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Campus Police-Citizen Encounters: Influences on Sanctioning Outcomes

Published onJul 15, 2020
Campus Police-Citizen Encounters: Influences on Sanctioning Outcomes
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Note: This is the postprint of the following paper; publisher version available here.

Allen, Andrea. 2015. Campus police-citizen encounters: Influences on sanctioning outcomes. American Journal of Criminal Justice, 40(4), 722-736.

Abstract: The vast majority of prior work on officers’ sanctioning decisions involves municipal police. An open question is whether findings from that research are generalizable to campus police, as they operate in a somewhat different context. This study explores the effect of legal and extralegal factors on campus officers’ sanctioning decisions in encounters. Data were collected through observations of campus police at a large university in the Southeastern United States. Logistic regression is used to analyze 95 campus police-citizen encounters. Findings indicate that officers more severely sanction suspects when there are more bystanders present and suspects display a negative demeanor or are visibly intoxicated. The results suggest that some but not all of the same influences affect municipal and campus police sanctioning decisions. Paths for future research are discussed.  

Introduction

Campus crime is widely recognized as a social problem (Sloan & Fisher, 2011), but few studies focus on the persons primarily responsible for handling it: campus police officers. The most recent comprehensive survey of campus police departments found that there were more than 10,000 sworn officers working for nearly a thousand colleges and universities (Reaves, 2008). Their job is no small task. In 2012, for instance, upwards of 30,000 Part I offenses were recorded by campus police (USDOE, 2013). They made more than 52,000 arrests and issued more than a quarter-million student disciplinary actions—a sanction given to university students who violate university policy or criminal law—for liquor law, drug abuse, and weapons offenses alone (USDOE, 2013).

What influences campus officers’ decisions on how severely to sanction suspects? A bounty of research with municipal officers has examined that outcome, yet whether those findings are generalizable to campus police is an open question (but see Moon & Corley, 2007). Campus and municipal police are similar in some respects, but also differ in ways that could affect the sanctioning of offenders. The goal of this paper is to determine whether a variety of factors known to affect municipal police have a similar effect on campus police. Toward that end, the paper first reviews research on what influences municipal officers’ sanctioning decisions, and outlines how campus policing is distinct from that of many municipalities. This is followed by a description of the method and data, namely a quantitative analysis of campus police-citizen encounters observed during ride-alongs. Finally, the findings are presented and discussed with reference to their implications for theory and future research.

Municipal Officer Sanctioning of Suspects

In dealing with suspects, police have a variety of sanctioning methods at their disposal. For instance, they can opt to arrest a suspect, cite them, issue a written warning, or verbally warn them. Whether and how officers sanction suspects may be consciously or unconsciously affected by a variety of situational factors, broadly categorized as extralegal and legal factors.[1]

Legal factors include offense seriousness. While there are various conceptions of offense seriousness, the research consistently finds that officers are likelier to arrest persons suspected of committing a more serious offense (Black, 1971, 1980; Black & Reiss, 1970; but see Terrill & Paoline, 2007). Seriousness affects sanctioning because officers believe punishment should fit the crime, and the law may require officers to arrest persons suspected of committing certain serious offenses.

One type of extralegal factor is structural issues. This includes how police are mobilized, whether bystanders are present, and the number of officers at the scene. Mobilization is how an officer becomes involved in an encounter. Proactive encounters are officer initiated, whereas reactive encounters follow a citizen’s request for service (Black, 1980). Some research finds that suspects are likelier to be sanctioned more severely in officer-initiated encounters (Black, 1971; Lundman, 1974). An explanation of this pattern is that officers’ decisions to stop a suspect are based—or at least should be based—on reasonable suspicion or evidence of a crime, whereas citizens may mobilize police regardless of the amount of available evidence.  

Prior studies find that encounters which unfold in the presence of bystanders are sanctioned more severely (Engel, Sobol, & Worden, 2000; Manning, 1977). In theory, this matters because police do more to assert their authority and control over the situation when around bystanders to dissuade them from intervening and to send a deterrence message (Alpert & Dunham, 2004).

