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From the Officer’’s Perspective: A

Multilevel Examination of Citizens’’

Demeanor during Traffic Stops

Robin S. Engel, Rob Tillyer, Charles F. Klahm IV & James Frank Available online: 01 Jun 2011

To cite this article: Robin S. Engel, Rob Tillyer, Charles F. Klahm IV & James Frank (2011): From the Officer’’s Perspective: A Multilevel Examination of Citizens’’ Demeanor during Traffic Stops, Justice Quarterly, DOI:10.1080/07418825.2011.574643

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From the Officer’s Perspective:

A Multilevel Examination of

Citizens’ Demeanor during

Traffic Stops

Robin S. Engel, Rob Tillyer, Charles F. Klahm IV

and James Frank

Taylor and Francis RJQY_A_574643.sgm 10.1080/07418825.2011.574643 Justice Quarterly 0741-8825 (print)/1745-9109 (online) Original Article 2011 Taylor & Francis 00 0 0000002011 RobinEngel [email protected]

Over the past 60 years, a substantial body of research has considered the influ-ence 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’

percep-Robin S. Engel, Ph.D., is an associate professor of criminal justice at the University of Cincinnati and Director of the University of Cincinnati Policing Institute. Her research includes empirical assess-ments of police behavior, police/minority relations, police supervision and management, criminal justice policies, criminal gangs, and violence reduction strategies. Her previous research has been

published in Criminology, Justice Quarterly, Journal of Research in Crime and Delinquency, Journal

of Criminal Justice, and Criminology and Public Policy. Her most recent work is focused on crime prevention, homicide reduction, and developing police–academic partnerships. Rob Tillyer, Ph.D., is an assistant professor of criminal justice at the University of Texas, San Antonio. His research inter-ests include decision-making within the criminal justice system, crime prevention, and victimology.

His recent journal articles have appeared in Justice Quarterly, Journal of Criminal Justice, and

Policing. He is currently working on a BJA-funded grant to train students as crime and intelligence analysts while concurrently addressing local crime problems with evidence-based approaches. Charles F. Klahm IV is an assistant professor of criminal justice at Wayne State University. His research interests include understanding police behavior, especially their use of force, the use of evolving technology in policing, as well as how technology influences crime prevention initiatives.

Recent journal articles have appeared in Police Quarterly and the Southwest Journal of Criminal

Justice. James Frank is a professor of criminal justice at the University of Cincinnati. He received his J.D. from Ohio Northern University in 1977 and Ph.D. from the School of Criminal Justice at Mich-igan State University in 1993. His primary research interests include understanding police behavior at the street level, the formation of citizen attitudes toward the police, and the use of evolving

technology by patrol officers. He has published policing articles in Justice Quarterly, Police

Quar-terly, Journal of Criminal Justice, Crime and Delinquency, and Policing: An International Journal of Police Strategy and Management. Correspondence to: Robin S. Engel, School of Criminal Justice, University of Cincinnati, PO Box 210389, Dyer Hall 600, Cincinnati, OH 45221, USA. E-mail: [email protected]

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tions 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

Introduction

On 16 July 2009, Sergeant James Crowley, a White officer from the Cambridge (MA) Police Department, responded to a call for service regarding a burglary in progress. He arrived to find Harvard University Professor Henry Gates, Jr., a Black male who had recently entered his own home after struggling with a jammed door. A verbal altercation ensued and ultimately Professor Gates was arrested for disorderly conduct (Schworm & Ellement, 2009). Professor Gates reported he believed his arrest was based on his race (Olopade, 2009). After the encounter was heavily publicized, President Barack Obama commented that the Cambridge police had “acted stupidly” (Sweet, 2009). Officer Leon Lashley, a Black police officer who was also at the scene, fully supported the actions of his colleague, as did law enforcement groups across the country (Saltzman & Noonan, 2009). Four days later, the disorderly conduct charge against Professor Gates was dropped, and later that month, President Obama invited Professor Gates and Sergeant Crowley to the White House to discuss their differences and ease racial tensions.

Ultimately, the issues between Gates and Crowley were handled behind closed doors at what was dubbed by the media as President Obama’s “Beer Summit” (Feller, 2009). The lingering issue for Americans across the country, however, was whether Professor Gates had been treated differently by Sergeant Crowley based on his race. Many believed Gates had been racially profiled; others contended that it was not his race, but rather his attitude that prompted the police sanction (O’Donnell, 2009; Rochman, 2009). The difference between Gates and Crowley’s encounter and the thousands of other police–citizen encounters that occur every day was only the media attention that it received. As noted by Gates (2009, para. 2): “Sergeant Crowley and I, through an accident of time and place, have been cast together, inextricably, as characters—as metaphors, really—in a thousand narratives about race over which he and I have absolutely no control.” While many minority citizens often perceive that they are treated differently by law enforcement officials due to their race, police officers—at least informally—often complain that minority citizens are more disrespectful and less likely to comply with commands. Therefore, the question remains, is it citizens’ race or demeanor (or some combination) that officers react to during police–citizen encounters?

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Further, what constitutes disrespect? What behaviors do officers perceive as disrespectful and does this vary by officers and citizens’ race? This research begins to systematically disentangle these issues from the officer’s perspective. Since Westley’s (1953) classic study of police behavior that reported citizens’ hostile behaviors influenced police decision-making, there has been great inter-est in the influence of citizens’ demeanor on officer behavior. A substantial portion of this research has focused on officers’ willingness to use formal arrest authority in response to citizens’ displays of disrespect; more recent research attention has been focused on the influence of demeanor across a variety of police–citizen outcomes. In an effort to better understand the concept of citizens’ demeanor, a related body of research has also focused on how best to measure and operationalize this construct. In addition, a smaller body of research has emerged that seeks to identify the correlates of citizens’ demeanor. Combin-ing Tedeschi and Felson’s (1994) theory of coercive actions with Tyler’s (1990) theory of citizen compliance provides an appropriate and compelling context within which to best interpret these previous findings and guide future research. Using official data collected during traffic stops by the Cleveland Division of Police (CDP), we add to this growing body of literature by advancing the conceptualization and operationalization of citizens’ demeanor during police– citizen encounters through the use of officers’ classifications of citizens’ disre-spect, non-compliance, verbal resistance, and physical resistance. The present study advances collective knowledge concerning citizens’ demeanor in several critical ways. First, most prior research used third party (trained observers) perceptions of citizens’ demeanor. Unlike these studies, we measure demeanor using officers’ perceptions. This is critical because theoretical explanations concerning the influence of demeanor are premised on the assumption that it is officers’ perceptions of citizens’ behavior that influence their decision-making process. Second, in an effort to better understand how officers conceptualize citizens’ demeanor, the present study compares alternative measures of citi-zens’ demeanor, including disrespect, non-compliance, verbal resistance, and physical resistance. Third, this study estimates the relationship between citi-zens’ race/ethnicity and multiple measures of demeanor using multilevel statis-tical modeling. The nested nature of police–citizen encounters requires such an analytic approach to ensure proper model specification and unbiased coeffi-cients. Finally, this study examines these relationships within the context of traffic stops, where the majority of police–citizen encounters occur in America (Durose, Smith, & Langan, 2007). The findings demonstrate racial differences in citizens’ demeanor after controlling for a host of other relevant factors. The implications for future research and policy are discussed.

