorists who were stopped.
5 Indeed a comparison of the data in the study with comparable results from the earlier period may give the
community a sense of whether the system is improving.
b. Table 8, p.11, indicates that the hours from midnight to 3:00 am
account for a disproportionate proportion of the stops. The racial distributOrf'of fly +: 2: 59
people stopped in this period deserves additional attention.
c. Table 14, page 15 indicates that "black and other" driveL�%Woe more
likely than whites to receive warnings. Other research on this issue indicates that when
racial profiling occurs and traffic stops of minorities are pretextual, the minorities
stopped are less likely to be cited or charged because there was no good cause for the
stop in the first place. In this regard, the racial distribution of the 431 persons against
whom "no action" was taken would be relevant.
C. The Post -Stop Decisions
Part II of Figure 1, which is attached to this letter, indicates the range of decisions
that may be made after a motorist has been stopped. The Edwards et al. study analyzes
thoroughly only two of those decisions — who received warnings and who received
citations (pp.22-23). It concludes that those inquiries detected no evidence of bias.
Because these are core findings, good practice calls for a report of the statistical models
and results of these analyses, which were not included in the study.
It is not clear from the report, why it does not focus as well on the following post -
stop decisions:
a. Other outcomes - such as arrest, field interview, and no action.
b. Searches - requested and conducted, as well as search type.
c. Property seizures.
d. Use of force.
The raw data raise questions about the post -stop decisions that, in our judgment,
are not answered satisfactorily.
The text on page 13 indicates that black drivers consented to searches at a higher
rate than whites (28% v. 23%). This resulted in blacks being over -represented (24%)
among the 83 consent searches compared to their representation (9%) among all persons
stopped (p.9).
Blacks were also over -represented (15%) among those searched "incident to
arrest" (Table 12, p.14).
In addition, other research indicates that the key decision is not the consensual
search itself but the request to conduct the search. When racial profiling is in place,
racial minorities are more likely to be asked if they will consent to a search. That
information is not presented in the report.
Blacks were also more likely to be arrested (13%) v. a 7% rate for whites and 6%
for others (p.15).
0
Conclusion
First, the study's conclusion on the core issue of whether there is racial bias in
traffic stops is not supported by the evidence. It may well be that there is no bias and we
sincerely hope that this is the case. However, the limited scope of the methodology used
in this study cannot support the conclusion that there is no racial profiling in Iowa City
traffic stops. Moreover, the raw data presented in the report suggest further analysis is
needed on the basic stop issue.
Second, as for the issue of bias in post -stop decisions, the report only scratches
the surface of the issues that should have been addressed concerning post -stop searches,
arrests, use of force, property seizures, and final outcomes. In short, far more analysis is
required before this study can validly support a judgment about racial profiling in Iowa
City traffic stops.
If we were reviewing this study for a scholarly journal, we would recommend that
it be returned to the authors for a more thorough consideration of their methodology and
a more systematic analysis of all of the issues that the data permit them to present. It
might also be advisable in any areas that appear to be problematic to conduct follow up
research involving a consideration of the evidence and final disposition of specific cases
and the collection of questionnaires from a sample of motorists who were stopped.
Sincerely yours,
David C. Baldus
34 Seventh Ave. North
George Woodworth
14 West View Acres
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DAVID C. BALDUS h{r147i1 p ryir4f�O01
Joseph B. Tye Professor, University oflowa College of Law • Iowa City, Iowa 52242-1113
Ph: 3191335-9012 - Fax: 3191335-9098 - Internet david-baldus@uiowa.edu �•
ACADEMIC EMPLOYMENT
UNIVERSITY OF IOWA COLLEGE OF LAW, IOWA CITY, IOWA
Joseph B. Tye Professor, 1983 - Present
Professor, 1972-83
Associate Professor, 1969-71
Subjects: Criminal Law, Anti -discrimination Law, Capital Punishment, Federal Criminal
Law, and Admiralty
SYRACUSE UNIVERSITY COLLEGE OF LAW
Center for Interdisciplinary Legal Studies
Professor and Director, 1981-82
NATIONAL SCIENCE FOUNDATION
Director, Law and Social Sciences Program, 1975-76
PRE -ACADEMIC EMPLOYMENT
PENNSYLVANIA CONSTITUTIONAL CONVENTION
Delegate, 1967-68
GENERAL PRACTICE OF LAW, Pittsburgh, Pennsylvania
1964-68
U.S. ARMY/ARMY SECURITY AGENCY (ASA)
Lieutenant, 1958-59
YALE LAW SCHOOL
LL.M, 1969 - LL.B., 1964
UNIVERSITY OF PITTSBURGH
M.A., 1962 (Political Science)
DARTMOUTH COLLEGE
A.B., 1957 (Government Major)
BOOKS AND MONOGRAPHS
Statistical Proof of Discrimination, 386 pages, Shepards-McGraw Hill (1980) (with James
W. Cole).
Annual Supplement, Statistical Proof of Discrimination (1981), (1982), (1983), (1984),
(1985), (1986), and (1987) (with James W. Cole).
Eoual Justice and the Death Penalty: A Legal and Empirical Analvsis, 698 pages,
Northeastern University Press (1990) (with G. Woodworth & C. Puj�s) 4, `, , c 9
ARTICLES. BOOK CHAPTERS & REPORTS
, ..
"State Competence to Terminate Concession Agreements with Ajje3is;•"��3;K„ nt4dy,'L',
56-97 (1964).
"Pennsylvania's Proposed Film Censorship Law - House Bill 1098," 4 Duquesne L. Rev.
429-40 (1966).
"Welfare As A Loan: An Empirical Study of the Recovery of Public Assistance Payments
in the United States," 25 Stanford L. Rev. 123-250 (1973).
"A Model Statute for the Regulation of Abandoned Railroad Rights of Way" in Re -Use
Planning for Abandoned Transportation Properties, Final Report to DOT. 109-25 (K.
Deuker and R. Zimmerman eds. 1975) (with S. Grow).
"A Comparison of the Work of Thorsten Sellin and Isaac Ehrlich on the Deterrent Effect
of Capital Punishment," 85 Yale. L. J. 170-86 (1976) (with J. Cole).
"Quantitative Proof of Intentional Discrimination," 1 Evaluation Quarterly 53-85 (1977)
(with J. Cole).
"Statistical Modeling to Support a Claim of Intentional Discrimination," Am. Statistical
Assn.. Proceedings of the Soc. Star. Sec. Part I pp. 465-70 (1977) (junior author with J.
Cole).
"Quantitative Methods for Judging the Comparative Excessiveness of Death Sentences" in
The Use/Nonuse/Misues of Applied Social Research in the Court: Conference
Proceedings, 83-94 (M. Saks & C. Baron eds. 1980).
"Identifying Comparatively Excessive Sentences of Death," 33 Stan. L. Rev. 601-77
(1980) (with C. Pulaski, G. Woodworth, and F. Kyle).
"Comparative Review of Death Sentences: An Empirical Study of the Georgia
Experience," 74 J. Crim. L. & Criminology 661-753 (1983) (with C. Pulaski & G.
Woodworth).
"Monitoring and Evaluating Contemporary Death Sentencing Systems: Lessons From
Georgia," 18 U.C. Davis L. Rev. 1375-1407 (1985) (with C. Pulaski & G. Woodworth).
"Arbitrariness and Discrimination in the Administration of the Death Penalty: A
Challenge to State Supreme Courts," 15 Stetson L. Rev. 133-261 (1986) (with C. Pulaski
and G. Woodworth).
"Law and Statistics in Conflict: Reflections on McCleskev v. Kemp," in Handbook on
Psychology and Law 251-73 (D. Kagehiro & W. Laufer eds. 1991) (with G. Woodworth &
C. Pulaski).
"Race Discrimination and the Death Penalty," in Oxford Companion to the Suoreme Court of
the United States 705-07 (K. Hall ed. 1991) (with C. Pulaski and G. Woodworth).
Death Penalty Proportionality Review Proi ect: Final Report to The New Jersey Suoreme
Court. 120 pages plus 200+ pages of tables and appendices, (September 24, 1991)
State v. Robert Marshall: Report to the New Jersey Supreme Court, 80 pa�'ges (September 24,
1991).
"Proportionality Review of Death Sentences: The View of the Special Master," 6 Chance
18-27 (Summer 1993) (with G. Woodworth). ('
"Reflections on the 'Inevitability' of Racial Discrimination in Capital 5enterlcing and th"
'Impossibility' of its Prevention, Detection, and Correction;' 51 Wash & Lee L. Rev. 419-
79 (1994) (with G. Woodworth and C. Pulaski).
"Improving Judicial Oversight of Jury Damage Assessments: A Proposal for the
Comparative Additur/Remittitur Review of Awards for Nonpecumary Harms and Punitive
Damages;" 80 Iowa L Rev. 1109-1267 (1995) (with J. MacQueen & G. Woodworth).
Keynote Address: "The Death Penalty Dialogue Between Law and Social Science." 70
Ind. U. L. Rev. 1033- 41 (1995).
"Additur/Remittitur Review: An Empirically Based Methodology for the Comparative
Review of General Damages Awards for Pain, Suffering, and Loss of Enjoyment of Life,"
(with G. Woodworth and J. MacQueen) in Reforming the Civil Justice System, 386415
(Likamer, ed. 1996).
"When Symbols Clash: Reflections on the Future of the Comparative Proportionality
Review of Death Sentences," 26 Seton Hall L. Rev. 1582-1606 (1996).
"Race Discrimination in America's Capital Punishment System Since Furman v. Georgia
(1972): the evidence of race disparities and the record of our courts and legislature in
addressing the issue," Report to A.B.A. Section of Individual Rights and Responsibilities
(7/25/97) (19 pages) (with G. Woodworth).
"Pediatric Traumatic Brain Injury and Bum Patients in the Civil Justice System: The
Prevalence and Impact of Psychiatric Symptomatology;" 26 J .Am. Acad. Psychiatry L.
247-58 (1998) (junior author with J. Max et al.)
"Race Discrimination and the Death Penalty: An Empirical and Legal Overview" (with G.
Woodworth) in America's Experiment with Capital Punishment) 385416 (J. Acker et al,
eds. 1998).
'Race Discrimination and the Death Penalty in the Post Furman Era: An Empirical and
Legal Overview, With Recent Findings From Philadelphia;' 83 Cornell L. Rev. 1638-1770
(1998) (with G. Woodworth et al.).
"The Use of Peremptory Challenges in Capital Murder Trials: A Legal and Empirical
Analysis," 3 U. Penn. J. of Constitutional Law 3-170 (2000) (with G. Woodworth et al,)
"Disposition Of Nebraska Capital and Non -Capital Homicide Cases (1973-1999): A Legal
and Empirical Analysis: Report to the Nebraska Commission on Criminal Justice and Law
Enforcement" (October 10, 2001), 120 pages (with G. Woodworth et al.) (forthcoming in
the University of Nebraska Law Review).
"D. Chambers, Making Fathers Pav;" 78 Mich. L. Rev. 750 (1980)
M. O. Finkelstein, Quantitative Methods in Law & W. Fairley & F. Mosteller, Statistics
and Public Policy, 1980 Am. Bar. Found. R. J. 409.
—L
�—li__'....
"W. White, The Death Penalty in the Eighties" & "H. Bedau, Death is Different," 1 Crim.
L. Forum 185 (1989) (with G. Woodworth & C. Pulaski)......,,, ,�� �'�' ?; -_0
PAPERS PRESENTED SINCE 1985;�'.(�
"Arbitrariness and Discrimination in Capital Sentencing: A Challenge For Presented State
Supreme Courts," Stetson Law School, March 1985.
"Arbitrariness and Discrimination in Capital Sentencing: The Georgia Experience,"
FortunolF Criminal Justice Colloquium, N.Y.U. Law School, May 1985.
"Statistical Proof in Employment Discrimination Litigation: An Overview", State of
Washington Judicial Conference, Tacoma, Washington, August, 1985.
"Arbitrariness and Discrimination in Capital Sentencing" Symposium on Capital
Punishment, Columbia Law School, December 1985.
"Capital Punishment -- A Tragic Choice?" Mount Mercy College, Cedar Rapids, Iowa,
April 1986.
"Consistency and Evenhandedness in Federal Death Sentencing Under Proposed
Legislation," testimony before House Criminal Justice Subconumttee, Washington, D.C.,
May 1986.
"The Impact of Prosecutional Discretion on Arbitrariness and Discrimination," American
Criminology Society, Atlanta, GA, November 1986.
"Death Penalty Cases: The Role of Empirical Data," National Judicial College of San
Diego, February 10, 1987.