The number of officers at the scene has been shown to affect sanction decisions, too. For example, Engel and colleagues (2000) found that multiple officers being involved in an encounter increased the likelihood of a suspect being arrested. Such an effect could be due to additional officers providing a sense of social support for severe sanctioning, or simply that encounters with multiple officers are also the most serious ones (see Worden, 1995).

Another sort of extralegal factor is suspect characteristics, such as a suspect’s race, age, sex, status/class, demeanor, and intoxication. Race is perhaps one of the more salient suspect characteristics affecting police-citizen encounters. Some prior research shows that minorities are more likely to be arrested than whites (Black, 1980; Smith & Davidson, 1984; Smith & Visher, 1981), but other studies find the opposite (Brown & Frank, 2005; Klinger, 1996; Novak, 2004). To address the inconsistency in findings, Kochel, Wilson, and Matrofski (2011) conducted a meta-analysis and concluded “definitively … [that] racial minority suspects experience a higher probability of arrest than do Whites” (p. 498). However, they contend that “[t]he extant research does not demonstrate the causes of this racial disparity” (p. 499).

Suspects’ age may shape officers’ sanctioning decisions as well. Many studies find that age and the severity of sanctioning are inversely related (Black, 1980; Brown & Frank, 2005; Engel & Calnon, 2004), but, again, some research finds the opposite. One explanation for sanctioning younger persons more severely is they are likelier to resist police control (Engel, 2003; Kavanagh, 1997; but see Belvedere et al., 2005). Ethnographic research with municipal police suggests that officers are more punitive toward juveniles due to feeling they are inadequately punished by the courts (Muir, 1977; Rubinstein, 1973). Why older suspects would be sanctioned more severely is less apparent, though—and somewhat echoing the ethnographic research—one possibility is that agents of formal control are less severe toward minors because they are to be primarily controlled by parents (Horwitz, 1990).

Whether a suspect is male or female may also affect the severity of sanctioning. The literature consistently finds women to be far less likely than men to be arrested (Garner, Maxwell, & Heraux, 2002; Worden, 1995; Terrill & Mastrofski, 2002). The differential treatment of women may be explained by the chivalry hypothesis (Visher, 1983) or due to “women [being] viewed as a lesser threat to lives, property, and social order” as compared to men (Horwitz, 1990, p. 185).

A fourth kind of suspect characteristic is social status. One aspect of it is income; lower income persons have been found to have greater odds of being arrested (Black, 1980; Lundman, 1994, 1998; Terrill & Mastrofski, 2002; but for an exception, see Mastrofski, Worden, & Snipes, 1995). Sanctioning is also inversely related to another facet of status that Black (1976) calls “radial status”: the degree to which an actor is integrated into the community. Westley (1970), for example, found that officers were likelier to arrest out-of-town drivers than persons from the area. And in their seminal article on broken windows policing, Wilson and Kelling (1982) commented that officers treated community outsiders more punitively.

While there is some disagreement as to what constitutes demeanor, it usually encompasses resistance, noncompliance, disrespect, and hostility (see Dunham & Alpert, 2009; Engel et al., 2000; Worden, Schafer, & Mastrofski, 1996). However defined, research consistently finds that suspects who display a negative demeanor are sanctioned more severely (Black & Reiss, 1970; Engel et al., 2000; Lundman, 1994, 1996; Piliavin & Briar, 1964; but see Klinger, 1994, 1996). A plausible reason that officers do so is to assert their authority and (re)gain control of the situation (Bittner, 1990; Manning, 1977).

Some studies have considered the role of suspects’ intoxication in officers’ sanctioning decisions. This body of work consistently finds that suspects under the influence are likelier to be arrested than sober persons (Lundman, 1974, 1994, 1996, 1998; Reisig, McCluskey, Mastrofski, & Terrill, 2004; Terrill, Paoline, & Manning, 2003). The major reason for this appears to be that intoxicated suspects are prone to display a negative demeanor (Brown, 1981; Engel et al., 2000; Lundman, 1994, 1996, 1998). The odds of arrest are especially high when intoxicated suspects disrespect the police in the presence of bystanders (Engel et al., 2000).                    

