From the Officer’s Perspective: A Multilevel Examination of Citizens’ Demeanor during Traffic Stops

Abstract

Over the past 60 years, a substantial body of research has considered the influence of citizens’ demeanor on police behavior; and more recently, the correlates of citizens’ demeanor. This study advances our understanding of the demeanor construct by measuring officers’ perceptions of citizens’ disrespect, non‐compliance, and resistance during traffic stops. Using multilevel statistical models, we examine the correlates of citizens’ demeanor and assess the racial differences in these perceptions. The findings demonstrate that officers’ perceptions of citizens’ demeanor vary across racial/ethnic groups, after controlling for other relevant factors. Although White officers were significantly more likely than Black officers to classify drivers as disrespectful, Black and White officers were equally likely to report drivers as displaying behaviors that were non‐compliant and/or verbally resistant. Black drivers were significantly more likely to be reported as disrespectful, non‐compliant, and/or resistant, regardless of the officers’ race. The implications for future research and policy are discussed.

Keywords:

• policing
• demeanor
• race
• traffic stops

Acknowledgments

This research was supported by funding from the City of Cleveland and data were provided by the Cleveland Division of Police. The findings are those of the authors and do not necessarily represent the official positions of the City of Cleveland or the Cleveland Division of Police.

Notes

1. Anecdotal support for the discrepancy between observers’ perceptions and officers’ perceptions was documented during data collection for the Project on Policing Neighborhoods (POPN). In a narrative written about an encounter, the observer (Engel) noted that she perceived the citizens involved in the encounter had displayed clear disrespect toward the officer (and were therefore coded as disrespectful on the data collection form). While debriefing the officer, however, Engel asked the officer if he/she perceived the behavior displayed by the participants as disrespectful; the officer indicated he/she did not perceive the behavior as disrespectful because that was typical behavior for the types of people who resided in that neighborhood.

2. Specifically, the traffic stop form collected information on the: (1) stop (e.g., date/time, location, type of roadway, reasons for the stop, and the duration of the stop); (2) driver (e.g., gender, age, race/ethnicity, zip code of residency, and demeanor); (3) vehicle (e.g., condition of the vehicle, modifications, state of registration, and number of passengers); (4) outcome (e.g., citation, written warning, arrest, search, and contraband seized); and (5) identification information (e.g., location of the stop by zone, officers’ badge number, unit number, and district number). Officers collected information on Scantron forms that were sent directly to the research team for compilation. The data collection form was initially pilot‐tested during a one‐month period with all officers from the traffic unit. Based on this pilot test, adjustments were made to the form and the training provided to officers. A second pilot test was conducted for two months with the full department, and biweekly status reports were provided to district commanders regarding the accuracy of the data throughout the full data collection period (see Engel et al., 2006 for additional details regarding the data collection process).

3. Information was recorded only on officer‐initiated traffic stops; stops based on citizens’ initiation or as the result of police check‐points (e.g., registration, DUI, seat belts, etc.) were not included in the data. Contacts with citizens resulting from traffic accidents were also excluded from the data collection effort.

4. This had a number of important implications on the analyses, findings, and conclusions of the larger study that are fully reported in Engel et al. (2006). For purposes of the current study, however, there is less concern that this limitation significantly impacted the results. If we consider the Cleveland data as capturing all traffic stops deemed “serious” enough by officers to warrant official action, we are in position to better understand the factors that may lead to citizens’ displays of disrespect during these types of traffic encounters. It is unknown what impact citizens’ demeanor had on the (unknown) number of traffic stops that resulted in verbal warnings or no official police action. This is a limitation of our data that is more thoroughly discussed in the discussion section.

5. All officers who reported more than 30 traffic stops were included in this study. An examination of the excluded traffic stops indicated no systematic pattern or selection bias at Level 1. Officer characteristics differed slightly between the full dataset (N = 698) and the multilevel dataset based on at least 30 traffic stops per officer (N = 236). The multilevel dataset had a higher rate of White officers, male officers, and traffic officers, but overall less experience. The distinction in datasets is marginal and offset by the value of having stable parameters based on a minimum 30 cases.

6. Officers were provided training on completion of the form, and the above listed definitions of the demeanor categories were available to officers as part of this training. During this pilot test, there were no known concerns raised by officers regarding the definition of these categories. Likewise, throughout the course of the project, no questions were raised to the research team regarding the collection of citizens’ demeanor on the traffic stop form.

7. The use of officers’ perceptions of drivers’ race/ethnicity is an acceptable method for examining racially based policing. Officers may incorrectly perceive drivers’ actual race and/or ethnicity. This possible misperception, however, is irrelevant for data collection analyses that seek to explain officer decision‐making. Accusations of racial profiling are based on the presumption that officers treat minority citizens differently. Therefore, proper data collection efforts must identify officers’ perceptions of the race/ethnicity of the driver, not the driver’s actual race/ethnicity. Other information about the driver (year of birth and residential zip code) was gathered directly from drivers’ licenses. Race/ethnicity is not captured on Ohio drivers’ licenses.

8. Originally, the traffic stop form captured officers’ perceptions of drivers’ race/ethnicity in one of seven categories, but due to the infrequent occurrence of Native American, Asian/Pacific Islander, and Middle Eastern drivers these categories were combined with the unknown/missing category to form a category labeled as “Other” drivers.

9. Main city roadways are defined as any main thoroughfare that is heavily populated with traffic on which vehicular traffic is given preferential right‐of‐way, and at the entrances to which vehicles from intersecting roadways are required to stop or yield by law. They may include divided highways, four‐lane roads, or two‐lane roads.

10. Additional analyses (not displayed) suggest that when age is collapsed into dichotomous variables (under 25 years of age and under 30 years of age), no statistically significant difference was found for younger drivers relative to older drivers.

11. Specifically, Engel (2003) reported that 23% of suspects classified as disrespectful did not demonstrate any non‐compliance, verbal aggression, or physical aggression; likewise over half of non‐compliant suspects, 35% of verbally resistant suspects, and 15% of physically resistant suspects were not classified by observers as being disrespectful.