"Individual Rights and the Constitution: Issues and Trends in the Death Penalty,"
Controversy & The Constitution Conference, Ames, Iowa, February 12, 1987.
"Equal Justice in Proposed Federal Death -Sentencing Legislation: lessons from the states,"
Testimony before the United States Sentencing Commission, Hearing on the
Commission's responsibility regarding promulgation of sentencing guidelines for federal
capital offenses, Washington, D.C., February 17, 1987.
"Usable Knowledge from the Social Sciences: A Lawyer's Perspective," University of
Nebraska College of Law, April 10, 1987.
"Equal Justice and the Death Penalty: Some Empirical Evidence," University of Nebraska
College of Law, April 10, 1987.
"McCleskey v. Kemp: A methodological critique," Law and Society Association,
Washington, D.C., June 12, 1987.
"Law and Statistics in Conflict: Reflections on McCleskey v. Kemp," University of Bristol
(March 4, 1988), University of Durham (March 16, 1988), Hebrew University (April 17,
1988), University of Reading (May 6, 1988), University of Oxford (May 27, 1988).
"Arbitrariness and Discrimination in the Imposition of the Deadi Piehaliy; Testimony
before Senate Judiciary Committee, Washington, D.C., October 2, 1989.
19 1io 2: S9
"Arbitrariness and Racial Discrimination in Post -Furman Deat "Sentencing: Implications
for the Racial Justice Act and Proposed Federal Death -Penalty ggislati9gn�."-Testimony
before the Constitutional and Civil Rights Subcommittee, ggµkE' ddicia}y C,iirfttnittee,
Washington, D.C., May 3, 1990.
"The Proportionality Review of Death Sentence: New Jersey's Options," New Jersey Bar
Assembly, Headquarters, New Brunswick, New Jersey, April 23, 1992.
"Proportionality Review of Death Sentences: New Jersey's Options," Law and Society
Association, Philadelphia, May 24, 1992.
"Regulating the Quantum of Damages for Personal Injuries through Enhanced Additur-
Remittitur Review," Law and Society Association, Philadelphia, May 28, 1992.
"Proportionality Review of Death Sentences" & "Race Discrimination in the Use of the
Death Penalty," University of Michigan Law School, January 1993.
"Reflections on the Reinstatement of the Death Penalty in Iowa," Public Lecture, Coe
College, April 1993.
"Discretion and Disparity in the Administration of the Death Penalty" & "Racial and
Ethnic Bias in the Criminal Law: Some Trends and Prospects," AALS Workshop on
Criminal Law, Washington, D.C., October 29 & 30, 1993.
"Improving Judicial Oversight of Jury Damages Assessments: A Proposal for the
Comparative Additur/Remittitur Review of Awards for non -pecuniary harms and punitive
damages," Conference of Chief Justices, Williamsburg, Virginia, January 1993;
Department of Pediatrics, University of Iowa Medical School, February, 1993; Conference
on Civil Justice Reform, NYU Law School, October 1993.
"Racial Discrimination in Capital Sentencing: Reflections on its Inevitability and the
Impossibility of its Prevention and Cure," Symposium on Racism in the Criminal Law,
Washington and Lee Law School, March 11, 1994.
"Racial Discrimination in Mortgage Lending," Department of Housing and Urban
Development, January 19, 1994.
"The Death Penalty Dialogue Between Law and Social Science," Keynote Address,
Symposium, Capital Jury Project, Indiana Law School, February 24, 1995.
"Reflections on the Failure to Reinstate the Death Penalty in Iowa" & "Claims of
Arbitrariness and Discrminaton Under State Law; recent trends." Legal Defense Fund
Annual Conference on the Death Penalty, Airlie House, Virginia, July 28 & 29, 1995.
"Statistical Approaches to Title VII Discrimination Claims" Defense Lawyers Association,
Des Moines, September 1995.
"The Marshall Hypothesis Revisited," University of Pittsburgh Law School, October 1995.
"When Symbols Clash, Reflections of Proportionality Review, Death Sentences,"
Luncheon speaker, Death Penalty Conference, Seton Hall Law School, Nov. 2, 1995.
Law As Symbol: explaining the uses of the death penalty in Anurica,f ID PAu1
School, Chicago, January 1996; Northwestern Law School, March 1996,
99 9
"Post-McCleskey Discrimination Claims: Law, Proof and Possibilities;"�{Z'leiry" Sessiotx
Legal Defense Fund Annual Conference on the Death Penalty, Georgpt9wn University,
July 26, 1996.E ,t
"Preliminary Finding from the Pennsylvania Capital Charging and Sentencing Study" and
"Law As Symbol," American Criminology Society, November 1996.
"The Death Penalty and How It Might Affect the Iowa Practitioner," Iowa Bar Association
Criminal Law Seminar, Des Moines, March 21, 1997.
"Race Discrimination and the Death Penalty: Recent Findings from Philadelphia" Plenary
Session, Legal Defense Fund Annual Conference on the Death Penalty, Airlie House,
Virginia, July 1997; Death Penalty Symposium; Cornell Law School March 1998;
American Society of Criminology, Washington D.C. November 1998.
"The Death Penalty for Iowa: What Would It Bring," testimony before the Iowa House
Judiciary Committee, March 1998.
"Race Discrimination and the Proportionality Review of Death Sentences," Yale Law
School, March 1998; St. John's Law School, March 1999.
`The Use of Peremptory Challenges in Capital Murder Trials: A Legal and Empirical
Analysis," Research Club, University of Iowa, December 17, 1999; Center for Socio-
Legal Studies, University of Iowa, January 21, 2000; "Race, Crime, and the Constitution
Symposium," University of Pennsylvania Law School, January 29, 2000; Law Dept.,
Erlangen University, Erlangen, Germany, July 18, 2000.
"Race Discrimination in the Administration of the Death Penalty," Senate Judiciary
Committee, Pennsylvania Legislature, Harrisburg, Pa., January 22, 2000; The Governor's
Race and the Death Penalty Task Force, Tallahassee, Florida, March 30, 2000.
"Reflections on the Use of Capital Punishment in Europe and the United States," Political
Science Dept., Erlangen University, Erlangen, Germany, July 17, 2000.
"Race Discrimination in the Administration of the Death Penalty: Current Concerns and
Possible Strategies for Addressing the Issue During a Moratorium on Executions," ABA's
Call to Action: A Moratorium on Executions, ABA Conference, Carter Center, Atlanta,
Georgia, October 12, 2001.
"Race and Gender Disparities in the Administration of the Death Penalty: Recent Finding
From Philadelphia and Legislative and Judicial Strategies to Reduce Race and Gender
Effects," Pennsylvania Supreme Court Committee on Racial and Gender Bias in the
Justice System, Philadelphia, Pa. December 6, 2000.
"Race Discrimination in the Administration of the Death Penalty," Death Penalty
Symposium, NYU Law School, March 29, 2001.
"Reflections on the Use of the Death Penalty in Europe and the United States," Capital
Punishment Symposium, Ohio State Law School, March 31, 2001.
"Arbitrariness and Discrimination in the Administration of the Death Penalty: the
Nebraska Experience," Judiciary Committee, Nebraska Legislature, October 18, 2001;
University of Nebraska Law School, February 22, 2002.
0
01
Member: American Bar Association; American Law Institute; American Society of
Criminology; Law and Society Association.
Board of Editors: Evaluation Quarterly (1976-79); Law & Policy Quarterly (1978-79);
Law and Human Behavior (1984- );Psychology, Public Policy and Law (1994- ).
Board of Trustees, Law and Society Association (1992-94).
Grant Recipient, N.S.F. Law and Social Science Program
1974-75— "Quantitative Proof of Discrimination."
Invited Participant, N.S.F. Sponsored Conference on the Use of Scientific Evidence in
Judicial Proceedings, November 1977.
Invited Participant, ABA--AAAS Conference on Cross Education of Lawyers and
Scientists, Airlie House, Virginia, May 1978,
Reporter, Roscoe Pound Am. Tr. Lawyers Foundation Conf. On Capital Punishment,
Harvard University, June 1980.
Grant Recipient, National Institute of Justice, 1980-81, "The Impact of Procedural Reform
on Capital Sentencing: the Georgia Experience."
Consultant, Delaware Supreme Court, April 1981 and South Dakota Supreme Court,
November 1981, on the proportionality review of death sentences.
Member, Special Committee of the Association of the Bar of New York on Empirical Data
in Legal Decision Making and the Judicial Management of Large Data Sets (1980-82).
Grant Recipient, NSF Law & Social Science Program "A Longitudinal Study of
Homicide Case Processing" (1983).
Consultant, National Center for State Courts project on the proportionality review of death
sentences (1982-84).
Expert witness in McCleskey v. Kemp, 105 S.C1. 1756 (1987), a capital case challenging
the constitutionality of Georgia's capital sentence process.
Recipient, Law and Society Association's Harry Kalven Prize for Distinguished
Scholarship in Law and Society (with G. Woodworth & C. Pulaski) for our capital
punishment research ( June 11, 1987).
Grant recipient, State Justice Institute, 1988-1992, "Judicial Management of Judicial
Awards for Noneconomic and Punitive Damages" (with Dr. J. MacQueen & J. Gittler).
Special Master for Proportionality Review of Death Sentences for the New Jersey
Supreme Court: 1988-91.
Member, AALS Committee on Curriculum and Research (1994-97)
7
Recipient, "Michael J. Brody Award for Faculty Excellence in Service to the University of
Iowa", October 1996.
Recipient, "Award For Faculty Excellence," Board of Regents, State of Iowa, October 18,
2000.
Grant recipient, Nebraska Crime Commission, "The Disposition of Nebraska Homicide
Cases (1973-1999)" (2000)
Member, AAUP, Iowa Chapter (1969-__), Member, Executive Board (1992-
Member Committee A (1985-_)
tp
O.•ISHAREDIDATABASEIBALDUSIRESU.NEIDB.Canem Remmedoc
3
Address:
George Woodworth
Department of Statistics
and Actuarial Science
241 SH
University of Iowa
Iowa City, IA 52242
Personal Data:
GEORGE WOODWORTH
CURRICULUM VITAE
August, 2002
FAX:
319-335-3017
Voice:
319-335-0816
Home:
319-337-2000
Intemet:
George-Woodworth@uiowa.edu
Born: May 29, 1940, Oklahoma City, Oklahoma
Marital Status: Married with two children
Education:
B.A. Carleton College, Northfield, Minnesota, 1962
Ph.D. University of Minnesota, 1966
Employment:
f„ 2: r o
Instructor, Department of Statistics, University of Minnesota, 1965-66.
Assistant Professor, Department of Statistics, Stanford University, 1966-71.
Assistent (Visiting Assistant Professor), Department of Mathematical Statistics, Lund Institute
of Technology, Lund, Sweden, 1970-71 (on leave from Stanford).
Associate Professor, Department of Statistics, The University of Iowa, Iowa City, Iowa, 1971-
1996.
Associate Director, Director (1973-1980), Acting Director (1982-3), Adviser (1984-present):
University of Iowa Statistical Consulting Center.
Associate Professor, Department of Preventive Medicine, Division of Biostatistics, University
of Iowa, 1990-1996.
Professor, Department of Statistics and Actuarial Science, University of Iowa, 1996-.
Professor, Department of Preventive Medicine, Division of Biostatistics, University of Iowa
1996-.
Research Interests:
Bayesian Inference
Statistical Computing
Applications of Statistics in Biomedical Science, Behavioral Science, and Law and Justice
Multivariate Analysis and Discrete Multivariate Analysis
Choice Modeling
Longitudinal Data
Dissertations Supervised:
Stanford University Ph.D.: "; ; ; I l 31;
1. Reading, James (1970). "A Multiple Comparison Procedure for Classifying All Pairs out of k
Means as Close or Distant".
2. Withers, Christopher Stroude (1971). "Power and Efficiency of a Class �£C'oddness of Fib't'
Tests."
3. Rogers, Warren (1971). "Exact Null Distributions and Asymptotic Expansions for Rank Test
Statistics."
University of Iowa, Ph.D.:
4. Huang, Yih-Min (1974). "Statistical Methods for Analyzing the Effect of Work -Group Size
Upon Performance."
5. Scott, Robert C. (1975). "Smear and Sweep: a Method of Forming Indices for Use in Testing
in Non -Linear Systems."