The Campus Policing Context

The above review is based solely on municipal officers’ sanctioning of suspects. Unknown is whether those various legal and extralegal factors also affect campus officers’ sanctioning decisions. Although municipal and campus police have many commonalities such as uniform style, the power to arrest, and organizational structure (Bromley, 2003; Paoline & Sloan, 2003), there are aspects of the campus context that make campus policing distinct. These differences, outlined below, raise the question of whether municipal and campus police are affected by the same influences.

The demographic makeup of the population is one difference between municipal and campus police. Though this is not true of every college or university, campus police usually patrol largely homogenous populations of whites from a middle-class and above upbringing (Digest of Education Statistics, 2013). This distinguishes many campus police from municipal officers working in urban areas that are demographically heterogeneous or socially disadvantaged. Furthermore, campus police typically patrol populations made up almost entirely of 18 to 24 year old students. Importantly, this age range marks the height of criminal involvement for the average person (Gottfredson & Hirschi, 1990). It is probably safe to say that no municipal officer patrols a community that is overwhelmingly comprised of that age group, unless, that is, they are assigned to a college campus.

Campus crime typically involves alcohol. Underage drinking is the most common offense. For instance, one national survey of underage, full-time college students found that about three-fifths of them drank in the past month, and two-fifths binge drank (NSDUH, 2006; see also CORE, 2013). One of the more serious offenses is driving under the influence, which college students report doing millions of times each year (Hingson et al., 2009). When Part I crimes occur on campus, the offender is usually intoxicated. For instance, upwards of 95% of all violent crimes involve an intoxicated assailant (CSACU, 1994; see also Hingson et al., 2009); more than half of all sexual assault cases involve an inebriated perpetrator (Abbey et al., 2001); and, a meta-analysis of the dating violence literature shows that drinking increases a person’s odds of committing this offense (Shorey, Stuart, & Cornelius, 2011).

Another distinguishing feature of campus policing is that officers must perform according to expectations of both university and campus police administrators. They generally resist aggressive policing and severe sanctioning because it may dissuade students from attending the university (Bordner & Petersen, 1983; Wolf, Mesloh, & Henych, 2007). This orientation distinguishes campus police from municipal agencies that adopt a more legalistic—i.e., uniform and mandatory—approach to law enforcement (Wilson, 1968).

Campus officers’ job role orientation also sets them apart from municipal police. Many campus security departments have adopted the in loco parentis doctrine, meaning that officers act “in the place of the parents” while students are away at school. This entails protecting students from “outsiders” (i.e., persons not affiliated with the university) who are perceived as the biggest threat to student and campus safety (Sloan & Fisher, 2011). In practice, officers frequently arrested outsider offenders while handling student offenders through the university’s judicial system (Sloan, 1992).

In addition to protecting students, campus police must also protect property. Though both campus and municipal officers may scour property for suspicious activity (e.g., a person loitering outside a closed business), the extent to which the former performs this duty is much greater than the latter. Campus police are typically charged with ensuring that all building doors are locked and the infrastructure is secured. This can be a daunting task, especially on campuses that sprawl upwards of 500 acres and have hundreds of buildings (Reaves, 2008). 

The unique facets of campus policing means findings from studies on municipal officers’ sanctioning may not fully reflect the actions of campus officers. To date, only one study has examined campus officers’ handling of encounters. Moon and Corley (2007) analyzed the effects of various legal and extralegal factors on officers’ sanctioning decisions for more than 10,000 traffic stops. They found that older drivers and minorities were likelier to be issued a legal sanction over a verbal warning. They also found that Asian males, compared to white males, had higher odds of receiving a legal sanction. The time of the stop also influenced officers’ sanctioning decisions; drivers stopped between 6 a.m. and 6 p.m. were punished more severely than those stopped at other times. A limitation of Moon and Corley’s (2007) study is they did not analyze several extralegal factors known to be significant in the municipal context, including demeanor, intoxication, student status, presence of bystanders, the number of officers at the scene, and mobilization.

The Present Study

This study’s contribution is to explore the effect of legal and extralegal factors on campus officers’ sanctioning of suspects during encounters. To do so, a sample of encounters observed during ride-alongs with campus officers at a large Southeastern University is analyzed. The study was approved by the Institutional Review Board (IRB) and access to the police department was granted by its Director.