The Impact of Citizens’ Demeanor on Police Behavior

The manner in which citizens respond to police authority has long received attention from scholars attempting to understand what factors may influence

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police officer decision-making. Whether referred to and conceptualized as citi-zens’ “demeanor,” “disrespect,” or “deference,” the behaviors captured in such measures are often quite similar. The body of research examining the impact of citizens’ demeanor over police decision-making is extensive. Table 1 documents 50 studies that used measures of citizens’ demeanor to explain some form of police behavior. This literature is roughly divided into three eras: (1) early qualitative and bivariate studies, (2) contemporary multivariate studies, and (3) post-Klinger (1994).

Initial qualitative research in policing demonstrated the importance and rele-vance of suspect demeanor in influencing police officer behavior (e.g., Van Maanen, 1974; Westley, 1953). Early quantitative studies followed, documenting the bivariate relationships between citizens’ demeanor and police actions. Although the majority of these studies reported that citizen demeanor influenced police officer behavior, the measures of citizen demeanor were often inconsis-tent across studies. Despite a lack of standardization, scholars reported fairly consistent findings in terms of the strong influence citizens’ demeanor had over officers’ behaviors. In the early 1980s, scholars began conceptualizing “antago-nistic” behaviors, including hostile attitudes, cursing at officers, refusing to cooperate, responding sarcastically, and being disrespectful. With few excep-tions, these studies again reported that antagonistic citizens were more likely to be arrested compared to civil or non-antagonistic citizens (see Table 1).

In a seminal article, Klinger (1994) challenged the conventional wisdom that had been mounting regarding the influence of citizens’ demeanor over police behavior. Klinger argued that citizens’ demeanor had been previously conceptu-alized as legally permissible behaviors in extant research, yet most measures of demeanor included actions that constituted criminal behavior. As a result of this discrepancy, Klinger suggested that existing research findings might be flawed because measures of citizens’ demeanor had confounded legally permissible behaviors and criminal actions. Using the 1985 Metro-Dade (FL) systematic social observation data, Klinger reported that when criminal actions were exam-ined separate from measures of disrespect, “hostile” suspects were not signifi-cantly more likely to be arrested.

Since Klinger’s (1994) call for measures of demeanor that omit behaviors constituting illegal acts, many different operationalizations of citizen demeanor have been explored in the policing literature, most of which heed his suggestion. While the bulk of the literature continued to demonstrate significant findings regarding the influence of demeanor over arrest (e.g., Brown & Frank, 2006; Engel, Sobol, & Worden, 2000; Novak & Engel, 2005; Swatt, 2002), findings regarding the impact of citizens’ demeanor on the use of force were less consis-tent. Some studies found that disrespectful suspects were more likely to have force used against them (Garner, Maxwell, & Heraux, 2002; Sun, 2007; Sun & Payne, 2004); other studies reported that disrespectful suspects were not more likely to be involved in use of force incidents (e.g., Terrill & Mastrofski, 2002).

Most studies now adhere to Klinger’s suggestions regarding the conceptualiza-tion of demeanor; however, it remains difficult to know precisely what is

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T

able 1

Studies examining demeanor

, its definition, and results

Author(s) DV IV Definition of Demeanor Results Westley (1953) 1 Force Disrespect

Lacking respect for police, talking back, and acting disrespectful. (p. 39)

—

Piliavin and Briar (1964)

2

Arrest

Demeanor

Measured as cooperative or uncooperative. (see footnote on p. 210 for full explanation)

—

Black and Reiss (1970)

3

Arrest

Deference

Measured as deferential, civil, or antagonistic. (p. 74)

—

Black (1971)

3

Arrest

Deference

Measured as deferential, civil, or antagonistic. (p. 1099)

—

Petersen (1972)

4

Arrest

Deference

References abusive, hostile, uncooperative, or belligerent behaviors. (p. 325)

—

Lundman (1974)

5

Arrest

Disrespect

Impolite (aggression, name calling, ridicule); temper (anger or hostility in voice); non-compliance (failure to comply with a direct order); or physical violence (verbal threats of physical force and actual physical violence). (pp. 134-135) Mixed (+)

Bennett (1976)

6

Perceived danger

Demeanor

Aggressive or passive behavior. (p. 84)

+

Pastor (1978)

7

Arrest

Demeanor

Measured as friendly cooperative, neutral-resigned, hostile-abusive, and violent. (p. 377)

+

Moyer (1981)

8

Demeanor

Measured as cooperative or hostile. (p. 238)

Mixed

Smith and Visher (1981)

9

Arrest

Demeanor

Measured as civil or antagonistic (hostile attitude, cursed at officer, and uncooperative). (p. 171)

+

Visher (1983)

9

Arrest

Demeanor

Measured as civil (0) to hostile (4) behavior (cursing at or hitting an officer). (p. 12)

+

Smith et al. (1984)

9

Arrest

Demeanor

Measured as antagonistic toward police. (p. 241)

+

Smith (1984)

9

Arrest

Demeanor

Measured as antagonistic toward police. (p. 24)

Mixed (+)

Worden and Pollitz (1984)

9

Arrest

Demeanor

References businesslike, friendly, apologetic, or sarcastic, disrespectful, hostile. (p. 113)

+

Smith and Klein (1984)

9

Arrest

Demeanor

Measured as civil or antagonistic. (p. 472)

+

Smith (1987)

9

Arrest

Demeanor

Measured as civil or antagonistic. (p. 772)

+

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T able 1 ( Continued ) Author(s) DV IV Definition of Demeanor Results

Bayley and Garofalo (1989)

10

Force

Demeanor

Obscene or insulting remarks or gestures directed at officers. (p. 9)

+

Worden (1989)

9

Arrest

Demeanor

References disrespect, hostility, and uncooperative behaviors. (p. 688; see footnote on p. 700)

+

Klinger (1994)

11

Arrest

Demeanor

Measured as civil, moderately hostile, or highly hostile. (p. 484)

Non-significant Lundman (1994) 5 Arrest Demeanor

Measured as impolite, deferent, or mixed (impolite and deferent). (p. 636)

Mixed (+)

Klinger (1996)

11

Arrest

Demeanor

Measured as apologetic, deferential, somewhat demeaning, or openly hostile. (p. 64) Mixed (Non- significant)

Worden and Shepard (1996)