6. Hoffman, Lorrie Lawrence (1981). "Missing Data in Growth Curves."
7. Patterson, David Austin (1984). "Three -Population Partial Discrimination."
8. Mori, Motomi (1989). "Analysis of Incomplete Longitudinal Data in the Presence of
Informative Right Censoring." (Biostatistics, joint with Robert Woolson)
9. Galbiati-Riesco, Jorge Mauricio (1990). "Estimation of Choice Models Under
Endogenous/Exogenous Stratification."
10. Shin, Mi-Young (1993). "Consistent Covariance Estimation for Stratified Prospective and
Case -Control Logistic Regression."
11. Lian, Ie-Bin (1993). "The Impact of Variable Selection Procedures on Inference for a
Forced -in Variable in Linear and Logistic Regression."
12. Nunez Anton, Vicente A. (1993). "Analysis of Longitudinal Data with Unequally Spaced
Observations and Time Dependent Correlated Errors."
13. Bosch, Ronald J. (1993), "Quantile Regression with Smoothing Splines."
14. Samawi, Hani Michel (1994). "Power Estimation for Two -Sample Tests Using Importance
and Antithetic Resampling." (Biostatistics, joint with Jon Lemke)
15. Chen, Hungta (1995). "Analysis of Irregularly Spaced Longitudinal Data Using a Kernel
Smoothing Approach." (Biostatistics)
16. Nichols, Sara (2000). "Logistic Ridge Regression." (Biostatistics)
17. DehkOrdi, Farideh Hossein (2001). "Smoothness Priors for Longitudinal Covariance
Functions." (Biostatistics)
University of Iowa, MS:
18. Juang , Chifei (1993). "A Comparison of Ordinary Least Squares and Missing Information
Estimates for Incomplete Block Data."
19. Wu, Chia -Chen (1993). "Time Series Methods in the Analysis of Automatically Recorded
Behavioral Data."
20. Peng, Ying (1995). "A Comparison of Chi -Square and Normal Confidence intervals for
Variance Components Estimated by Maximum Likelihood."
21. Wu, Li -Wei (1996). "CART Analysis of the Georgia Charging and Sentencing Study."
22. Meyers,Troy (2000) "Bias Correction for Single -Subject Information Transfer id
Audiological Testing."
Publications
Refereed Publications:
1. Savage, I.R., Sobel, M., Woodworth, G.G. (1966), "Fine Structure of the otttering df
Probabilities of Rank Orders in the Two Sample Case," Annals of Mathematical Statistics,
37, 98-112.
2. Basu, A.P., Woodworth, G.G. (1967), "A Note on Nonparametric Tests for Scale," Annals
of Mathematical Statistics, 38, 274-277.
3. Rizvi, M.M., Sobel, M., Woodworth, G.G. (1968), "Non -parametric Ranking Procedures
for Comparison with a Control," Annals of Mathematical Statistics, 39, 2075-2093.
4. Woodworth, G.G. (1970), "Large Deviations, Bahadur Efficiency of Linear Rank
Statistics," Annals of Mathematical Statistics, 41, 251-183.
5. Rizvi, M.H., Woodworth, G.G. (1970), "On Selection Procedures Based on Ranks:
Counterexamples Concerning Least Favorable Configurations," Annals of Mathematical
Statistics, 41, 1942-1951.
6. Woodworth, G.G. (1976), "t for Two: Preposterior Analysis for Two Decision Makers:
Interval Estimates for the Mean," The American Statistician, 30, 168-171.
7. Hay, J.G., Wilson, B.D., Dapena, J., Woodworth, G.G. (1977), "A Computational
Technique to Determine the Angular Momentum of a Human Body," J. Biomechanics, 10,
269-277.
8. Woodworth, G.G. (1979), "Bayesian Full Rank MANOVA/MANCOVA: An Intermediate
Exposition with Interactive Computer Examples," Journal of Educational Statistics, 4(4),
357404.
9. Baldus, DC., Pulaski, C.A., Woodworth, G.G., Kyle, F. (1980), "Identifying
Comparatively Excessive Sentences of Death: A Quantitative Approach," Stanford Law
Review, 33(1),1-74.
10. Louviere, J.J., Henley, D.H., Woodworth, G.G., Meyer, J.R., Levin, I. P., Stoner, J.W.,
Curry, D., Anderson D.A. (1981), "Laboratory Simulation vs. Revealed Preference
Methods for Estimating Travel Demand Models: An Empirical Comparison,"
Transportation Research Record, 797, 42-50.
11. Baldus, D.C., Pulaski, C.A., Woodworth, G.G. (1983), "Comparative Review of Death
Sentences: An Empirical Study of the Georgia Experience," The Journal of Criminal Law
and Criminology, 74(3), 661-753.
12. Louviere, J.J., Woodworth, G.G. (1983), "Design and Analysis of Simulated Consumer
Choice of Allocation Experiments: An Approach Based on Aggregate Data," Journal of
Marketing Research, XX, 350-367.
13. Baldus, D.C., Pulaski, C.A., Woodworth, G.G. (1986), "Monitoring and Evaluating
Contemporary Death Sentencing Systems: Lessons from Georgia," U.C. Davis Law Review,
I8(4), 1375-1407.
14. Baldus, D.C., Pulaski, C.A., Woodworth, G.G. (1986), "Arbitrariness and Discrimination
in the Administration of the Death Penalty: A Challenge to State Supreme Courts," Stetson
Law Review, XV(2), 133-261.
15. Bober, T., Putnam, C.A., Woodworth, G.G. (1987), "Factors Influencing the Angular
Velocity of a Human Limb Segment," Journal of Biomechanics, 20(5), 511-521.
16. Gantz, B.J., Tyler, R.S., Knutson, J.F., Woodworth, G.G., Abbas, P., McCabe, F..,L - -
Hinrichs, J., Tye -Murray, N., Lansing, C., Kuk, F., Brown, C. (1988), "Evaluation of Five
Different Cochlear Implant Designs: Audiologic Assessment and Predic('ori'.df I j ('. ; j: 0
Performance," Laryngoscope, 98(10), 1100-6.
17. Tye -Murray, N., Woodworth, G.G. (1989), "The Influence of Final Syllable i6tion orh.,
the Vowel and Word Duration of Deaf Talkers," Journal of the Acoustic K$Q ety_of' )"i-VA
America, 85, 313-321.
18. Baker, R.G., Van Nest, J., Woodworth, G.G. (1989), "Dissimilarity Coefficients for Fossil
Pollen Spectra from Iowa and Western Illinois During the Last 30,000 Years," Palynology,
13, 63-77.
19. Shymansky, J.A., Hedges, L.V„ Woodworth, G.G. (1990), "A Reassessment of the Effects
of 60's Science Curricula on Student Performance," Journal of Research in Science
Teaching, 27(2), 127-144.
20. Tye -Murray, N., Purdy, S., Woodworth, G.G., Tyler, R.S. (1990), "Effect of Repair
Strategies on Visual Identification of Sentences," Journal of Speech and Hearing
Disorders, 55, 621-627.
21. Cadoret, R.C., Troughton, E.P., Bagford, J.A., Woodworth, G.G. (1990), "Genetic and
Environmental Factors in Adoptee Antisocial Personality," European Archives of
Psychiatry and Neurological Sciences, 239(4), 231-240.
22. Chakraborty, G., Woodworth, G.G., Gaeth, G.J., Ettenson, R. (1991), "Screening for
Interactions Between Design Factors and Demographics in Choice -Based Conjoint,"
Journal of Business Research, 23(3), 219-238.
23. Kochar, S.C., Woodworth, G.G. (1991). "Rank order Probabilities for the Dispersion
Problem," Statistics & Probability Letters, 14(4), 203-208.
24. Knutson, J.F., Hinrichs, J.V., Tyler, R.S., Gantz, B.J., Schartz, H.A., Woodworth, G.G.
(1991), "Psychological Predictors of Audiological Outcomes of Multichannel Cochlear
Implants: Preliminary Findings," Annals of Otology, Rhinology & Laryngology, 100(10),
817-822.
25. Knutson, J.F., Schartz, H.A., Gantz, B.J., Tyler, R.S., Hinrichs, J.V., Woodworth, G.G.
(1991), "Psychological Change Following 18 Months of Cochlear Implant Use," Annals of
Otology, Rhinology & Laryngology, 100(11), 877-882.
26. Kirby, R.F., Woodworth, C.H., Woodworth G.G., Johnson, A.K. (1991), "Beta-2
Adrenoceptor Mediated Vasodilation: Role in Cardiovascular Responses to Acute Stressors
in Spontaneously Hypertensive Rats," Clin. and Exper. Hypertension.- Part A, Theory and
Practice, 13(5), 1059-1068.
27. Tye -Murray, N., Tyler, R.S., Woodworth, G.G., Gantz, D.J. (1992), "Performance over
Time with a Nucleus or Ineraid Cochlear Implant," Ear and Hearing, 13, 200-209.
28. Tye -Murray, N., Purdy, S.C., Woodworth, G.G. (1992), "Reported Use of Communication
Strategies by SHHH Members: Client, Talker, and Situational Variables," Journal of
Speech & Hearing Research, 35(3), 708-717.
29. Mori, M., Woodworth, G.G., Woolson, R.F. (1992), "Application of Empirical Bayes
Inference to Estimation of Rate of Change in the Presence of Informative Right Censoring,"
Statistics in Medicine, 11, 621-631.
!--11 ,
30. Shymansky, J.A., Woodworth, G.G., Norman, O., Dunkhase, J., Matthews, C., Lid, 2: -
(1993), "A Study of Changes in Middle School Teachers' Understanding of Selected Ideas
in Science as a Function of an In -Service Program Focusing on Student PrGcir7ge{itjoj�s�" kh 4;
Res. in Science Teaching, 30, 737-755.
31. Wallace, R.B., Ross, J.E., Huston, J.C., Kundel, C., Woodworth, G.G. (1993�, Euuwa,,i _ _ i
FICSIT Trial: The Feasibility of Elderly Wearing a Hip Joint Protective Gai@II hY to;I.edycei, Y;',,�
Hip Fractures," J. Am. Geriatr. Soc., 41(3), 338-340.
32. Gantz, B.J., Woodworth, G.G., Knutson, J. F., Abbas, P.J., Tyler, R.S. (1993),
"Multivariate Predictors of Success with Cochlear Implants," Advances in Oto-Rhino-
Laryngology, 48, 153-67.
33. Mori, M., Woolson, R.F., Woodworth, G.G. (1994), "Slope Estimation in the Presence of
Informative Right Censoring: Modeling the Number of Observations as a Geometric
Random Variable," Biometrics, 50(1), 39-50.
34. Nunez -Anton, V., Woodworth, G.G. (1994), "Analysis of Longitudinal Data with
Unequally Spaced Observations and Time Dependent Correlated Errors," Biometrics,
50(2), 445-456.
35. Baldus, D.C., Woodworth, G.G., Pulaski, C.A. (1994), "Reflections on the Inevitability of
Racial Discrimination in Capital Sentencing and the Impossibility of Its Prevention,
Detection, and Correction," Washington and Lee Law Review, 51(2), 359-430.
36. Cutrona, C.E., Cadoret, R.J., Suhr, J.A., Richards, C.C., Troughton, E. Schutte, K.,
Woodworth, G. G. (1994), "Interpersonal Variables in the Prediction of Alcoholism
Among Adoptees: Evidence for Gene -Environment Interactions," Comprehensive
Psychiatry, 35(3), 171-9.
37. De Fillippo, C.L., Lansing, C.R., Elfenbein, J.L., Kallaus-Gay, A., Woodworth, G.G.
(1994), "Adjusting Tracking Rates for Text Difficulty via the Cloze Technique," Journal
of the American Academy of Audiology, 5(6), 366-78
38. Gantz, B.J., Tyler, R.S., Woodworth, G.G., Tye -Murray, N. Fryauf-Bertschy, H. (1994),
"Results of Multichannel Cochlear Implants in Congenital and Acquired Prelingually
Deafened Children: Five Year Follow -Up," Am. J. Otol., 15 (Supplement 2), 1-7.
39. Cadoret, R.J., Troughton, E., Woodworth, G.G. (1994), "Evidence of Heterogeneity of
Genetic Effect in Iowa Adoption Studies," Annals of the New York Academy of Sciences,
708, 59-71.
40, Bosch, R., Ye, Y., Woodworth, G.G. (1995), "An Interior Point Quadratic Programming
Algorithm Useful for Quantile Regression with Smoothing Splines," Computational
Statistics and Data Analysis, 19, 613-613.
41. Cadoret, R.J., Yates, W.R., Troughton, E., Woodworth, G.G., Stuart, M.A. (1995),
"Adoption Study Demonstrating Two Genetic Pathways to Drug Abuse," Archives of
General Psychiatry, 52(I),42-52.