Participants worked in the field services unit, better known as the patrol division. This unit is charged with answering requests for service and proactively patrolling campus—which spans more than 570 acres—by vehicle, bicycle, and foot. At any given time, the patrol division operates with 25 to 35 officers; during the study period, a total of 33 officers worked in the unit, which included a supervising staff of a Major, Lieutenant, four Sergeants, and four Corporals. Patrol officers were divided amongst four teams, with each supervised by one of the Sergeants and one of the Corporals. Teams worked 12-hour shifts.

The campus is located in a large metropolitan city with a population over 100,000. At the time of the study, the university had about 1,600 full-time faculty members and 30,000 enrolled students, of which 22,000 were undergraduates. The average age of all students was 21. Nearly a third of students lived on campus. The student body was 71% white, 11% black, 3% Hispanic, and 15% other. Respectively, males and females accounted for 42% and 53% of the student population; 5% is unaccounted. Though household or parental income is unavailable from the university, less than one-fifth of students received Pell Grants, suggesting most students are not from an impoverished background.[2]

Campus police officers’ encounters with citizens (students, faculty/staff, and persons not affiliated with the university) on campus is the unit of study. Encounters included traffic stops, streets stops, and requests for service. The sample of encounters was built by a lone researcher riding along with campus police officers. Before beginning the ride-alongs, the researcher completed several preliminary ride-alongs in order to gain a sense of what information should be recorded, and also how the researcher would go about observing encounters.

Ride-alongs were conducted from January, 2011, through April, 2012, during the fall and spring semesters on Thursday, Friday, or Saturday evenings from 6:30 p.m. to 3:00 a.m. The reason for collecting data during these times is prior research suggests that alcohol-involved crime—the focus of the broader project (see Author, XXXX)—occurs most frequently on those days (Sloan & Fisher, 2011). Particular dates of ride-alongs were chosen on a convenience basis, producing a convenience sample of encounters. Thirty ride-alongs were completed, during which 103 police-citizen encounters were observed across 255 hours of observation. Thus, on average about one encounter was observed per 2.5 hours of observation; of course, this is an average, with some periods having more activity than others.

For each ride-along, the researcher attended roll call. At that time, a supervising officer (Corporal, Sergeant, Lieutenant, or Major) assigned the researcher to ride along with an officer on that shift. Participants were guaranteed confidentially, which helps minimize threats to validity due to reactivity (Reiss, 1971; see also Mastrofski et al., 1998b). Sixteen officers were assigned to the ride-alongs; however, while on those ride-alongs a total of 26 officers (79% of the population) were observed handling an incident. This is possible because data were recorded for the responding officer who was not always the officer assigned to the ride-along. Findings are not necessarily generalizable to all campus officers. Rather than sampling beats, officers were selected to the study. This is because while officers working the shift were assigned across four beats, they were free to patrol campus and initiate stops so long as they answered calls for service in their assigned area.

Information on each encounter was recorded using a structured observation protocol—which was pretested prior to the start of data collection—to obtain both quantitative and qualitative data; the observation guide was partially modeled after prior observational police studies (e.g., Mastrofski, Parks, Worden, & Reiss, 1998a). The structured observation guide helped ensure that the researcher consistently recorded information pertaining to the independent and dependent variables of interest. At the conclusion of each encounter, the sanctioning officer was debriefed. The debriefing is a short focused interview wherein the researcher asks the officer to explain why he or she handled the interaction in a particular way (Mastrofski et al., 1998b). Additionally, the researcher took detailed fieldnotes describing each encounter; the narratives provide qualitative insight into how both officers’ and citizens’ behaved in interactions.