9

Multiple

Demeanor

Measured as non-compliance, verbal resistance, disrespectful, hostile, cool, or detached. (p. 88)

Mixed (+)

Lundman (1996)

5

Arrest

Demeanor

Measured as impolite, deferent, mixed (impolite and deferent). (p. 312)

Mixed (+)

Kavanagh (1997)

12

Resist arrest

Disrespect

References verbal abuse, refusing to stop and talk to the officer, and refusing to be handcuffed. (p. 25)

+

Crawford and Burns (1998)

13

Multiple

Demeanor

Measured as a angry or aggressive. (p. 47)

Mixed

Son, Davis, and Rome (1998)

14

Seriousness Scores

Demeanor

Measured as respectful, argues, verbally abusive, and physically abusive. (p. 24)

Mixed (–)

Holmes, Reynolds, Holmes, and Faulkner (1998)

15

Multiple

Demeanor

Measured as calm, non-responsive, nervous/agitated, belligerent/ threatening, and abusive/violent. (p. 92)

Mixed

Engel et al. (2000)

9

Multiple

Demeanor

Refusing to answer questions/cooperate or arguing with or cursing at the officer. (p. 243; see Footnote 5 for explanation) Mixed (+)

Engel (2000)

16

Multiple

Demeanor

Verbally argumentative, impolite, failed to do something that was requested of them by the officer, or displayed disrespectful gestures. (p. 290)

+

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Mastrofski, Snipes, Parks, and Maxwell (2000)

16

Officer compliance

Disrespect

Calling the officer names, making derogatory or belittling statements, issuing slurs, speaking in a loud voice, or ignoring police commands. (p. 326)

Mixed

Engel and Silver (2001)

16

& 9

Arrest

Demeanor

Measured as disrespect and non-compliance. (p. 237)

+

Mastrofski, Reisig, and McCluskey (2002)

16

Police disrespect

Disrespect

Calling the officer names, making derogatory or belittling statements, issuing slurs, speaking in a loud voice, or ignoring police commands. (pp. 529-530)

+

Novak, Frank, Smith, and Engel (2002)

17

Arrest

Demeanor

Measured as deferential/civil or moderately/highly disrespectful. (p. 82; see Footnote 8 for explanation)

+

Garner et al. (2002)

18

Multiple

Demeanor

Measured as antagonistic and used physical force. (p. 732)

+

Terrill and Mastrofski (2002)

16

Force

Demeanor

Doing something that showed disrespect to the officer. (p. 233; see Footnote 21 for explanation)

Non-significant Swatt (2002) 19 Arrest Demeanor

Measured as threatening, disrespectful, apologetic, frightened, compliant, detached, or appropriate. (p. 28)

+

Terrill and Reisig (2003)

16

Force

Disrespect

Calling the officer names, making derogatory or belittling statements, issuing slurs, etc. (p. 310)

—

Terrill, Paoline, and Manning (2003)

16

Force

Demeanor

Doing something that showed disrespect to the officer. (p. 1022; see Footnote 10 for explanation)

Non-

significant

Sun and Payne (2004)

16

Multiple

Demeanor

+

Paoline and Terrill (2004)

16

Force

Demeanor

Doing something that showed disrespect to the officer. (p. 106; see Endnote 8 for explanation)

Non-significant Terrill (2005) 16 Force Disrespect

Measured as suspect disrespectful to police in language or gesture. (p. 124)

Mixed

16

Search

Disrespect

Measured as some form of disrespect. (p. 465)

Mixed

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Novak and Engel (2005)

17

Arrest

Demeanor

Showing moderate or high levels of hostility or disrespect toward officers’ authority. (p. 500)

+

Brown and Frank (2005)

17

Multiple

Demeanor

Measured as deferential/civil or moderately/highly disrespectful. (p. 443; see Endnote 8 for explanation)

Non-significant

McCluskey, Terrill, and Paoline (2005)

16

Force

Disrespect

Mixed

Brown and Frank (2006)

17

Arrest

Demeanor

Measured as deferential/civil or moderately/highly disrespectful. (p. 113; see Endnote 11 for explanation)

+

Sun (2006)

16

Officer assistance

Disrespect

—

Terrill and Paoline (2007)

16

Non-arrest

Disrespect

—

16

Force

Demeanor

Non-significant Sun (2007) 16 Multiple Disrespect

Mixed

Data source legend: 1 = Municipal police department observation and interview data; 2 = Metropolitan police department observat

ion data; 3 = Three site

observation data; 4 = Metropolitan police department observation data; 5 = Midwest City observation data; 6 = Southeast metropo

litan police department survey

data; 7 = Seattle P

olice Department observation data; 8 = Metropolitan police department survey data; 9 = PSS observation data;

10 = NYPD observation data; 11

= Metro-Dade PD observation data; 12 = NYC P

ort Authority survey and archival data; 13 = P

hoenix Arizona Use of Force P

roject d

ata; 14 = Ohio police officer

survey data; 15 = Ohio P

eace Officers T

raining Academy survey data; 16 = POPN observation data; 17 = Cincinnati observation dat

a; 18 = Garner six site

self-report data; and 19 = Chicago observation data.

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captured in the resulting measures. Collectively, the preponderance of the evidence reviewed in Table 1 demonstrates that citizens’ demeanor is an impor-tant factor in determining the outcome of police–citizen encounters. With a handful of exceptions, these studies have reported that citizens viewed as disre-spectful toward police were more likely to receive some form of coercive action.

Conceptualizing, Measuring, and Predicting Citizens’ Demeanor

Given the importance of citizens’ demeanor and its centrality in the literature explaining police behavior, it is somewhat surprising that a parallel body of literature devoted to understanding, defining, and measuring demeanor is quite limited. When considering the conceptualization and measurement of citizens’ demeanor for purposes of understanding police behavior, there are three distinct yet interrelated issues to consider: (1) what verbal and non-verbal behaviors constitute demeanor; (2) what are the most appropriate data sources for measuring demeanor; and (3) what factors during police–citizen encounters might be expected to correlate with demeanor? Each of these issues is discussed in greater depth below.

Conceptualization—What Constitutes Demeanor?

Following Klinger’s (1994) initial critique that demeanor was not consistently operationalized, examinations of the literature demonstrated that, in fact, researchers often used such terms as “antagonistic,” “disrespectful,” “threat-ening,” “hostile,” “abusive,” “resistant,” and “non-compliant” interchangeably to describe both non-verbal and verbal behaviors displayed by citizens (Worden & Shepard, 1996; Worden, Shepard, & Mastrofski, 1996). Academics debated about whether “disrespect” as originally conceived and described by early scholars as influencing police behavior (e.g., Brown, 1988; Van Maanen, 1974; Westley, 1953) actually included behaviors that may be illegal. The ultimate question for Klinger was whether officers responded to citizens’ demeanor because displayed behaviors were actually illegal. Yet, regardless of the illegal nature of some displays of disrespect, most scholars recognize that citizens’ demeanor is a multidimensional concept, with complexities that go well beyond simply characterizing verbal and behavioral cues (Worden et al., 1996). For example, demeanor may include elements of physical behaviors, appearance, subtle gestures, emotion, and speech—all of which might be culturally defined. Further compounding the problem is that these complex behaviors—when measured—are reduced to a simplistic “check box” on data collection forms. Likewise, Worden et al. (1996) noted the unresolved issues regarding the possi-ble cumulative nature of displays of disrespect. These scholars called attention to not only the types of behaviors shown by citizens, but their frequency and

intensity as well.