42. Tye -Murray, N., Spencer, L., Woodworth, G.G. (1995), "Acquisition of Speech by
Children who have Prolonged Cochlear Implant Experience," Journal of Speech & Hearing
Research, 38(2),327-37.
43. Cadoret, R.J., Yates, W.R., Troughton, E., Woodworth, G.G., Stewart, M.A. (1995),
"Genetic -Environmental Interaction in the Genesis of Aggressivity and Conduct Disorders,"
Archives of General Psychiatry, 52(I1), 916-924.
44. Tyler, R.S., Lowder, M.W., Parkinson, A.J., Woodworth, G.G., Gantz, B.J. (1995),
"Performance of Adult Ineraid and Nucleus Cochlear Implant Patients after 3.5 Years of
Use," Audiology, 34(3), 135-144.
r-+I
45. Baldus, D, MacQueen, JC, and Woodworth GG. (1995) "Improving Judicial Ovirsighi6f .
Jury Damages Assessments: A Proposal for the Comparative Additur/Remittitur Review of
Awards for Nonpecuniary Harms and Punitive Damages," with John C. Md4Qkeh t= 7: r, 0
George Woodworth, 801owa Law Review 1109 (1995), 159 pages.
46. Parkinson, A.J., Tyler, R.S., Woodworth, G.G., Lowder, M., Gantz, B.J., (19M) ."A Within,
Subject Comparison of Adult Patients Using the Nucleus FOF1F2 and FO)g',>7V1 13465'
Speech Processing Strategies," Journal of Speech & Hearing Research, Volume 39, 261-
277.
47. Baldus, D., MacQueen, J.C., Woodworth, G.G., (1996) "Improving Judicial Oversight of
Jury Damages Assessments: A Proposal for the Comparative Additur/Remittitur Review of
Awards for Nonpecuniary Harms and Punitive Damages," Iowa Law Review, (80) 1109-
1267.
48. Cadoret, Remi J., Yates, William R., Troughton, E., Woodworth, G.G. (1996) "An
Adoption Study of Drug Abuse/Dependency in Females," Comprehensive Psychiatry, Vol.
37, No, 2, 88-94.
49. Tripp -Reimer, T., Woodworth, G.G., McCloskey, J.C., Bulechek, G. (1996), "The
Dimensional Structure of Nursing Intervention," Nursing Research 45(1) 10-17.
50. Tyler RS. Fryauf-Bertschy H. Gantz BJ. Kelsay DM. Woodworth GG. (1997) "Speech
perception in prelingually implanted children after four years," Advances in Oto-Rhino-
Laryngology. 52:187-92.
51. Tyler RS, Gantz BJ, Woodworth GG, Fryauf-Bertschy H, and Kelsay DM. (1997)
"Performance of 2- and 3-year-old children and prediction of 4-year from 1-year
performance. American Journal of Otology. 18(6 Suppl):SI57-9, 1997.
52. Miller CA, Abbas PJ, Rubinstein JT, Robinson BK, Matsuoka AJ, and Woodworth G.
(1998) "Electrically evoked compound action potentials of guinea pig and cat: responses to
monopolar, monophasic stimulation." Hearing Research. 119(1-2):142-54, 1998 May.
53. Knutson JF, Murray KT, Husarek S, Westerhouse K, Woodworth G, Gantz BJ, and Tyler
RS. (1998) "Psychological change over 54 months of cochlear implant use." Ear &
Hearing, 19(3):191-201, 1998.
54. Gfeller K, Knutson JF, Woodworth G, Witt S, and DeBus B. (1998) "Timbral recognition
and appraisal by adult cochlear implant users and normal -hearing adults." Journal of the
American Academy of Audiology, 9(1):1-19,1998.
55. Baldus D, Woodworth G, Zuckerman D, Weiner NA, Broffitt B. (1998) "Racial
Discrimination and the Death Penalty in the Post -Furman Era: An Empirical and Legal
Overview with Recent Findings from Philadelphia," Cornell Law Review, 88:6, 1998.
56. Green GE. Scott DA. McDonald JM. Woodworth GG. Sheffield VC. Smith RJ. Carrier
rates in the midwestem United States for GJB2 mutations causing inherited deafness.
JAMA. 281(23):2211-6, 1999 Jun 16.
57. Gantz BJ. Rubinstein IT. Gidley P. Woodworth GG. Surgical management of Bell's palsy.
Laryngoscope. 109(8):1177-88, 1999 Aug
58. Featherstone KA. Bloomfield JR. Lang AJ. Miller -Meeks MJ. Woodworth G. Steinert RF.
Driving simulation study: bilateral array multifocal versus bilateral AMO monofocal
intraocular lenses. Journal of Cataract & Refractive Surgery. 25(9):1254-62, 1999 Sep.
59. Weiler JM. Bloomfield JR. Woodworth GG, Grant AR. Layton TA. Brown TL. McKenzie
DR. Baker TW. Watson GS. Effects of fexofenadine, diphenhydramine, and alcohol on
driving performance. A randomized, placebo -controlled trial in the Iowa driving simulator.
Annals oflnternal Medicine. 132(5):354-63, 2000 Mar 7
60. Tyler RS. Teagle HE Kelsay DM. Gantz BJ. Woodworth GG. Parkinson AJ. Slfee h
perception by prelingually deaf children after six years of Cochlear implant use: effects of
age at implantation. Annals of Otology, Rhinology, & Laryngology - Supp(einhut; 16: &4, 3: C 0
2000 Dec.
61. Ballard KJ. Robin DA. Woodworth G. Zimba LD. Age -related changes in rn tbf 6ontrdl
during articulator visuomotor tracking. Journal of Speech Language & Hii i'hgt Rese wchjj!.. �' /A
44(4):763-77, 2001 Aug.
62. Gfeller K. Witt S. Woodworth G. Mebr MA. Knutson J. Effects of frequency, instrumental
family, and cochlear implant type on timbre recognition and appraisal. Annals of Otology,
Rhinology & Laryngology. 111(4):349-56, 2002 Apr.
Books, Chapters:
63. Bober, T., Hay, J.G., Woodworth, G.G. (1979), "Muscle Pre -Stretch and Performance," in
Science in Athletics, eds. Juris Terauds and George G. Dales, Del Mar CA: Academic
Publishers, pp. 155-166.
64. Hay, J.G., Dapena, J., Wilson, B.D., Andrews, J.G., Woodworth, G.G. (1979), "An
Analysis of Joint Contributions to the Performance of a Gross Motor Skill," in
International Series on Biomechanics, Vol, 2B, Biomechanics VI-B, eds. Erling Asmussen
and Kuert Jorgensen, Baltimore: University Park Press, pp. 64-70.
65. Hay, J.G., Vaughan, C.L., Woodworth, G.G. (1980). "Technique and Performance:
Identifying the Limiting Factors," in Biomechanics VII-B, eds. Adam Morecki, Kazimerz
Fidelus, Krzysztof Kedzior, Andrzej Wit, Baltimore: University Park Press, pp. 511-520.
66. Woodworth, G.G. (1980). "Numerical Evaluation of Preposterior Expectations in the Two -
Parameter Normal Model, with an Application to Preposterior Consensus Analysis," in
Bayesian Analysis in Econometrics and Statistics, ed. Arnold Zellner, Amsterdam: North -
Holland Publishing Co., pp. 133-140.
67. Hodges, L.V., Shymansky, J.A., Woodworth, G.G. (1989), Modern Methods ofMeta-
Analysis: an NSTA Handbook, Washington, D.C.: National Science Teachers Association.
68. Baldus, D.C., Woodworth, G.G., Pulaski, C.A. (1990), Equal Justice and the Death
Penalty: A Legal and Empirical Analysis, Boston: Northeastern University Press.
69. Baldus, D., Pulaski, C., Woodworth GG (1992) "Law and Statutes in Conflict: Reflections
on McCleskey v. Kemp," in Handbook of Psychology and Law, edited by Dorothy K.
Kagehiro and William S. Laufer. New York: Springer-Verlag, 1992.
70. Baldus, D., Pulaski, C., Woodworth GG (1992) "Race Discrimination and the Death
Penalty," with Charles J. Pulaski, Jr. and George Woodworth, in The Oxford Companion to
the Supreme Court of the United States. New York: Oxford University Press, 1992, p 705-7.
71. Woodworth, G.G. (1994). "Managing Meta -Analytic Databases," in The Handbook of
Research Synthesis, eds. Harris Cooper and Larry V. Hedges, New York: Russell Sage
Foundation, pp.177-189.
72. Lovelace, D. Cryer, J., Woodworth, G.G. (1994), Minitab Handbook to Accompany
Statistics for Business Data Analysis and Modelling, 2nd edition, Belmont, CA:
Wadsworth Publishing Company,
73. Tye -Murray, N. Kirk, K.L., Woodworth, G.G. (1994). "Speaking with the Cochlear Implant
Turned On and Turned Off," in Datenknovertierung, Reproduktion and Drick, eds. I.J.
Hochmair-Desoyer and E.S. Hochmair, Wien, Manz, pp. 552-556.
74. Baldus, D. MacQueen, JC, Woodworth GG. (1996) "Additur/Remittitur Review: An
Empirically Based Methodology for the Comparative Review of General Damages Awards
for Pain, Suffering, and Loss of Enjoyment of Life," with John C. MacQueen and 6ep 1 i<ga .
Woodworth, in Reforming the Civil Justice System, edited by Lary Kramer. New York:
New York University Press, 1996, p 386, 30 pages.
19 F. 3:CIO
75. Baldus, D, and Woodworth, GG. (1998) "Race Discrimination and the Death Penalty: An
Empirical and Legal Overview," with George Woodworth, in America's Experir@ i,wit}l, i.,,,;
Capital Punishment, edited by James C. Acker, Robert M. Bohm, and CharlBb; LanO . '.'!r�
Durham, NC: Carolina Academic Press, 1998, page 385, 32 pages.
Unrefereed Articles, Reviews:
76. Libby, D.L., Novick, M.R., Chen, J.A., Woodworth, G.G., Hamer, R.M. (1981), "The
Computer -Assisted Data Analysis (CADA) Monitor," The American Statistician, 35(3),
165-166.
77. Woodworth, G.G. (1987), "STATMATE/PLUS, Version 1.2," The American Statistician,
41(3), 231-233.
78. Hoffmaster, D., Woodworth, G.G. (1987), "A FORTRAN Version of the Super Duper
Pseudorandom Number Generator," Science Software Quarterly, 3(2), 100-102.
79. Baldus, D.C., Woodworth, G.G., Pulaski, C.A. (1987) "Death penalty in Georgia remains
racially suspect," Atlanta Journal and Constitution, September 6, 1987.
80. Hawkins, D., Conaway, M., Hack1, P., Kovacevic, M., Sedransk, J., Woodworth, G.G.,
Bosch, R, Breen, C. (1989) "Report on Statistical Quality of Endocrine Society Journals,"
Endocrinology, 125(4), 1749-53.
81. Woodworth, G.G. (1989). "Statistics and the Death Penalty," Stats. The Magazinefor
Students of Statistics, 2, 9-12.
82. Baldus, D.C., Pulaski, C.A., Woodworth, G.G. (1989), "Reflections on 'Modem' Death
Sentencing Systems," Book review, Criminal Law Forum, 1, 190-197.
83. Baldus, D., Woodworth, G.G. (1993). "Proportionality: The View of the Special Master,"
Chance, New Directions for Statistics and Computers, 6(3), 9-17.
84. "Race Discrimination in America's Capital Punishment System since Furman v. Georgia
(1972): The Evidence of Race Disparities and the Record of Our Courts and Legislatures in
Addressing the Issue," with George Woodworth, Report to the A.B.A. Section of Individual
Rights and Responsibilities (1997), 19 pages.
85. Baldus, David C., George Woodworth, David Zuckerman, Neil Alan Weiner, and Barbara
Broffitt (2001). "The Use of Peremptory Challenges in Capital Murder Trials: A legal and
Empirical Analysis," University of Pennsylvania Journal of Constitutional Law, February,
2001.
86. "Complement to Chapter 6. The WinBUGS Program," in Bayesian Statistics: Principles,
Models, and Applications, Second Edition, by S. James Press, John Wiley and Sons, Inc.,
New York, 2002.
Convention Papers, other Oral Presentations:
87. Woodworth, G.G. (1983), "Analysis of a Y-Stratified Sample: The Georgia Charging and
Sentencing Study," in Proceedings of the Second Workshop on Law and Justice Statistics,
ed. Alan E. Gelfand, U.S. Department of Justice, Bureau of Justice Statistics, pp. 18-22.