Measures

            For each police-citizen encounter, information was obtained on the dependent and independent variables of interest. The dependent variable is how severely an officer sanctioned a suspect, which was recorded using an expanded list of sanctioning options: arrest; citation; student disciplinary action; written warning; suspect information/trespass; verbal warning; no sanction. Note that a student disciplinary action is a sanction drawn up by campus police officers and forwarded on to the Office of Student Conduct, which then may sanction students with, for instance, educational activities (e.g., classes, reflection or research papers), restitution, community service, probation, suspension, or expulsion. For analysis, sanction severity was collapsed into a binary measure, penalty, to capture whether an officer issued a suspect a sanction carrying a penalty (yes=1, no=0). Sanctions with a penalty include student disciplinary action, written citation, and arrest; sanctions without a penalty are written warning, suspect information/trespass, verbal warning, and no sanction. If a suspect was issued more than one sanction, only the most severe is coded for analysis. For incidents in which there was more than one suspect, the sanction for the most severe suspect is analyzed.

The analysis considers the relationship between sanction severity and several independent variables categorizable as legal factors, structural factors, and suspect characteristics. Legal factors include offense seriousness. Offense seriousness was operationalized according to Sellin and Wolfgang’s (1964) scale; according to them, violent offenses, theft, vandalism, and threats are the most serious (=1) compared to all other offenses (least serious=0).

Structural factors are the method of police mobilization, number of bystanders, and the number of officers at the scene. Police mobilization is how the responding officer entered into the police-citizen encounter, be it reactive (=0) or proactive (=1). Number of bystanders and number of officers are measured continuously.

Suspect characteristics include race, sex, age, student status, alcohol intoxication, and demeanor; these were recorded based on the responding officer’s perception. Race is operationalized as minority (=1) and white (=0). Sex is measured as female (=0) and male (=1). Drawing on Lanza-Kaduce and Greenleaf (2000), age is represented as over 30 years old (=0) or under 30 years old (=1). Student status is coded as non-student (=0) and student (=1). Demeanor captures whether the suspect exhibited a positive demeanor (=0) or negative demeanor (=1) during the encounter; a suspect is coded as having a negative demeanor if he/she displayed at least one of the following behaviors: disrespect, noncompliance, or resistance. Disrespect is conceptualized as rolling eyes and turning away while being spoken to, albeit without clearly disobeying the officers’ orders (Dunham & Alpert, 2009); noncompliance entails refusal to answer questions, refusal to cooperate, and verbal resistance such as arguing and cursing (Engel et al., 2000); and, resistance refers to trying to pulling away from an officer or attempting to flee. Alcohol intoxication is operationalized as visibly intoxicated (=1) or not (=0); this assessment was based on visible cues such as slurred or incoherent speech and imbalanced walking.

Analysis

A total of 103 police-citizen encounters were observed, however, only 95 are included for analysis. Eight cases are excluded because the suspect was not present at the scene (i.e., the encounter was between an officer and victim only), and, therefore, could not be sanctioned. Logistic regression is the statistical method of analysis because the dependent variable— penalty—is dichotomous. Although the sample size is small, Hosmer and Lemeshow (2000) argue that logistic regression can be used so long as there are at least ten cases in the sample per independent variable.

Ordinal logistic regression is the preferred method of analysis due to the ordered nature of the campus officers’ sanctioning options, described above. However, preliminary analyses revealed too many empty and small cells in crosstabs of the independent and dependent variables to use this modeling technique. Multinomial logistic regression was also attempted using a trichotomous measure, but there were too many empty and small cells.

Analyses proceeded as follows. First, descriptive statistics and crosstabulations for the independent and dependent variables were performed. At that stage, offense seriousness was excluded because of a low cell count: 2% of cases were serious as defined by Sellin and Wolfgang (1964). Next, correlation matrices were run to detect multicollinearity among the predictors. One association was flagged as highly correlated: suspect age and student status. The decision was made to drop suspect age because officers did not always obtain it (e.g., because they did not ask or look at a suspect’s ID card), though they consistently determined whether a suspect was a student or not; thus, the latter measure is more accurate. Finally, a logistic regression model was analyzed. Levels of significance are reported up to 0.1 given that the sample of encounters is small. The findings presented below reflect the final sample and model.

 Findings

Descriptive statistics for the study’s independent and dependent variables appear in Table 1. Campus officers most often issued written warnings (43.2%), followed by citation (18.9%), arrest (10.5%), verbal warning (7.4%), student disciplinary (5.3%), and information/trespass (4.2%); no sanction was given in 10.5% of cases. Thus, and as pertains to the dependent variable, officers issued no penalty (65.3%) about twice as often as they issued a sanction with a penalty (34.7%).

