Developing an appropriate benchmark to estimate the population of individuals exposed to police observation is the most important methodological consideration in racial profiling research. This article compares data on stops generated by police officers using the three most common benchmarks in racial profiling research—population, field observations, and accident records—and finds only slight differences in the results. In addition, the article outlines the relative advantages and disadvantages of each benchmark and offers suggestions to future researchers.
Get full access to this article
View all access options for this article.
References
1.
AlpertG.P.SmithM.R., & DunhamR.G. (2003, March). Toward a better benchmark: Assessing the utility of not-at-fault traffic crash data in racial profiling research. Paper presented at Confronting Racial Profiling in the 21st Century, Northeastern University, Institute on Race and Justice, Boston, MA.
2.
California Highway Patrol. (2003). Public contact demographic data summary. Sacramento, CA: California Highway Patrol.
3.
CarrB. R. (1969). A statistical analysis of rural Ontario traffic accidents using induced exposure data. Accident Analysis and Prevention1 (4), 343–357.
4.
CoxS. M.PeaseS. E.MillerD. S., & TysonC. B. (2001). Interim report on traffic stops statistics for the state of Connecticut. Rocky Hill, CT: Division of Criminal Justice, Office of Chief State's Attorney.
5.
DavisG. A., & GaoY. (1993). Statistical methods to support induced exposure analysis of traffic accident data. Transportation Research Record, 1401, 43–49.
6.
DeYoungD. J.PeckR. C., & HelanderC. J. (1997). Estimating the exposure and fatal crash rates of suspended/revoked and unlicensed drivers in California. Accident Analysis and Prevention29 (1), 17–23.
7.
GarberN., & SrinivasanR. (1991). Characteristics of accidents involving elderly drivers at intersections. Transportation Research Record, 1325, 8–16.
8.
GreenwaldH. P. (2003). Police vehicle stops in Sacramento, California (second report). Sacramento, CA: City of Sacramento.
9.
HaightF. A. (1970). A crude framework for bypassing exposure. Journal of Safety Research2 (1), 26–29.
10.
KurodaK. (1984). Safety impacts of vehicle design and highway geometry. Unpublished doctoral dissertation, Department of Civil and Environmental Engineering, Michigan State University.
11.
LamberthJ. (1996). Revised statistical analysis of the incidence of police stops and arrests of black drivers/travelers on the New Jersey Turnpike between exits or interchanges 1 and 3 for the years 1988–1991. Retrieved July 12, 1999, from the http://www.aclu.org.org/profiling/index.html.
12.
LangeJ. E.BlackmanK. O., & JohnsonM. B. (2001). Speed violation survey of the New Jersey Turnpike: Final report. Trenton, NJ: Office of the Attorney General.
13.
LylesR. W.StamatiadisP.BlowerD., & CampbellK. (1990). The Michigan heavy stuck study: Final report. East Lansing, MI: College of Engineering, Michigan State University.
14.
LylesR. W.StamatiadisP., & LighthizerD. R. (1991). Quasi-induced exposure revisited. Accident Analysis and Prevention23 (4), 275–285.
15.
MaleckT., & HammerH. (1987). Driver age and highway safety. Transportation Research Record, 1059, 6–12.
16.
McKelveyF. X., & StamatiadisN. (1988). Final report: A study of the relationship between older drivers and highway safety in the state of Michigan. East Lansing, MI: College of Engineering, Michigan State University.
17.
Minnesota Department of Public Safety. (2003). Minnesota racial profiling study: All jurisdictions report. Retrieved November 11, 2003 from http://mnclu.org/nr_racial_profiling.html.
18.
MooseC. A. (2002). Traffic stop data collection analysis (fourth report). Montgomery County, MD: Montgomery County Department of Police.
19.
Police Foundation. (2003). A multijurisdictional assessment of traffic enforcement data collection in Kansas. Washington, DC: Author.
20.
PolusA.HochermanI., & EfratE. (1988). Evaluation of the accident rates of male and female drivers. Transportation Research Record, 1172, 42–46.
21.
RojekJ.RosenfeldR., & DeckerS. (2004). The influence of driver's race on traffic stops in Missouri. Police Quarterly7 (1), 126–147.
22.
Soto v. New Jersey, 734 A.2d 350 (Supreme Court of New Jersey 1996).
23.
StamatiadisN., & DeaconJ. A. (1995). Trends in highway safety: Effects of an aging population on accident propensity. Accident Analysis and Prevention27 (4), 443–459.
24.
StamatiadisN., & DeaconJ. A. (1997). Quasi-induced exposure: Methodology and insight. Accident Analysis and Prevention29 (1), 37–52.
25.
StaplinL., & LylesR. R. (1991). Age difference in motion perception and specific traffic maneuvers problems. Transportation Research Record, 1325, 23–33.
26.
Texas Department of Public Safety. (2002). Traffic stop data report. Austin, TX: Texas Department of Public Safety.
27.
ThorpeJ. T. (1967). Calculating relative involvement rates in accidents without determining exposure. Traffic Safety Research Review11 (1), 3–8.
28.
Washington State Patrol. (2001). Report to the legislature on routine traffic stop data. Olympia, WA: Washington State Patrol.
29.
WhitmanG. R. (2002). Denver Police Department contact card analysis. Denver, CO: City and County Department of Public Safety.
30.
Wilkins v. Maryland State Police, Civil action No. CCB-93-483 (Maryland Federal District Court 1993).
31.
WithrowB. L. (2001). The Wichita stop study: Initial analysis. Wichita, KS: Midwest Criminal Justice Institute, Wichita State University.
32.
WithrowB. L. (2002). The Wichita stop study: A beat level analysis. Wichita, KS: Midwest Criminal Justice Institute, Wichita State University.
33.
ZingraffM. T.MasonH. M.SmithW. R.Timoskovic-DaveyP.WarrenP.McMurrayH. L., & FenlonC. R. (2000). Evaluating North Carolina State Highway Patrol data: Citations, warnings and searches in 1998. Retrieved November 12, 2000 from http://www.nccrimecontrol.org/shp/ncshreport.html.