88. Woodworth, G.G., Louviere, J.J. (1985), "Simplified Estimation of the MNL Choice
Model using IRLS," Contributed talk at TIMS/ORSA Marketing Science Conference at
Vanderbilt University.
89. Woodworth, G.G. (1985), "Recent Studies of Race- and Victim Effects in Capital
Sentencing," Proceedings of the Third Workshop on Law and Justice Statjsll&s, ed. G.G.
Woodworth, U.S. Department of Justice, Bureau of Justice Statistics, pp. 5&5& .; 13 r C 3
90, Woodworth, G.G., Louviere, J.J. (1988), "Nested Multinomial Logistic Choic Models .
Under Exogenous and Mixed Endogenous -Exogenous Stratification," ASA ,,{ ,#Oditigsv
the Business and Economics Statistics Section, American Statistical Assoc �tibn; • pb.1I21-'•�-
129.
91. Woodworth, G.G. (1989), "Trials of an Expert Witness;' ASA Proceedings of the Social
Science Section, American Statistical Association, pp. 143-146,
92. Kirby, R.F., Woodworth, C.H., Woodworth, G.G., Johnson A.K., (1989), "Differential
Cardiovascular Effects of Footshock and Airpuff Stressors in Wistar-Kyoto and
Spontaneously Hypertensive Rats," Societyfor Neuroscience Abstracts, 15, 274.
93. Woodworth, C.H., Kirby, R.F., Woodworth, G.G., Johnson, A.K. (1989), "Spontaneously
Hypertensive and Wistar-Kyoto Rats Show Behavioral Differences but Cardiovascular
Similarities in Tactile Startle," Society for Neuroscience Abstracts, 15, 274.
Unpublished Technical Reports and Manuscripts under Review:
94. Kadane, JB and Woodworth, GG. (1998) "Hierarchical Models for Employment
Decisions," Submitted to Journal ofthe American Statistical Association,
Archived Data:
95. Baldus, D.C., Woodworth, G.G., Pulaski C.A. (1989). "Procedural Reform Study," Inter -
University Consortium for Political and Social Research: Criminal Justice Archive,
96. Baldus, D.C., Woodworth, G.G., Pulaski C.A. (1989). "Charging and Sentencing Study,"
Inter -University Consortium for Political and Social Research: Criminal Justice Archive.
Professional Awards:
1987 Harry Kalven prize of the Law and Society Association (with David Baldus and Charles
Pulaski).
1987 Iowa Educational Research and Evaluation Association, annual award "For Excellence in
the Field of Educational Research and Evaluation for Best Educational Evaluation Study,"
(with Lary Hedges and James Shymansky).
1991 Gustavus Myers Center for the Study of Human Rights in the United States, selection of
Equal Justice and the Death Penalty as an outstanding book on the subject of human rights
(with David Baldus and Charles Pulaski).
Service Activities
Departmental Service:
University of Iowa Statistical Consulting Center:
Founder, Associate Director, Director (1973-1980)
Acting Director (1982-3)
Member of Steering Committee and Adviser (1984-present).
University Service:
Outside member of over thirty Ph.D. dissertation committees, 1973-present.
Woodworth, G.G., Lenth, R.V.L. (1982) "A Stratified Sampling Plan for Estimating
Departmental and University -Wide Administration Effort."
University of Iowa, Basic Mathematics Committee, January 1983-84.
Statistics Advisor to the University of Iowa Journal of Corporation Law, 1984-85.
University of Iowa, Research Council, 1994-87, Chairman 1986-87.
University House Advisory Committee, 1986-87.
Chairman, Political Science Review Committee, 1988-89.
Interdisciplinary Ph.D. Program in Applied Mathematical Sciences, 1988-present.
University of Iowa, Judicial Commission, 1979-81, 1990-93.
University of Iowa, Liberal Arts Faculty Assembly, 1985-87, 1995-6.
Professional Service:
NAACP Legal Defense and Education Fund, 1980-3: Statistical Analysis of the Georgia
Charging and Sentencing Study, Expert testimony in McCleskey vs. Zant (decided in the
U.S. Supreme Court).
ASA Law and Justice Statistics Committee, 1982-1987: Member of two methodological review
panels in Washington, DC. Organizer of two-day Workshop on Law and Justice Statistics,
August 1985.
ASA Visiting Lecturer Program, 1984-1988.
1984 Invited talk at Culver -Stockton College
1986 Invited talk at Moorhead State University
1988 Invited talk at Grinnell College
Invited Participant, 1984, Planning Session for Florida Capital Charging and Sentencing Study,
Florida Office of Public Defender, Richard H. Burr, Esq.
Editor, Proceedings of the Third Workshop on Law and Justice Statistics, American Statistical
Association, 1985.
Invited Panelist, 1986 Law and Society Association Annual Meeting, Panel discussion of
current state of capital sentencing research.
Invited Speaker, 1987 Seminar -Workshop on Meta -Analysis in Research, University of Puerto
Rico, San Juan, Faculty of Education, Department of Graduate Studies.
Associate Editor, Evaluation Review, 1983-1986.
Baldus, D., Woodworth, G.G., Pulaski, C.A. (1989). Oral Testimony before the U.S. Senate
Judiciary Committee (presented by D. Baldus).
Invited Participant, ASA Media Experts Program (1989).
10
Statistical Consultant to Special Master, David Baldus. State of New Jersey, Administrative
Office of Courts -- Proportionality Review System. 1989-present.
ASA Law and Justice Statistics Committee, second appointment, 1993-95.
Baldus, D., Woodworth, G.G. (1993), "An Iowa Death Penalty System in the 1990's and
Beyond: What Would it Bring?" Report submitted to the Senate Judiciary Committee, Iowa
Legislature, February 24, 1993.
Baldus, D., MacQueen, J.C., Woodworth, G.G. (1993), "An Empirically -Based Methodology
for Additur/Remittitur Review and Alternative Strategies for Rationalizing Jury Verdicts,"
Report prepared for the Research Conference on Civil Justice Reform in the 1990's.
Baldus, D.C., Woodworth G.G. (1995), "Proportionality Review and Capital Charging and
Sentencing: A Proposal for a Pilot Study," Commonwealth of Pennsylvania, Administrative
Office of Courts.
Refereeing (since 1980):
1980: Journal of the American Statistical Association
1982: Journal of Educational Statistics
1983: Journal of Statistical Computation and Simulation
Annals of Mathematical Statistics
Evaluation Review (associate editor)
1984: Transportation Research
Law and Society Review
American Journal of Mathematical and Management Sciences
Journal of Educational Statistics
Evaluation Review (associate editor)
1985: Edited Proceedings of 3rd Workshop on Law and Justice Statistics
Evaluation Review (associate editor)
1986: Psychological Bulletin
National Science Foundation
Evaluation Review (associate editor)
1987: J. Amer. Statist. Assoc.
1988: Science (ca. 1988)
1990: Annals of Otology, Rbinology & Laryngology
American Speech -Language -Hearing Association
Macmillan Publishing Company
Survey Methodology Journal
1991; international Journal of Methods in Psychiatric Research
1993: Multivariate Behavioral Research
1994: International Journal of Methods in Psychiatric Research
1995: SIAM Review
Duxbury Press
Acta Applicandae Mathematicae
1996: American Journal of Speech -Language Pathology
1998: Duxbury Press
2001: John Wiley and Sons, Inc.
2002: Addison-Wesley
11
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Extramural Consulting:
American College Testing
Allergan
Beling Consultants, Moline IL
Bettendorf Iowa AEA
Coerr Environmental, Chapel Hill
Defender Association of Philadelphia
Death Penalty Information Center
Florida State Public Defender's Office
Gas Research Institute.
Hoechst Marion Roussel / Aventis
HON Corporation
Legal Services Corporation of Iowa
Iowa State Attorney General's Office
Intramural Consulting:
Kaiser Aluminum , !-' j ; , 3:
Electric Power Research Instif ife
NAACP Legal Defense and Education Fund
National Research Council
Supreme Court of Nebraska
Pittsburgh Plate Glass
Rhone-Poullenc
Stanford Law School
StarForms
Supreme Court of New Jersey
Vigertone Ag Products
Westinghouse Learning Corporation
WMT news department
I consult almost on a weekly basis with colleagues and students throughout the University,
including at one time or another (but not limited to): Audiology, Biology, Exercise Physiology,
Geology, Law, Marketing, Nursing, Otolaryngology, Physics, Psychology, Psychiatry, Science
Education, the Iowa Driving Simulator, and the National Advanced Driving Simulator.
Expert testimony / depositions:
Robert R. Lang, Esq. (Legal Services Corporation of Iowa)
1982 Ruby vs. Deere (gender discrimination)
Mark R. Schuling, Iowa Assistant Attorney General.
1984 Burlington Northern Railroad Co. vs. Gerald D. Bair, Director (taxation)
Teresa Baustian (Iowa Asst. Atty. General - Civil Rights Division)
1988 Howard vs. Van Diest Supply Co. (age discrimination)
Walter Braud, Esq.
1988 Hollars et. al. vs. Deere & Co. et. al. (gender discrimination)
Mark W. Schwickerath, Esq.
1988 Schwickerath vs. Dome Pipeline, Inc. (effects of chemical spill)
Richard Burr, Esq.
1990 Selvage vs. State of Florida (capital sentencing)
Amanda Potterfield, Esq.
1990 Reed vs. Fox Pool Corporation (product liability)
1994 State of Iowa vs. Dalley (forensic identification via DNA)
Jerry Zimmerman, Esq.
1991 George Volk Case (age discrimination)
1993 Rasmussen vs. Rockwell (age discrimination)
1994 Hans vs. Courtaulds (age discrimination)
Thomas Diehl, Esq.
1992 State of Iowa vs. William Albert Harris (jury composition)
Diane Kutzko, Esq. (Iowa State Bar Association)
1995 Consultation on the validity of the Iowa bar exam.
John Allen, Esq.
1995 Buchholz vs. Rockwell (age discrimination)
Michael M. Lindeman, Esq.
1995 Beck vs. Koebring (age discrimination)
Timothy C. Boller, Esq.
12
1995 Larh vs. Koehring (age discrimination)
Thomas C. Verhulst
1995 Carr vs. J.C. Penny (racial discrimination)
J. Nick Badgerow, Esq.
1995 Zapata et. al., vs. IBP, Inc. (racial/national origin discrimination)
David J. Goldstein, Esq., Faegre and Benson, Minneapolis
1999 Payless Cashways, Inc. Partners v. Payless Cashways (age discrimination)
Catherine Ankenbamdt, Deputy First Assistant Wisconsin State Public Defender
2001 Civil commitment hearing of Keith Rivas (Prediction of Sexual Recidivism)
Michael B. McDonald, Assistant Florida Public Defender
2001 Frye hearing in re Actuarial Prediction of Sexual Recidivisim
Greg Bal, Assistant Iowa Public Defender
2001 Civil commitment hearing of Lanny Taute (Prediction of Sexual Recidivism)
Harley C. Erbe, Esq. Walker Law Firm, Des Moines
2002 Campbell et al. v. Amana Company (Age Discrimination)
Texas State Counsel for Offenders, Huntsville, TX
2002 Daubert hearing in re Actuarial Prediction of Sexual Recidivisim
Michael H. Bloom, Assistant Wisconsin Public Defender
2002 Detention of Morris F. Clement, Forest County Case No. 00 CI 01
(Prediction of Sexual Recidivism)
Federal Court Division, Defender Association of Philadelphia, Capital Habeas Gm(iUs Unit
2002 Petitioner Reginald Lewis (racial discrimination) - rj
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13
Iowa City Police Department
Traffic Stop Data Analysis: 2001
Presented to
The City Council of Iowa City, IA
LOA
Terry D. Edwards, J.D.
Elizabeth L. Grossi, Ph.D.
Gennaro F. Vito, Ph.D.
Angela D. West, Ph.D.