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Despite the intuitive appeal of a broader conceptualization of citizens’ demeanor, the data currently available do not allow for the disentangling of these issues. While Klinger’s (1994) critique prompted debate among scholars regarding the proper conceptualization and measurement of demeanor, no firm conclusions or consensus was reached. The proper operationalization of demeanor, therefore, continues to be an issue for policing research.

Measurement and Data Sources

Each of the studies reviewed in Table 1 uses one of three types of data to measure citizens’ demeanor: (1) observers’ perceptions; (2) post hoc assess-ment; or (3) hypothetical scenarios. Yet, none of these data sources measures how officers perceive citizens are behaving during actual police–citizen encoun-ters. The bulk of the available research relies on assessments of citizens’ demeanor based on third-party observations. The reliability and validity of these particular measures, however, are largely unknown. Further, the validity and reliability of systematic social observation has been questioned (Spano, 2005). Ultimately, it is the officers’ perceptions of citizens’ demeanor—not observers’ perceptions—that should be measured to examine any potential rela-tionships between citizen demeanor and officer behavior.

Smith, Makarios, and Alpert (2006) suggested that script theory best explains factors that may influence officers’ perception of suspicion toward citizens. Script theory suggests that people construct cognitive roadmaps through engagement with repeated situations, and that cognitive schematics inform their perceptions of these situations, guiding their behavior in subsequent encounters. Thus, an officer who consistently interacts with disrespectful people might, over time, consider that behavior status quo and disregard it as a negative experience. What is status quo for the officer might be interpreted as disrespectful by the observer because there is a lower threshold for such behav-iors, ultimately biasing the data collected by observers. Likewise, Klinger’s (1997) theory suggests that officers’ “vigor” is influenced, at least in part, by their surroundings and what they experience on a daily basis when carrying out their duties. If an officer is assigned to an area where citizens routinely curse at officers, he/she might not characterize that behavior as disrespectful; however an observer unfamiliar with this context likely would interpret such behavior as disrespectful.1

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.

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Given the importance of understanding citizens’ demeanor is directly linked to understanding officer decision-making and the result of police–citizen encounters, citizens’ demeanor is likely best measured from the perspective of the officer rather than an observer. As previously argued by Engel and Silver (2001) as an advantage of their study examining arrest decisions based on citi-zens’ mental status, “if the goal is to understand officers’ decision-making, then officers’ perceptions of mental disorder are more relevant than classifica-tions based on clinical criteria” (p. 236). The same logic applies to research examining citizens’ demeanor during police–citizen encounters. Because the goal is to understand coercive police actions, officers’ perceptions of citizens’ demeanor is the appropriate measure, regardless of how independent observers would classify those same citizens.

What Factors Impact Demeanor?

Early examinations of citizens’ demeanor revealed racial differences in displays of disrespect toward police (e.g., Hudson, 1970; Lundman, Sykes, & Clark, 1978; Piliavin & Briar, 1964; Sykes & Clark, 1975). Building on these early studies, researchers developed more thorough measures of police–citizen encounters based on observational studies (e.g., Mastrofski, Snipes, & Supina, 1996; McCluskey, Mastrofski, & Parks, 1999), grounded the study of citizen demeanor within theoretical frameworks (Engel, 2003), and most recently, used qualitative data to inform understandings of citizen demeanor (Dunham & Alpert, 2009).

Using observational data from Richmond (VA), Mastrofski et al. (1996) reported that citizens were more likely to be “non-compliant” with officer requests in situations when the citizen was poor, known to the police, and multiple officers were at the scene. They also reported that displays of non-compliance were more likely during encounters involving minority officers and White citizens, but less likely in situations involving White officers and minority citizens. In contrast, McCluskey et al. (1999) reported that although citizens’ age, intoxication, and mental illness were all associated with poor demeanor during police–citizen encounters, no statistical significance was discovered for interactions between officer and citizen race.

Engel (2003) examined citizens’ demeanor using data from the 1977 Police Services Study (PSS). Her results indicated that suspects who were intoxicated (drugs or alcohol) and suspects in encounters with multiple officers present were significantly more likely to demonstrate displays of disrespect, verbal resistance, and physical resistance. Consistent with McCluskey et al.’s (1999) findings, Engel also found that minority citizens, (regardless of the officers’ race), were significantly more likely to be non-compliant (e.g., refuse to answer questions or comply with officer requests) compared to Whites suspects, but not more likely to show more aggressive forms of resistance.

Reisig, McCluskey, Mastrofski, and Terrill (2004) used Project on Policing Neigh-borhoods (POPN) data to examine citizen disrespect by using both encounter and

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neighborhood predictors in a hierarchical linear model (HLM). They reported that citizens who were male, poor, intoxicated, mentally impaired, and had height-ened emotionality were more likely to be classified by observers as disrespectful. After controlling for concentrated disadvantage within neighborhoods, Reisig et al. (2004) concluded that minority citizens were not more disrespectful toward the police, but that areas of economic and social disadvantage were more likely to contain citizens who display disrespect. The importance of geographic location was reinforced by findings that police—citizen encounters conducted in areas deemed more dangerous by the police resulted in higher rates of suspect resis-tance (Belvedere, Worrall, & Tibbetts, 2005).

Most recently, Dunham and Alpert (2009) used observation data from Miami-Dade to qualitatively measure officers’ and citizens’ demeanor throughout the encounter. Results indicated that citizens were more likely to possess poor demeanor at the beginning and end of the encounter, compared to officers. Also, they reported that citizens’ demeanor changed throughout the encounter as a result of behavior exhibited by the police. This finding has significant implications for the accuracy of measures of demeanor during police–citizen encounters.