University of Louisville
Department of Justice Administration
Louisville, KY
August ig, 2002
Introduction & Overview
♦ Analysis of 9,702 contacts occurring over the 9 month period from April r-
December 3i, 20or
♦ Contract between ICPD and researchers at the University of Louisville
♦ ICPD contact sheet/MDT screens
♦ Formatted into an Excel spreadsheet, then transferred into SPSS for analysis
♦ 38 variables (driver demographics, stop information, officer badge number)
♦ Minor glitches with the data collection that were addressed as they arose, or
when they became known
Data Analyses
♦ Two levels of analysis: descriptive and multivariate
♦ Descriptive analyses provide percentages and give only a very superficial look
at the data --they describe the current state of affairs
✓ Lack igferential ability: cannot answer "why"
✓ Cannot predict events: cannot address "what if'
✓ Cannot describe relationships among variables
✓ Only a 'first step" in a thorough analysis
♦ Multivariate analyses provide an in-depth examination of the data
✓ h{ferential: can help to answer "why"
✓ Predictive: can help to predict future outcomes
✓ Can help to understand relationships and interactions between
and among variables that lead to a certain reality (as portrayed by the
percentages)
♦ CHAID: Chi -Square Automatic Interaction Detector (see attached)
✓ Examines each decision point (e.g., arrest, citation, moving violation)
✓ Determines the ability of driver demographics (age, sex, race) and other
events and characteristics to predict any particular outcome
✓ Results in a "decision tree" that orders factors related to the outcome in
order of their strength (predictive power)
✓ Outcomes of interest: i) Reason for the stop (moving violation,
equipment/registration violation)?; 2) Search conducted?; 3) Type of
search (incident to arrest, consent)?; 4) Property seized?; 5) Outcome of
stop (warning, citation, arrest)?
Results
Reason for stop?
✓ No factor (race, sex, age, residency) was a significant predictor of an
equipment/registration violation
✓ Age was the most significant predictor of a moving violation. It had
significant interactions with sex, and residency. The "base rate' for
moving violations was 68.6%. Most likely to be stopped for this reason
were those over 40 with non -Iowa registrations (84.5%). Next
most likely were those under 18 who were female (81.8%).
♦ Search conducted of driver or vehicle?
✓ No factor (race, sex, age, residency) was a significant predictor of
whether a driver or a vehicle was searched.
♦ Type of search conducted (consent or incident to arrest)?
✓ No factor (race, sex, age, residency) was a significant predictor of
whether a consent search or a search incident to arrest was conducted.
♦ Property seized?
✓ No factor (race, sex, age, residency) was a significant predictor of
whether property was seized.
♦ Outcome of stop (warning, citation, arrest)?
✓ No factor (race, sex, age, residency, reason for stop, multiple reasons for
stop, search conducted, property seized) was a significant predictor of
arrest.
✓ Whether a search was conducted was the most significant predictor
of receiving a warning. It had significant interactions with reason for
stop and residency. The "base rate" for receiving a warning was 55.5%•
Most likely to be warned were those who were NOT searched, who had
equipment/registration violations, and who had non -Iowa
registrations (77.1%).
✓ Whether a driver was stopped for an equipment/registration
violation was the most significant predictor of receiving a citation. It
had significant interactions with age and residency. The "base rate' for
receiving a citation was 38.7%. Most likely to receive a citation were those
NOT stopped for an equipment/registration violation, who were
over 30 years old and who had Iowa registrations (50.4%). Next
most likely were those NOT stopped for an equipment%registration
violation who were under iS years old (50.2%).
✓ Race was never "the factor" influential enough to be predictive
of any outcome.
The Baseline Dilemma
♦ Comparing "what is" to "what should be" is problematic.
♦ To determine "what should be" one would have to get a measure of the racial
distribution of drivers who are doing something that would make them
eligible to be stopped ("violators"). This should mirror the racial distribution
of drivers who actually are stopped ("stopped violators").
♦ Research that has attempted to measure this is flawed. Usually use speeding
as the only detectable behavior —speeding stops are a minority.
♦ Comparisons to population census data are invalid.
✓ Census figures include the entire population and the population of drivers
to be stopped is generally only of driving age (over 15);
✓ Driving populations and police stop practices fluctuate depending on
several factors (measurable and immeasurable);
✓ Ignores the fact that a significant proportion of drivers stopped are not city
residents (38% in the current study);
✓ No theory to back the belief that the population of drivers stopped should
reflect any resident population;
✓ No theory to back the belief that driving characteristics/events should be
equally distributed among populations —different groups can have
different driving patterns/characteristics (males, younger persons, etc.).
Conclusion and Recommendations
♦ These data provide no evidence that the ICPD is systematically engaging in
discriminatory stop practices. This does not preclude the possibility that any
individual officer could be using race as a factor in any individual contact
situation. This possibility is not measurable using these data and is unlikely
to be measurable in any situation.
♦ Age and sex of the drivers, along with other events related to the stop were
more predictive of stop outcomes.
♦ Recommendations have been communicated to the ICPD on an on -going
basis and steps have been taken to improve the data collection process and
the quality of the data. We are currently negotiating a second contract for a
full year of data collection (2002).
2
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Page 7
David Baldus: Good evening Mayor Lehman and member of Council. I appreciate
the opportunity to appear here tonight. (Reads statement).
Lehman: Before you ... you did ... we did give to Dr. West, I believe, a copy of
the information you gave us last night and she has agreed to respond in
writing. Did you ... and I haven't had an opportunity to study what you
gave us last night but basically what you said tonight in the report that
you gave us last night.
Baldus: This is an elaboration on what I gave you last night.
Lehman: We will receive from her a written response.
Baldus: Very well.
Lehman: And we will see to it that you get a copy.
Baldus: Okay. Very good. Thank you.
Pfab: Motion to accept correspondence.
Champion: Second.
This represents only a reasonably accurate transcription of the Iowa City City
Council meeting of August 20, 2002
#4 Page 8
Lehman: Moved by Pfab, seconded by Champion to accept correspondence.
Baldus: Thank you.
Lehman: All in favor? Opposed? Motion carries.
Kanner: So, Ernie this looks like this is an issue that probably needs some more
discussion.
Lehman: I think it will wait until we get the response and then we'll find out
whether we think it warrants more discussion. How does the Council
feel?
O'Donnell: Exactly Ernie we'll wait until we get a response
Champion: I think we should wait for the response.
Lehman: And we'll see what happens.
Kanner: I would say let's have a work session after the response so we can hear
both of these peoples give and take and obviously there's some
different perspectives.
Lehman: We'll have that opportunity when we get the response I would say.
Kanner: We'll have that.
This represents only a reasonably accurate transcription of the Iowa City City
Council meeting of August 20, 2002
�4�
REMARKS TO THE CITY COUNCIL OF IOWA CITY
David C. Baldus
George Woodworth
August 20, 2002
My name is David Baldus, 34 7`h Ave. N. and I teach at the University of Iowa
College of Law. Joining me in these remarks is George Woodworth, who teaches in the
University of Iowa Department of Statistics and Actuarial Science.
We are here this evening to address the validity and accuracy of the principal
conclusion of the empirical study on racial profiling in Iowa City traffic stops that was
recently presented to city council. The study was prepared by criminologists from the
University of Louisville.
We have an interest in the methodology and validity of this study because we
have spent much of our professional lives conducting empirical studies of the impact of
race in the criminal justice system.
The bottom line conclusion of the Louisville study is that the data "provide no
empirical evidence that the Iowa City Police Department is systematically engaging in
discriminatory stop practices."
In considering these claims, it is important to distinguish between two very
different points of decision in the process of stopping and charging motorists. First is the
threshold decision to stop a motorist. This is the core decision about which there is the
greatest public interest concerning racial profiling.
This threshold decision is followed by a series of post -stop decisions involving
searches, citations, warnings and arrests. These decisions raise important, but distinctly
secondary issues.
The "no bias" claim of the study has led many people to believe that it
definitively established that race is not a factor in the initial stop decisions. For example,
the Iowa City Press Citizen stated in an editorial this morning that the study shows that
"Iowa City police did not systematically engage in a practice of pulling over drivers
based on their skin color." The Iowa City police chief has stated on several occasions
that he is pleased with the study because it shows that race plays no role in Iowa City stop
decisions.
However, as much as we hope that the police chiefs belief is true, the traffic stop
study contains absolutely no data to support either that belief or a belief that racial bias
does play a systemic role in the process. The study simply does not address the issue.
The only analysis in the study that purports to address the stop issue merely
establishes that among the motorists who are stopped, blacks are no more likely than
whites to be charged with a moving violation. This conclusion has nothing whatever to
do with whether race played a role in the initial stop decisions. On this issue it is
important to note that one co-author of the study, Dr. Angela West, agrees with us and
has stated: "On the basis of our study, one simply cannot tell if race is a factor in the
initial decision to stop motorists."
The reason the study provides no basis for answering this question is a
fundamental flaw in its research design. It has good racial information on the motorists
who were stopped. They were 9% black. However, it contains no racial information on
the people who were not stopped. Nor does it contain racial information on the
population of stopped motorists that one would likely see if there were no racial bias in
the system. Without such a comparison population nothing definitive can be said about
the stop issue.
To understand the significance of this omission from the study's research design,
imagine that we were studying the impact of immaturity, i.e, being 16-18 years of age, on
auto accident rates and we only had information on the age distribution of the drivers
actually involved in auto accidents. Further, imagine that these data showed that 16-18
year olds were involved in 25% of the accidents. With only that information, we could
say nothing at all about the impact of driver immaturity on the risk of being in an auto
accident. To make any judgment about that issue, one would need information on the age
distribution of all drivers. If the data showed that 16-18 year olds constituted only 10%
of the licensed drivers, but were involved in 25% of the accidents, that comparison would
provide relevant evidence on the influence of immaturity on auto accident rates.
Therefore, to support an inference about the role of race in traffic stops we need
racial data on a comparison population of citizens or drivers that could then be compared
to the 9% of blacks among the motorists who were stopped.
We have no preconceived belief about what the results of a properly conducted
study would show. What we do believe it that the citizens and police force of Iowa City
deserve to have the best study possible, one that cannot be significantly challenged on
methodological grounds.
Given the flaws in the Louisville study, it is surprising that so many people
misinterpret its meaning regarding the stop issue. The reason is that the study is
profoundly misleading. This arises from the weakness of its research design, a lack of
clarity in its analysis, and a confounding of its findings about the role of race in the post -
stop decisions with the role of race in the initial stop decisions.
Because these are technical issues, we suggest that you submit the stop report to
peer review by scholars who conduct empirical studies of this type. This study has been
subjected to no peer review. Nor was the original research proposal. When co-author
Angela West was asked about a peer review of the traffic study, she replied that peer
review was not needed because the study was not going to be published in a scholarly
journal. In our judgment, there is a far greater need for peer review of a study that is
offered up, like the traffic stop study has been, as a basis for public action on an
2
important and sensitive political issue, than there is for peer review of a study
published in a scholarly journal, which is unlikely to have any impact on important
issues.
We thank you for the opportunity to appear this evening and will be pleased to
answer any questions you may have.
3
QTW
u.Jrr1rsF1]- TrQk1TTV
dare to be great
August 27, 2002
Chief R.J. Winkelhake
Iowa City Police Department
410 E. Washington St.
Iowa City, IA 52240
Dear Chief Chief Winkelhake,
IP4
■ DEPARTMENT OF
IUSTICE ADMINISTRATION
College of Ads and Sciences
Univen:ity of Louisville
Louisville, Kentucky a0292
Office: 502-852 6567
Fax: 502-852-0065
Enclosed is a hard copy of the fax that I sent you earlier containing our response to the Baldus
and Woodworth critique.
Please make copies and distribute as you see fit. However, please note that the actual model (of
abiders and violators) may only be used with my permission. I am actually publishing a revised
version of this letter in an upcoming issue of the Journal of Forensic Psychology Practice, as part
of a debate on measurement issues related to traffic stop practices.
I look forward to hearing what the City Council thinks of the response. Of course, I also am
interested in Baldus and Woodworth's reaction, as well.
It has been a pleasure working with you and we are looking forward to the second year, as well.
If you have any questions or concerns, please do not hesitate to contact me or any other member
of the research team
Sincerely,/ / , 1
ed—
Angela D. West, PhD.
_.�.S rdIOULSMLLE
dare to be great
August 22, 2002
City Council of Iowa City
410 E. Washington St.
Iowa City, IA 52240
■ DEPARTMENT OF
IUSTICE ADMINISTRATION
College of Arts and Sciences
University of Loulsville
Louisville, Kentucky 40292
Office: 502-852.6567
Fax: 502-852-0065
Re: 1) Letter from David Baldus and George Woodworth dated August 18, 2002 and
presented to the City Council work group meeting on August 19, 2002; and 2) written
memo from Baldus' appearance before the City Council formal meeting on August 20
Dear City Council Members:
1 am writing as requested to address the concerns raised by Professor Baldus and Dr.
Woodworth regarding the methodology and conclusions in our study of the ICPD traffic
stops.
The critique from Baldus and Woodworth claims that: 1) our study "fails to establish that
there is no systemic discrimination in ICPD stop practices;" and 2) that our study is
"incapable of answering that question one way or another" (p. 1, pare. 3). Their critique
is based primarily on "esoteric methodological issues" (p. 1).
The "Decision" to Stop/Reason for the Stop
They correctly assert that our study examined two principal points of interest. However,
the authors have /neolfecdy identified one of those two points as "the decision to stop."
The correct delineation would be "the reason for the stop." The difference is subtle but
crucial. We cannot measure an officer's decision to stop any particular vehicle except by
the reason that he or she provides. That is, to measure why an officer stops a vehicle,
we must rely on the reason that the officer provides on the contact report. That may or
may not be the real reason that the officer decided to stop the vehicle. Decision -making
is an internal process that is not available for measurement on a form; the reason for the
stop, on the other hand, is measurable. Officers may engage in biased decision -making
processes, but be able to translate those biased decisions into valid reasons for a
particular stop. If Baldus and Woodworth have devised a mind -reading method to
determine why an officer decides to stop any particular vehicle, or if they find officers
willing to indicate on a data collection form that the reason for the stop was "color of
driver's skin,° we will be more than happy to employ either of those methods in any
future studies. Short of that, we can only go by what the officer indicates on the form.
Reasons for the stop primarily involved moving violations and equipment/registration
violations. Again, Baldus and Woodworth Inconecdy state that our analysis of being
stopped for a moving violation was the "sum and substance of the 'multivariate' results
bearing on the stop issue' (p. 2). We also analyzed being stopped for
equipment/registration violations, other violations, pre-existing knowledge, criminal
offense, special detail, and other (see p. 20 of the full report). However, being stopped
for a moving violation was the only event that had significantly related predictors (age
was the primary predictor).
The second area to which Baldus and Woodworth refer involves the "post -stop
decisions" (p. 4). Again, the authors Incomectlystate that our study "analyzes
thoroughly only two of those decisions — who received warnings and who received
citations' (p. 4, para. 3). On page 20 of our full report, we explain that we conducted full
CHAID analysis on all the 'post -stop decisions' (having a vehicle or driver search
conducted, being searched incident to arrest or by consent, having property seized, and
stop outcome—waming, citation, arrest). Only two of those events (receiving a warning
and receiving a citation) had significant predictors. For the events with no significant
predictors, no further discussion was necessary.
Comparison Grouo/Baseline Dilemma
It seems that the primary argument Baktus and Woodworth have pertains to the lack of a
comparison group. They emphasize the importance of knowing the "proportion of
minorities among all the drivers who could have been stopped but were not" (p.2, para.
3). This comment is qualitatively and quantitatively different from their earlier claim (p. 2,
para. 1) that "to test the extent to which race may be a systemic factor in the exercise of
officer discretion to stop motorists, one would ideally have information on the racial
characteristics of the people who were not stopped. This would enable us to compare
the racial composition of those stopped with those who were not stopped' (p. 2). Drivers
who were not stopped are different from drivers who could have been stopped but were
not.
It is here where I must distinguish between two subpopulations of drivers. As indicated
by the graph attached as Figure 1, at any given time on any given day at any given
location under any given set of circumstances, there is a population of drivers ("All
Drivers"). That population of drivers can be divided into two mutually exclusive
categories:
Subpopulation #1: "Ab/ders"
Abiders are drivers generally "not eligible to be stopped' because they are not
doing anything illegal or anything that would otherwise bring them to the attention
of law enforcement. Abiders should not be stopped by the police.
11••T'•T!17F1CL>E:17�1'i7.7fF.'Ti'ir
Violators are drivers "eligible to be stopped" because they are doing something
that brings them to the attention of law enforcement (weaving, improper lane
changes, failure to signal, speeding, reckless driving, expired plates, inoperable
equipment, etc.). Violators should be stopped by the police. By all accounts,
this subpopulation consists of the majority of drivers (most drivers could be
considered violators, primarily for speeding). In fad, a survey by the National
Highway Traffic Safety Administration (NHSTA) found that 84% of surveyed
drivers reported seeing speeding or other unsafe driving all or most of the time
(NHSTA Traffic Tech 186, 1999).
One can divide abiders and violators into drivers "stopped" and "not stopped."
Theoretically, "stopped' drivers should consist only of violators. 'Not stopped" drivers,
on the other hand, include both abiders and violators; abiders should not be stopped and
it is impossible to stop all violators.
The job of law enforcement is to stop the violators (and conversely, not stop the
abiders). But given that the violators are so numerous, law enforcement officers use
their discretionary powers in determining who to stop and who to not stop. As a result,
many violators are not stopped and abiders sometimes are (see Figure 2).
In this model, there are four possible combinations of outcomes —two of which involve
the possibility of discriminatory practices:
1) Abiders who are not stopped
Abiders should not be stopped and are not = No discrimination.
2) Abiders who are stopped
Abiders should not be stopped and are = Possible discrimination
3) Violators who are stopped
Violators should be stopped and are = No discrimination
4) Violators who are not stopped
Violators should be stopped and are not = Possible discrimination
Therefore, there are two potential sources of discrimination in police stop practices,
stopping drivers who should not be stopped, and not stopping drivers who should be
stopped (outcomes #2 & #4 above). It is interesting to note that research efforts to date
have focused on scenario #3—violators who are stopped --given that the only
information we have is from traffic stops. Theoretically, this is not a potential source of
discrimination since an officer must give a valid reason for stopping any vehicle (and so
the driver, per se, is a violator and should have been stopped). Whether the driver
believes the stop is valid is another question.
The real focus of inquiry should be on outcomes #2 and #4.
Those who say that police °racially profile" would seem to have two main contentions:
1) minority abiders are wrongfully stopped at higher rates than white abiders
(and conversely, white abiders are rightfully not stopped at higher rates than
minority abiders). In this case, the minority driver is alleging that he or she is
stopped ONLY because of their skin color. They were not doing anything that
would make them eligible to be stopped as a violator;
2) minority violators are rightfully stopped at higher rates than white violators
(and conversely, white violators are wrongfully not stopped at higher rates
than minority violators). In this case, the minority driver ackraMedges that
he or she was doing something that would make them eligible to be stopped
(they are a violator), but law enforcement disproportionately targets them
rather than white violators.
For the first contention, the most appropriate comparison group would be the
subpopulation of abiders who were not stopped. The racial distribution of abiders not
stopped should approximate the racial distribution of abiders who were stopped. That
would mean that law enforcement was stopping people of both races equally when they
should not have been stopped. Stopping people who should not be stopped is a
problem in itself and would need to be addressed by management. Proving that a
person should not have been stopped, however, is problematic, given that law
enforcement officers have at their disposal a wide variety of reasons for making a traffic
stop.
For the second contention, the most appropriate comparison group would be the
subpopulation of violators who were not stopped. The racial distribution of violators not
stopped should approximate the racial distribution of violators who were stopped. This
would mean that law enforcement was stopping people of both races equally when they
should have been stopped.
For both of these situations, the problem Iles in determining the racial distribution of
abiders and violators who were not stopped. Currently, there /s no measure of the
racial distribution of who is not stopped by the police. Obtaining data on who is not
stopped is similar to obtaining data on crimes that are not reported, what criminologists
call the 'dark figure of crime." Although victimization and self -report studies attempt to
measure this, each method has serious methodological issues that severely limit the
validity of crime data it measures. Again, if Baldus and Woodworth have devised a
method to do this, we would be more than happy to use it in any future analyses.
Finally, these populations and subpopulations are constantly changing depending on the
time of day, the day of week, the week, the month, the season, the weather, social
events, location, and many more fedora. With such constantly changing populations,
how can one devise valid measures of their characteristics? For example, think of the
population driving on Sunday at 9:30 am. Are those drivers different from drivers on
Sunday at 1:30 am? In Iowa City, 41% of the stops were between midnight and 3:00
am. Baldus and Woodworth must recognize that drivers on the road at this time are
unlikely to reflect the population of Iowa City, in general. What if we are examining a
stretch of roadway that cuts through an Hispanic neighborhood and there is a Latino
festival being held? There will be more Hispanic drivers than normal in that particular
population.
As to the validity of making condusions without comparison populations, we believe it
preferable to use the current data as a basis for comparison rather than make invalid
comparisons to poorly devised proxy measures. The study is to be used as a
management tool, in conjunction with other measures of police performance, including
citizen satisfaction surveys, complaints, reports of excessive force, etc. The data and
statistical analyses cannot substitute for good police -community relations, but primarily
serve as an additional way for law enforcement agencies to measure their performance
in this area.
Moreover, we believe that the CHAID analysis provides the baseline from which the data
can be evaluated. We have data pertaining to the populaffon of stops. Each event can
be computed as to its likelihood for the entire group, then sub -group comparisons can be
made to that figure. For example, the base rate for receiving a moving violation was
68.6% for the population. This means that, in the entire population of stops, 68.6% of the
stops were made for a moving violation. CHAID determines whether any group
(determined by race, sex, age, residency, etc.) received moving violations at significantly
higher rates than the base rate for the entire population. In this case, certain age groups
of drivers (younger) were at significantly higher risk than others. This means that age is
a significant factor in whether a person receives a moving violation. The NHSTA has
found that, indeed, younger drivers report the highest levels of driving through stop signs
without slowing, weaving back and forth between lanes, tailgating, driving through red
fights, making an angry or obscene gesture or comment, cutting off another car, and
driving under the influence (NHSTA Traffic Tech 186, 1999). These are all things that
could result in moving violations.
I prefer not to address the study to which Baldus and Woodworth refer in their critique,
specifically because 1 do not know how that data was collected, for what purpose it was
collected, or even from what time period it was collected. Although Baldus and
Woodworth state that, `in 1998, we conducted such an analysis of the Iowa City stop
data that were available at that time' (p. 2, pars. 4), the attached charts refer to a time
period from 8/1 /99 — 4/10/00. 1 am not clear how they could analyze in 1998 data from
1999-2000. Moreover, they used very basic descriptive statistics failing to control for
other variables that may have impacted the stop, examined only 2 variables (race and
time of stop), used comparison data nearly 10 years old, and compared to the
city/county population without having a measure of the proportion of stopped drivers
actually from the city/county.
This last problem remains one of the most compelling reasons for researchers not to
compare the demographics of stopped drivers to the demographics of any resident
population —in Iowa City, 38% of the stopped drivers were not residents of Iowa City,
and 27% were not residents of either the city or Johnson County. This has held true in
other studies we have conducted and makes any type of locally based, demographic
proxy totally invalid.
Descriptive versus Inferential Data
As for Baldus and Woodworth's discussion of our "raw data' (p. 3), one cannot examine
the percentages and make inferences about relationships among variables. Descriptive
statistics are not inferential. Regarding point c. on page 4., Baldus and Woodworth
make a good argument. It may be that drivers are being stopped for pre4extual
reasons, resulting in an increased likelihood of warning and a reduced likelihood of
citation. It also may be that officers are aware that their outcome decisions are under
scrutiny and fear being accused of profiling, so they are more likely to release Black
drivers with a warning.
Officer interviews, however, indicate that decisions to cite or to wam may depend to
some degree on the perceived socioeconomic status of the driver. Interviewed officers
said that they were reluctant to issue a citation to a driver who they thought might have a
problem paying the cost of the ticket (i.e., poorer drivers). One officer said that he
considers whether the ticket might result in the person failing to pay, having a bench
warrant issued for their arrest, being arrested, missing work, and enduring even greater
financial hardship (personal interview, August 20, 2002). On the other hand, officers
may be more likely to issue citations to drivers who look like the cost of a traffic citation
would pose no undue financial hardship (i.e., wealthier drivers). In the case of many
cities, those who are in positions of greatest socioeconomic need are the minority
populations. This may result in minority drivers being warned more often and white
drivers being cited more often. Of course, there are several other possible explanations.
In their "remarks to the city council" dated August 20, 2002, Baldus and Woodworth
provide an example to illustrate the signifiicant'flaw" in our research design. They say
that, to determine the impact of immaturity on auto accident rates, one would need to
have the rate at which immature persons were involved in accidents C16-18 year olds
were involved in 25% of the accidents," page 2, para. 2) AND the proportion of immature
persons among all licensed drivers ("16-18 year olds constituted only 10% of the
licensed drivers"). They claim that, because '16-18 year olds constituted only 10% of
the licensed drivers, but were involved in 25% of the accidents, that comparison would
provide relevant evidence on the influence of immaturity on auto accident rates' (p. 2,
pare. 2).