Disrespect in Context: Interactive Theory of Coercive Action and Normative Theory of Citizen Compliance

While the findings regarding the importance of citizens’ demeanor during police–citizen encounters are clear, what becomes more speculative is why citi-zens display disrespect and/or non-compliant/resistant behaviors. Two theoret-ical perspectives—one that explains coercive actions (Tedeschi & Felson, 1994) and one that examines compliance (Tyler, 1990)—seem particularly relevant when considered together. First, Tedeschi and Felson’s social interactionist theory of coercive actions suggests that individuals need to establish and protect their social identify, which can lead to the use of coercive actions. They stressed the importance of impression management, or “saving face” when there has been a challenge to authority. A typical reaction to perceived chal-lenges to authority or social identities may involve coercive actions. Extending this logic to “politeness norms,” Tedeschi and Felson argued that individuals desire to maintain autonomy and control; behavior that threatens this control will likely produce resistance. When applied to police–citizen encounters, Tedeschi and Felson’s theory explains both officers’ coercive behaviors after perceived challenges to their authority and citizens’ resistance after perceived challenges to their autonomy.

Alternatively, Tyler (1990) has outlined the normative processes associated with compliance (e.g., internalization of norms regarding values, justice, and commitment to legal authorities). His examination of why citizens obey the law demonstrates the importance of citizens’ perceptions of police legitimacy (also see, Tyler, 2001; Tyler & Huo, 2002; Tyler & Wakslak, 2004). Tyler has reported that citizens comply with the law either because they believe the

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laws are legitimate or because they believe that legal authorities are legiti-mate. Therefore, one could reason that when citizens do not perceive legal authorities are legitimate, they are less likely to comply with officer requests and/or the law. Applying Tyler’s theory to explain citizens’ demeanor has great implications for understanding the potential differences in demeanor displayed across racial, gender, and age groups, as many studies have documented clear differences in perceptions of police legitimacy across these groups. Together, Tedeschi and Felson’s interactive theory of coercion and Tyler’s theory of compliance suggest that the dynamics during police–citizen encounters, combined with differences in perceptions of police legitimacy across groups, would predict clear differences in how, when, and why demeanor influences police behavior.

Application to Traffic Stop Studies

Beginning in the mid-1990s, the political and academic pressures surrounding the “Driving While Black” phenomena led to police agencies across the country collecting traffic and pedestrian stop data. These data typically consist of offi-cially generated reports produced by police officers that document all traffic or pedestrian stops, regardless of the official disposition of those stops (Ramirez, McDevitt, & Farrell, 2000). As part of this documentation, officers typically record crude measures of the circumstances of the stop (e.g., reason for the stop, observed violations, date, time, location, etc.) and characteristics of the drivers, passengers, and pedestrians (e.g., race, ethnicity, gender, age, etc.) (Tillyer, Engel, & Cherkauskas, 2010). What has been lacking from these data collection efforts has been the collection of more meaningful information previ-ously shown to influence police behavior—most notably, citizens’ behaviors, and more specifically, their demeanor (Engel & Johnson, 2006). Despite the demon-strated importance of citizens’ demeanor, most studies examining traffic and pedestrian stops continue to ignore this important predictor, in part due to the lack of available data.

Using official traffic stop data from the CDP, Engel, Klahm, and Tillyer’s (2010) previous analyses of the impact of police-measured citizens’ demeanor are the lone exception. These analyses demonstrated that motorists classified by police as “disrespectful” were 2.4 times more likely to be arrested compared to those who were not characterized by officers as disrespectful. Likewise, motorists who were recorded by officers as “non-compliant,” “verbally resistant,” and/or “physically resistant” were 3.7 times more likely to be arrested compared to those who were described as compliant. Engel et al. (2010) concluded that citizens’ demeanor during traffic stops (previously unmeasured in studies using official traffic stop data) had a direct impact on police decisions to arrest. Engel et al. (2010) further noted the need to better understand the racial/ethnic differ-ences in displays of disrespect, non-compliance, and resistance, because failure to measure these behaviors in previous traffic stop studies may have produced

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spurious findings related to racial/ethnic differences in traffic stop outcomes. Using measures of officers’ perceptions of citizens’ demeanor, these issues are directly examined in the current study.

Cleveland Traffic Stop Study

This study further examines traffic stops conducted within the City of Cleveland, Ohio in 2005–2006. Based on the 2000 Census, the population of Cleveland was slightly less than 500,000 with 51% Black, 42% White, and 7% Hispanic. In 2003, the CDP had slightly more than 1,800 sworn officers, of which roughly 80% were male. White officers comprised approximately 65% of all sworn officers and Black officers represented roughly 30% of the police force.

Through a federal grant, the CDP received funding to study officer decision-making during traffic stops. Specifically, the grant provided for an independent, external evaluation of policing practices during traffic stops conducted by the CDP. The initial purpose of the study was to aid CDP administrators in determin-ing if racial and/or ethnic disparities in traffic stops and post-stop outcomes existed, and if evident, the possible sources of these disparities. A traffic stop form was developed in consultation with CDP officers, to collect information for

all officer-initiated traffic stops conducted, regardless of the disposition of the traffic stop.2 Officers were required to complete this form at the conclusion of the traffic stop when possible, but no later than the end of their shift.

Data analyzed represented information recorded during officer-initiated traf-fic stops from July 1, 2005 to February 28, 2006.3 During the eight-month study period, the CDP reported traffic stops of 43,707 drivers. The larger study demonstrated that at least one citation was issued to the driver in 96.7% of the traffic stops reported; of the remaining 3.3% of traffic stops, nearly all involved some other form of official action taken by the police, including a written warn-ing, search or arrest of the driver, or of one or more passengers in the vehicle (Engel, Frank, Tillyer, & Klahm, 2006). This finding suggests that the purpose

2. Specifically, the traffic stop form collected information on the: (1) stop (e.g., date/time, loca-tion, 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 vehi-cle, 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 compila-tion. 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 regard-ing 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.

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and design of the research study (to capture information on all traffic stops, regardless of formal dispositions) was compromised. Therefore, this study is best described as an examination of more serious traffic stops that rise at least to the level of citation, rather than a study of all traffic stops.4

Measures

Of the 43,707 traffic stops that were recorded, 42,205 (96.6%) contained valid information on all data fields of interest. For the analyses reported below, only officers with 30 or more traffic stops were included to ensure stable estimates; 3,014 traffic stops (7.1%) were removed because they were initiated by officers who did not engage in at least 30 stops. The analyses, therefore, are based on 39,191 traffic stops performed by 236 officers.5 The range, frequency, and stan-dard deviation of the all variables are reported in the first set of columns in Table 2.

Citizens’ Demeanor

CDP officers were asked to indicate on the traffic stop form whether the driver was civil, disrespectful, non-compliant, verbally resistant, and/or physically resistant; officers were trained and the data collection form directly stated “mark all that apply.” These categories were originally developed based on previous research findings, and discussions with CDP officers assigned to a data collection form committee. Drivers were considered “civil” if they complied with officers’ requests and deferred to their authority. Following Worden and Shepard (1996), officers recorded the driver as “disrespectful” if they demon-strated any behavior (verbal or non-verbal) that was discourteous, rude, or indi-cated an unwillingness to defer to officers’ authority. “Non-compliant” drivers were defined as those who refused to comply with officers’ requests or refused to answer officers’ questions. “Verbally resistant” drivers were those who were

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 offi-cial 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.

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T able 2 Descriptive statistics HLM Data (Level 1, N = 39,191; Level 2, N = 236) Disrespect (N = 1,503) Non-compliance/ resistant ( N = 1,314) Variables Min Max Mean SD Mean SD Mean SD

Dependent variables Disrespect

0 1 0.04 0.19 — — 0.20 — Non-compliant/resistant 0 1 0.03 0.18 0.17 0.38 — —

Driver characteristics White driver

0 1 0.31 0.46 0.25 0.43 0.21 0.41 Black driver 0 1 0.62 0.48 0.69 0.46 0.74 0.44 Hispanic driver 0 1 0.05 0.21 0.04 0.19 0.04 0.20 Other driver 0 1 0.02 0.12 0.02 0.12 0.01 0.11 Male driver 0 1 0.66 0.47 0.64 0.48 0.64 0.48 Driver age 7 106 36.51 12.88 35.47 12.43 36.45 12.74 Non-compliance 0 1 0.02 0.13 0.08 0.28 — — Verbally resistant 0 1 0.02 0.13 0.14 0.35 — — Physically resistant 0 1 0.00 0.05 0.02 0.14 — —

Vehicle characteristics No registration

0 1 0.05 0.21 0.06 0.24 0.03 0.18 Vehicle modifications 0 1 0.02 0.13 0.03 0.16 0.02 0.14 Vehicle condition—poor 0 1 0.08 0.26 0.09 0.29 0.09 0.29 Number of passengers 0 5 0.69 0.95 0.76 1.06 0.55 0.88

Encounter characteristics In zone

0 1 0.29 0.45 0.29 0.45 0.30 0.46 Daytime 0 1 0.56 0.50 0.52 0.50 0.52 0.50 Weekend 0 1 0.19 0.39 0.23 0.42 0.18 0.38 Highway 0 1 0.80 0.40 0.75 0.43 0.83 0.37

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T able 2 ( Continued ) HLM Data (Level 1, N = 39,191; Level 2, N = 236) Disrespect (N = 1,503) Non-compliance/ resistant ( N = 1,314) Variables Min Max Mean SD Mean SD Mean SD Speeding 0 1 0.27 0.44 0.18 0.39 0.33 0.47 Moving misdemeanor 0 1 0.54 0.50 0.61 0.49 0.52 0.50 Equipment 0 1 0.06 0.24 0.05 0.22 0.04 0.20 License/registration 0 1 0.07 0.26 0.06 0.23 0.04 0.20 Pre-existing information 0 1 0.02 0.12 0.02 0.12 0.01 0.08 Other reason 0 1 0.04 0.20 0.08 0.27 0.06 0.23 Number of violations 1 5 1.09 0.34 1.12 0.39 1.10 0.37 Search 0 1 0.08 0.27 0.16 0.37 0.22 0.42 Arrest 0 1 0.04 0.20 0.13 0.33 0.17 0.38

Officer characteristics White officer

0 1 0.74 0.44 0.72 0.45 0.56 0.50 Male officer 0 1 0.97 0.18 0.98 0.13 0.99 0.11 Traffic officer 0 1 0.12 0.32 0.24 0.43 0.10 0.29 Officer experience 4.20 33.02 10.25 5.24 10.42 5.34 15.67 9.03 Population 3,359.76 24,953.69 12,209.88 4,177.63 12,433.91 3,524.09 11,511.11 3,068.03 %White 1.38 82.81 36.14 24.71 34.70 23.85 27.78 25.81 %Black 4.84 98.06 55.92 30.78 56.93 30.64 65.82 32.25 %Hispanic 0.50 26.42 5.80 6.61 6.53 7.27 4.72 5.91 %Male 42.93 75.67 48.93 5.65 49.81 6.10 47.70 4.61 Poverty factor − 1.21 2.15 0.07 0.60 − 0.01 0.50 0.26 0.52 Note.

Disrespect and non-compliance/resistant models are not mutually exclusive; thus, their cumulative total (

N

= 2,817) does not reflect the total number of

cases with at least one form of non-civil demeanor (

N

= 2,558).

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verbally abusive, including cursing at or threatening the officer. Finally, officers recorded drivers as “physically resistant” if they attempted to flee, physically threatened, or assaulted officers.6

This is the first traffic stop study known to the authors that attempted to collect officers’ perceptions of citizens’ demeanor during traffic stops. A substantial majority of drivers (93.5%) were reported to be civil, compared to 3.8% of drivers reported as disrespectful, 1.8% non-compliant, 1.7% verbally resistant, and 0.3% physically resistant. The categories of non-compliant, verbally resistant, and physically resistant were combined into a single measure of non-compliance/resistance (3.4%). Disrespect and non-compliance/resistance were not mutually exclusive (e.g., a driver could be recorded as both disre-spectful and non-compliant/resistant). In the 2,558 traffic stops where officers classified drivers as having at least one type of non-civil demeanor, 10.1% reported both disrespect and non-compliance/resistance.

One important limitation of previous findings regarding citizens’ demeanor is the difficulty in disentangling causal order during police–citizen encounters. For example, it is possible that drivers do not become disrespectful, non-compliant, or resistant until after a custodial arrest is made. In these situations, demeanor does not predict arrest, but rather arrest may predict demeanor. Cleveland officers were specifically instructed that when they filled out the traffic stop forms at the conclusion of the stops, they were to record their perceptions of citizens’ demeanor prior to any specific coercive action that they (the officer) took, including issuing a citation, making a custodial arrest, or using force. Officers were specifically trained that they should not consider any non-civil treatment that they perceived occurred as a result of their coercive behavior (citation, arrest, or search) toward citizens. For example, if an otherwise civil driver cursed at an officer after receiving a ticket, officers were instructed to code that citizen as civil. While adherence to this coding procedure cannot be directly tested, there was no indication or anecdotal information available that officers did not adhere to the coding procedures as instructed. Nevertheless, coercive outcomes including searches and arrests are included in the multivari-ate models as statistical controls.

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.

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Driver Characteristics

The racial/ethnic characteristics of drivers stopped were determined through officers’ perceptions; drivers were not asked to identify their race or ethnicity.7 Of the 39,191 motorists stopped by CDP officers, the majority (62.4%) were Black, followed by White (31.3%), Hispanic (4.8%), and Other races/ethnicities (1.6%).8 The likelihood of arrest varied across racial/ethnic groups. Of the White motorists stopped by CDP officers, 2.7% were arrested, compared to 5.1% of Black motorists and 4.0% of Hispanic motorists. The average age of drivers stopped by CDP officers was 36.51 years, and 66.2% of the stopped drivers were male.