There are several problems with this argument. First, one would need to know what
percentage of the 16-18 year old licensed drivers is actually driving? If a greater
proportion of 16-18 year olds is driving on the highways, one would expect their accident
rates to be higher. How often were these drivers driving? If 16-18 year old drivers spent
more time on the roadways than drivers of other ages, one would expect their accident
rates to be higher. How far are these drivers driving? If 16-18 year old drivers are
driving greater distances than drivers of other ages, one would expect their accident
rates to be higher. This is the impact of multiple variables on an outcome.
Using Baldus and Woodworth's own argument, one should know the proportion of 16.18
year old drivers in the population having accidents compared to the proportion not
having accidents. Then, this proportion would be compared to figures from other age
groups. If 16-18 year olds represent only 10% of licensed drivers, the implication is that
they should only be involved in 10% of the accidents. This is faulty logic at its best. The
proper comparison should be to compare the percentage of 16-18 year old licensed
drivers having accidents to the percentages of other age groups having accidents,
not what proportion of the driving population they are, or in what percentage of accidents
they are involved. We also would be interested in the inverse —the percentage of
licensed 16-18 year olds not having accidents compared to the percentages of other
ages not having accidents.
If comparing percentages is all the analysis that is required to make conclusions, then
much effort is wasted by researchers who conduct muMvariate analyses to make
inferential conclusions about the relationships among variables. In fad, Professor
Baldus has wasted a great deal of time and effort in his own research on racial
disparities in capital punishment. All he needed to have said was that, since Blacks
comprised only 10% of a state's population, but were 25% of those sentenced to death,
there was racial disparity in the sentencing. This, of course, fails to account for a
defendant's prior criminal history, severity of the crime, and several other Important
factors that might have an impact on a person's sentence.
Current research efforts have recognized the inadequacies of population comparisons.
In fact, in a review of 13 published studies on the traffic stop practices of various law
enforcement agencies across the country between 1996 and 2001, Engel, Calnon, and
Bernard (2002) argue Mat 'the mere presence of disparity in the aggregate rate of stops
does not, in itself, demonstrate racial prejudice, any more than racial disparity in prison
populations demonstrates racial prejudice by sentencing judges" (p. 250).
In addition, a recent publication by the Bureau of Justice Statistics concluded that racial
differences in percentages of drivers stopped by the police 'are not necessarily evidence
that police use race as a factor in deciding whether to make a traffic stop —that is, not
necessarily evidence of 'racial profiling" (Langan, Greenfekl, Smith, Durose, and Levin,
2001, p. 13). Although a national survey indicated that black drivers in 1999 'had higher
chances than whites of being stopped at least once and higher chances than whites of
being stopped more than once ... to form evidence of racial profiling, the survey would
have to show that (ail other things being equal), blacks were no more likely than whites
to violate traffic laws, and police pulled over blacks at a higher rate than whites' (p. 13,
italics in the original). Currently, we are unable to measure racial differences in the
breaking of traffic laws (or the racial distribution of violators on the roadways).
It is apparent that Baldus and Woodworth realize the inadequacies of merely comparing
percentages. Professor Baldus has published numerous studies examining the impact
of race on various events and always has conducted fairly sophisticated multivariste
analyses to reach his conclusions. It is curious that, when it came to their study of Iowa
City traffic stop data, however, they were reluctant to do so. Perhaps they realized that
the impact of race would be negated by other factors, such as driver sex and age. A
conclusion of no racial bias could be damaging to researchers who have made careers
and political allies by finding racial disparities.
Finally, in response to the rest of the critique on page 4, 1 am not dear as to the
implications of their arguments. For example, they state that blacks are mover -
represented' among consent searches, searches incident to arrest, and arrests. I am
not sure what they mean by over -represented. Why, for example, would a group of
people be arrested in proportion to their representation in the population? What theory
can Baldus and Woodworth present that supports their implication that arrests should be
distributed in proportion to any representation? Many events during a traffic stop result
from preceding events. For example, property is often seized AFTER a search. These
types of events are not independent, and they are not evenly distributed, but occur
based on events that have previously transpired. Some events may be related to things
that were not measured in the current study. For example, a driver may be arrested
after the officer conducts a warrant check that results in notification of an outstanding
warrant for that driver.
In conclusion, I am perplexed as to how Baldus and Woodworth can critique our study
on the "limited scope of the methodology' (p. 5, para. 1), especially given that their own
study was purely descriptive and only included 2 variables. We used multivariate
techniques and 38+ variables. We are left to wonder if Baldus and Woodworth, or any
other critics of our methodology, would be leveling the same criticisms if race had
emerged as a significant predictor of events and we had concluded that the Iowa City
Police were engaged in discriminatory stop practices. We believe we would not have
heard a single objection to the methodology if that had been the conclusion.
Peer Review
This was a technical report, prepared for agency use. it was not prepared for publication
or peer review in its current form. We recognize that a scholarly publication of this study
would require significant revision to its format and to Re content. We also recognize that
the publication process involves peer review, and when we get to that point, we will
certainly ask for it and use whatever recommendations are made to improve the
presentation of the study.
Moreover, we have presented our research design and methodology at several
professional conferences (Academy of Criminal Justice Sciences, American Society of
Criminology, Southern Criminal Justice Association) where peers have had the
opportunity to review and provide feedback. Our overall experience at these
conferences is that others conducting research in this area are impressed with the scope
of our inquiry and the depth of our analyses.
We understand and value the role of peer review. Unsolicited peer review, however, of
the type that Baldus and Woodworth provide not only is unprofessional, but also is
insulting. I, or any of my colleagues at the University of Louisville who worked on this
study, would have been more than happy to speak with Baldus and Woodworth and to
address any concerns in a more private forum. The type of last minute presentation that
occurred at the Council's work group meeting on August 19 is more akin to guerilla
warfare in defense of personal and political agendas than to legitimate professional
critique.
I am happy to have been able to represent my research team and the University of
Louisville. I appreciate the willingness of the City Council to allow me to present and
clarify the study that we conducted, and to present this written response to Baldus and
Woodworth's critique. However, this shall be the last I speak or write as far as
responses are concerned.
You all should be proud that Iowa City has a proactive police department that was willing
to examine its stop practices before there were any problems. This is not often the case.
I have continued to be impressed with the professionalism and the dedication to
community service that I have seen exhibited by Chief Winkelhake and the other
members of the Iowa City Police Department.
If I can be of further assistance to you or I can clarify any of the material presented in
this letter, please do not hesitate to call me. I have made business cards with my
contact information available for your convenience. I also look forward to continuing our
work with the ICPD and with the City of Iowa City in studying another year of traffic
stops.
Sin ly,
�. t�l .
Ange a ID West, Ph.D.
University of Louisville
Department of Justice Administration
Brigman Hall, 2nd Floor
Louisville, KY 40292
References
Engel, R.S., Calnon, J.M., and Bernard, T.J. (2002). Theory and racial profiling:
Shortcomings and future directions in research. Justice Quarterly, ] 9 (2), 249-
274.
Langan, P.A., Greenfeld, L.A.,
1
S.K., Durose, M.R., and Levin, D.J.
National Highway Traffic Safety Administration (1999). Speeding and aggressive driving
documented in national telephone survey. Traffic Tech Number 186. Washington,
D.C.: U.S. Department of Transportation.
10
Fiigure 1: All Drivers
i Violk(�
Drivers
Figure 2: Stopped Drivers Among All Drivers
RIONESOM
Abiders not stopped (no discrim.) Abiders stopped (possible discrim.)
Violators stopped (no discrim.) %� Violators not stopped (possible discrim.)
October 8, 2002
To: City Council of Iowa City
From: David Baldus and George Woodworth
Re: Iowa City Police Stop Study by Angela West, University of Louisville
We appreciate your sending us Angela D. West's August 27, 2002 letter to Chief
Winkelhake responding to our August 18 and 20, 2002 critiques of her police stop study.
Dr. West's letter presents a number of reasons why the conduct of police stop
studies is difficult. However, her letter does not in anyway question our assertion that her
June 13, 2002 study - "Traffic Stop Practices of the Iowa City Police Department: April
1- December 31, 2001 "- simply does not address the issue of whether race was a factor in
the initial traffic stops. This is also what she said in her remarks to city council at its
August 19, 2002 work session, i.e., "On the basis of our study, one simply cannot tell if
race is a factor in the initial decision to stop motorists."
It is for this reason that the claim of the police chief and others that the Louisville
study demonstrates that race is not a factor in the initial decisions to stop motorists has no
support whatever in that study and is a complete misrepresentation of what it does
contain.
Submitted by Vice -Chair
Loren Horton
Profiling
report
pleases
police
Data show
officers don't
discriminate
By Vanessa Miller
Iowa City Press -Citizen
The Iowa City Police
Department did not engage
in discriminatory profiling
during traffic stops in 2001,
according to study results
released Thursday.
The study is based on
information collected from
every traffic -related contact
that occurred in the city
from April 1 to Dec. 31, 2001.
Researchers from the
University of Louisville com-
piled the results of the study
comprised of information on
the driver, police officer and
the stop event. Driver demo-
graphics noted in the study
are race, sex, age, residency
and vehicle registration.
Only the badge number is
recorded for each officer.
Stop event information
includes date, time, reason
for stop, if a search was con-
ducted, details on any prop-
erty seized, force used and
the outcome of the stop.
"The data showed that no
systematic action was taken
by the police department
based on race or much of
anything for that matter,"
Iowa City Police
traffic stops
April I. Dec. 31, 2001
Male Female
65%I_ 35%
rj
R.J. Wudcelhake. "That does
not mean, however, that any
one individual could not
have been sub to this
activity by a,
in dual Offi-
cer. So we need to continue
to be conscious."
Based on data recorded
from 9,702 interactions
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Iowa City Press -Citizen: Opinion
Page I of 2
Tuesday, August 20, 2002
Continue traffic -stop monitoring
We're glad the Iowa City Police Department plans to
continue studying its traffic -stop data to determine if race
plays a role in pulling over drivers.
Although studies like this cost money,
and initial data indicates there is no
THE ISSUE:
correlation between Iowa City traffic
Iowa City Police get
stops and a driver's race, it's an
an initial thumbs -up in
exercise worth having and continuing.
preliminary analysis
of traffic -stop data,
An initial study of eight -months' worth
which was analyzed
of traffic data, from April I to Dec. 31,
for possible racial
2001, shows that Iowa City police did
profiling.
not systematically engage in a practice
of pulling over drivers based on their
WE SUGGEST:
skin color.
Continued data
collection and
The details of that analysis can be
analysis, and better
tricky to interpret, however.
record -keeping, will
make the data more
For instance, of more than 9,000
useful.
recorded cases in which police were
involved in a traffic -related contacts, 84 percent of people pulled over
were white.
In comparison, Iowa City's population is 87.3 percent white; Johnson
County's is 90.1 percent white.
According to the study's authors, from the University of Louisville, it
means very little to compare the number of blacks cited in traffic
stops to the number of blacks in the local population, however.
The study's writers say we should instead be comparing the citation
statistics to the number of blacks or non -white people in the pool of
drivers eligible to be pulled over.
That's what makes the continuation
of this data -collection analysis so
important. The first round of data
collection creates a baseline to
which we can compare data yet to
be collected.
Police Chief R.J. Winkelhake says
he is pleased by the findings, as he
should be.
"The data showed that no systematic
action was taken by the police
department based on race or much
of anything for that matter," he said.
"That does not mean, however, that
What do
you think?
• Should the police
continue to track
trafficstop data?
• Send your comments
to Opinion Page, P.O.
Box 2480, Iowa City,
Iowa 52244; fax to (319)
834-1083, e-mail to
opinion@ press-
citizen.com.
Submitted by Vice -Chair
Loren Horton
http://www.press-citizen.com/opinion/pceditorials/staffeditO82002.htm 9/11/02
Iowa City Press -Citizen: Opinion Page 2 of 2
any one individual could not have been subject to this activity
(profiling) by an individual officer. So we need to be conscious."
We agree.
If these statistics are a true reflection of reality, the department should
keep up the good work.
However, the next step is to collect a year's worth of "clean
data" (without inputting errors), as the study writers suggest.
As Winkelhake and the study's authors both suggest statistical
analysis alone will not reveal whether any individual officer is
engaging in racial profiling.
Administrative supervision and community oversight is best suited to
ferret that out.
In 1999, Iowa City Police were the first in the state, and among the
first in the country, to voluntarily begin collecting this data.
Let's not lose that momentum.
This is one of those cases where good enough is not going to be
enough.
http://www.press-citizen.com/opinion/peeditorials/staffedit082002.htm 9/11/02