Vehicle Characteristics

Information was also collected on vehicle characteristics, including if the vehi-cle had a valid registration sticker (4.5% of stops involved vehivehi-cles without proper registration). Officers also noted whether the vehicle had any after-market modifications, such as tinted windows, high performance exhaust systems, or aftermarket rims. Officers reported the condition of the vehicle as good, fair, or poor; this variable was dichotomized as poor condition or not (7.5% of vehicles were reported in poor condition, with visible cosmetic defects to the exterior of the vehicle including broken head or taillight(s), mirror(s), muffler, window(s), or severe body damage). Each of these variables represents an improvement over previous traffic stop studies that have not collected this information. Over half (54.9%) of drivers stopped did not have passengers.

Encounter Characteristics

As indicated in Table 2, 28.8% of the stopped drivers were residents of the police zone in which they were stopped. A majority of traffic stops initiated by

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 infor-mation 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.

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CDP (55.6%) occurred during daylight hours between 6 AM and 7 PM, during weekdays (81.1%), and on main roadways or interstate highways (80.1%).9

Officers documented the reason they initiated the traffic stop, which included speeding, felony, and misdemeanor moving violations, equipment inspection, traffic enforcement, and/or registration/license check. The most frequent violations observed prior to a traffic stop were moving misdemeanor violations (53.5%), followed by speeding or other moving felony (26.8%). The number of infractions documented during a traffic stop ranged from one to five, with a mean of 1.1. A custodial arrest of the driver was reported in 4.2% of the traffic stops, while 8.2% of stops resulted in vehicle searches.

Level 2: Officer Characteristics

Officers were required to enter their badge number on every traffic stop form submitted. This badge number was then linked to specific information about each officer, including their race, gender, assignment, and years of experience. In addition, the geographic location of each traffic stop was recorded and linked to each officer. Information, drawn from the Census block within which the traffic stop occurred, included the population, the percent White, Black, Hispanic, and male, and a composite measure of poverty within that geographic area. This geographic information was averaged within each officer to create a rate for each officer. In other words, the geographic variables represent the typical context in which the officer initiated their traffic stops. Higher values indicate that an officer initiated their traffic stops in areas with higher levels of that variable. In these data, 39,191 traffic stops with valid information were made by 236 officers. Officers ranged in their number of traffic stops from 30 traffic stops to 1,366, with an average of 169 stops.

Analytical Technique

Hierarchical modeling is appropriate for data collected across different units of aggregation (Raudenbush & Bryk, 2002). Police data, and traffic stop data in particular, may demonstrate correlated error due to the varying levels of aggre-gation, thereby violating one of the fundamental assumptions of traditional multivariate modeling. Hierarchical or multilevel modeling addresses this concern by distinguishing between effects located at the individual/encounter level and those occurring at a higher unit of aggregation (Luke, 2004). In these data, variables associated with the encounter, such as driver and vehicle char-acteristics, were modeled at Level 1, while officer characteristics were

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.

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modeled at Level 2. The use of multilevel modeling was further justified by the revelation that levels of disrespect and non-compliance/resistance varied across officers. Thus, it was important to examine Level 2 variables that may assist in explaining the variation in these outcomes across officers, net of encounter characteristics.

All analyses were computed using multilevel, Bernoulli models computed in HLM 6 with the significance level preset to 0.001 due to the large sample size (N = 39,191). The disrespect and non-compliance/resistant models required a correction for over-dispersion, which occurs when the standard error of the dependent variable is larger than the mean of that variable (Hanushek & Jackson, 1977). The Level 1 coefficients for all models were fixed with the intercept left to vary randomly across Level 2 units because the research hypotheses did not predict that these effects would differ significantly in magnitude across the Level 2 units (Raudenbush & Bryk, 2002). All Level 1 units were group mean centered for analysis, which allows for interpretation within aggregate units by controlling for contextual effects across Level 2 units (Raudenbush & Bryk, 2002). Coefficients and standard errors are reported for all variables while odds ratios are provided for statistically significant variables.

Findings

To better understand how officers’ conceptualize “disrespect,” a series of cross-tabulations were performed. The results demonstrate that the concept of “disrespect” is based on more than the actual non-compliant and resistant

behaviors recorded by officers. Of the 703 drivers described by officers as non-compliant with officer requests, only 18.1% (127) were also classified by officers as disrespectful. Likewise, only 32.0% (210) of the 657 suspects that were verbally resistant were also classified as disrespectful. Finally, only 29.1% (30) of the 103 citizens identified as physically resistant were also considered disre-spectful by officers. All of the cross-tabulations indicated statistically signifi-cant differences in these measures at the 0.001 level. Therefore, the analyses below focus on two separate dependent variables: (1) disrespect (attitude-based) and (2) non-compliance and/or resistant (behavior-(attitude-based). In the models estimating disrespect, the behavior-based variables are included as predictor variables based on the assumption that officers may view suspects as disrespect-ful based in part (but not solely) on their behaviors. In contrast, non-compliance/resistance measures are capturing specific behaviors and should not

be influenced by officers’ perceptions of drivers’ disrespect; therefore the multivariate models examining non-compliance/resistance do not include measures of disrespect.

To further describe the differences between civil and non-civil drivers, Table 2 also reports descriptive for each variable when the sample is divided into subsamples of all drivers considered disrespectful, and all drivers considered non-compliant/resistant. Bivariate analyses indicate that racial/ethnic groups

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significantly differed in their demeanor classification by officers; Black drivers were significantly more likely to be classified by officers as disrespectful, non-compliant, and resistant (verbally and physically) compared to Whites. Specifi-cally, 4.3% and 4.0% of Black drivers were classified by officers as disrespectful and non-compliant/resistant, respectively, compared to only 3.1% and 2.3% of White drivers. Interestingly, female drivers were slightly more likely than males to be considered disrespectful and non-compliant/resistant. Specifically, 4.1% and 3.6% of female drivers were classified by officers as disrespectful and non-compliant/resistant, respectively, compared to 3.7% and 3.2% of male drivers. These differences were statistically significant at the 0.05 level. These bivariate relationships are further examined in the multivariate analyses reported below. Table 3 reports the HLM results predicting officers’ perceptions that drivers were disrespectful during traffic stops. Model 1 includes drivers’ characteristics (i.e., race—White is the excluded category, gender, age, if the stop occurred in the police zone where they reside, and drivers’ behavior including non-compli-ance, verbal resistnon-compli-ance, and physical resistance), vehicle characteristics (i.e., registration, modifications, condition, and number of passengers), and encoun-ter characencoun-teristics (i.e., time, day of the week, type of highway, reason for the stop—speeding is the excluded category, number of violations, arrest, and search). Model 2 includes the previous variables, while adding officer character-istics at Level 2 (i.e., officer race, gender, traffic assignment, experience, population, percent residential population Black, Hispanic, male, and poverty). Finally, additional models (not shown in tabular form) examined officer–driver race cross-level interactions.

Consistent across Models 1 and 2 is the finding that Black drivers were approximately 1.3 times more likely compared to Whites to be perceived by officers as disrespectful, even while controlling for their behavior and traffic stop outcomes. Also of note is that Hispanic drivers were 1.5 times less likely compared to Whites to be perceived by officers as disrespectful. Interestingly, female drivers were 1.3 times more likely to be perceived by police as disre-spectful during traffic stops compared to male drivers. Drivers’ age—measured as a continuous variable—was not statistically significant.10 The results also demonstrate that drivers who were non-compliant with police were 5.1 times more likely to be perceived as disrespectful, compared to over 14 times more likely for verbally resistant drivers. It is also interesting to note that physically resistant motorists were not significantly more likely than their non-physically resistant counterparts to be described by police as disrespectful.

Model 2 demonstrates that White officers were 1.5 times more likely to report that stopped drivers displayed disrespect toward them compared to non-White officers. In addition, officers with less than five years of experience were slightly more likely to report encountering disrespectful drivers. On average,

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

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T

able 3

Multilevel models (Level 1 = 39,191; Level 2 = 236)

Disrespect Non-compliance/resistant Model 1 Model 2 Model 3 Model 4 Variables Coefficient OR Coefficient OR Coefficient OR Coefficient OR

Driver characteristics Black driver

0.22* 1.25 0.23* 1.26 0.50*** 1.65 0.49*** 1.64 Hispanic driver − 0.43** 0.65 − 0.43** 0.65 0.31 — 0.31 — Other driver 0.45 — 0.45 — 0.39 — 0.39 — Male driver − 0.24** 0.79 − 0.24** 0.79 − 0.25* 0.78 − 0.26* 0.77 Driver age 0.00 — 0.00 — 0.00 — 0.00 — Non-compliance 1.63*** 5.10 1.65*** 5.21 — — — — Verbally resistant 2.68*** 14.61 2.67*** 14.42 — — — — Physically resistant 0.35 — 0.35 — — — — —

Vehicle characteristics No registration

0.46* 1.58 0.47* 1.61 0.38 — 0.37 — Vehicle modifications 0.35 — 0.35 — 0.13 — 0.14 — Vehicle condition—poor 0.07 — 0.07 — 0.24* 1.28 0.25* 1.28 Number of passengers 0.02 — 0.02 — − 0.08 — − 0.08 —

Encounter characteristics In zone

− 0.09 — − 0.09 — 0.08 — 0.08 — Daytime − 0.10 — − 0.07 — − 0.15 — − 0.13 — Weekend 0.07 — 0.07 — 0.14 — 0.13 — Highway 0.09 — 0.09 — − 0.18 — − 0.18 — Moving misdemeanor 0.29** 1.33 0.26** 1.30 − 0.01 — − 0.01 — Equipment − 0.12 — − 0.13 — − 0.31 — − 0.32 — License/registration − 0.09 — − 0.11 — − 0.78*** 0.46 − 0.78*** 0.46

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T able 3 ( Continued ) Disrespect Non-compliance/resistant Model 1 Model 2 Model 3 Model 4 Variables Coefficient OR Coefficient OR Coefficient OR Coefficient OR Pre-existing information 0.35 — 0.30 — − 0.73 — − 0.72 — Other reason 0.61*** 1.84 0.58*** 1.79 0.32 — 0.32 — Number of violations 0.16* 1.17 0.16* 1.17 0.29*** 1.33 0.29*** 1.33 Arrest 0.75*** 2.11 0.74*** 2.10 1.35*** 3.86 1.36*** 3.88 Search 0.13 — 0.13 — 1.15*** 3.17 1.15*** 3.16

Officer characteristics White officer

— — 0.41* 1.51 — — − 0.17 — Male officer — — − 0.30 — — — − 0.14 — Traffic officer — — − 0.14 — — — − 0.49 — Officer experience — — − 0.05*** 0.95 — — 0.01 — Population — — 0.00* 1.00 — — 0.00 — %Black — — 0.01** 1.01 — — 0.00 — %Hispanic — — 0.04 — — — 0.01 — %Male — — 0.08*** 1.08 — — 0.00 — Poverty factor — — − 0.02 — — — − 0.15 — Intercept − 3.67*** 0.03 − 3.66*** 0.03 − 4.32*** 0.01 − 4.33*** 0.01 * p ≤ .05; ** p ≤ .01; *** p ≤ .001.

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traffic stops initiated by officers in areas with greater population, higher rates of Black citizens, and male citizens resulted in more instances of citizen disre-spect. Based on the results from Model 2 demonstrating the impact of both driv-ers’ and officdriv-ers’ race on perceptions of disrespect, a series of cross-level interactions were performed. The results (not displayed in tabular form) demonstrate that there remains significant unexplained variation in citizen disrespect across officers, and in particular the effect of Black drivers on disre-spect, but officers’ race does not explain this variation.

Table 3 also reports the HLM results predicting officers’ perceptions that drivers were non-compliant, verbally resistant, and/or physically resistant during traffic stops (i.e., Models 3 and 4). Note that the models reported, however, do not include disrespect as a predictor variable because officers’ perceptions of disrespect are not expected to influence drivers’ displayed behaviors of non-compliance or resistance.

Across all models, drivers’ race again was a significant predictive factor. Compared to White drivers, Black drivers were 1.6 times more likely to display behaviors interpreted by police officers as non-compliant and/or resistant. While Hispanic drivers were significantly less likely than White drivers to be reported as disrespectful, they were equally likely to be non-compliant and/or resistant. Drivers’ gender again appears to play an important role, although in a somewhat unexpected direction. Female drivers were 1.3 times more likely than male drivers to be reported by officers as non-compliant and/or resistant.

When officer characteristics are added in Model 4, the findings remain unchanged. None of the officer characteristics were significant predictors of drivers’ non-compliance/resistance. While there remains significant unex-plained variation in drivers’ non-compliance and/or resistance, cross-level interactions suggest that drivers’ race affects reports of non-compliance/ resistance uniformly across officers.

Discussion

As suggested through the combined application of Tedeschi and Felson’s inter-active theory of coercive actions and Tyler’s normative theory of citizen’s compliance, this study demonstrated that officers’ perceptions of citizens’ displays of disrespect, non-compliance, and resistance differ across racial/ ethnic groups. Black drivers in Cleveland were more likely to be reported by police officers as disrespectful and non-compliant/resistant, even after control-ling for characteristics of the driver, traffic stop, and officer. Consistent with McCluskey et al. (1999), these findings did not demonstrate significant citizen– officer race interactions. White officers were significantly more likely than Black officers to classify drivers as disrespectful, but this effect was consistent across drivers’ race. Further, Black and White officers were equally likely to report drivers as displaying behaviors that were non-compliant and/or verbally resistant.