Most encounters unfolded without bystanders at the scene (M = 0.62, SD = 2.18). Campus police initiated encounters in 87% of cases. The average number of officers at the scene was two, with a range of six. As for the suspect characteristics, 78.9% were white; 65.3% were male; and, 72.6% were students of the university. Nearly a quarter of suspects appeared visibly under the influence of alcohol. Almost 12% of suspects exhibited a negative demeanor.

[Table 1 near here]

            Correlation matrices were performed to detect collinearity among the predictors. Results are found in Table 2. No correlation coefficient was strong enough to raise concern.

[Table 2 near here]

The final logistic model estimates whether the various structural and suspect characteristics of encounters increase the odds of campus police issuing a penalty (i.e., student disciplinary action, citation, arrest) to suspects. Results indicate that the final model significantly differs from its null model and is a good fit; findings are presented in Table 3. The model accounts for 42% of the variance. ROC curve results indicate that the area under the curve is 0.826 with 95% confidence interval (0.732, 0.919), and is significantly different from the true area (0.5); thus, the logistic regression is significantly different than chance.

Of the structural factors, only the variable for bystanders is significant. Each unit increase in the number of bystanders at the scene increases the odds of suspects being issued a penalty by about 63%. Two suspect characteristics are significant: negative demeanor significantly increases the odds of receiving a penalty; also, suspects who appear visibly intoxicated on alcohol are about 211% more likely to be given a sanction carrying a penalty.

[Table 3 near here]

Discussion

This paper’s major goal has been to determine whether factors known to affect municipal officers’ sanctioning decisions also influence those of campus police. The likelihood of campus officers penalizing a suspect by arresting, citing, or issuing a student disciplinary action was greater when there were more bystanders present and the suspect was intoxicated or displayed a poor demeanor. These effects mirror those typically found in studies of municipal police.

In some respects, however, this study’s null results are more interesting because they point to how campus and municipal police may differ. Variables that did not reach statistical significance are suspects’ race, sex, and student/insider status, the number of officers present at the encounter, and whether it was proactively or reactively mobilized. Why might these factors be more likely to affect municipal than campus police officers’ sanctioning decisions?

Unlike Moon and Corley’s (2007) study of campus police, this one did not find suspects’ race to affect sanctioning decisions. This difference could be a methodological artifact or due to differences between the two sets of officers in subconscious stereotyping (see Harris, 2007). Of course, future research should continue to test for the effect of race on encounter outcomes. In the present study and that of Moon and Corley, suspects’ sex did not affect sanction severity. Why campus officers do not sanction males and females differently is speculative at this point. A possibility is the liberal thinking found on some college campuses may counteract the chivalry effect (Visher, 1983).

The finding that students were sanctioned as severely as ‘outsiders’ not only contradicts municipal research (Black, 1976; Westley, 1970; Wilson & Kelling, 1982), but also challenges the idea that campus security departments adopt the in loco parentis doctrine. Recall that it entails protecting students from persons not affiliated with the university by policing the latter group more vigorously than the former (Sloan, 1992; Sloan & Fisher, 2011). Perhaps, then, this doctrine is a thing of the past, simply not applicable to the study site, or only affects officers’ decisions on who to stop (i.e., not relevant to sanctioning post-stop).

As relates to the number of officers, municipal officers working in higher crime jurisdictions have less time to provide backup and thus are less likely to do so unless the incident is a serious one. Yet the campus officers under study often responded as backup not only for added safety and security but also because their hands were not tied handling another incident. Thus, encounters with more municipal officers are the ones most likely to result in a severe sanction (see Worden, 1995), but this relationship does not hold with campus officers.

Some prior research with municipal police finds proactive stops are typically sanctioned more severely, but this relationship was not observed in the current study. Perhaps this is a reflection of the differences in crime between campus and municipal settings. Recall that only 2% of all the observed campus police officer-citizen encounters were serious. Generally, there is more serious crime in many municipalities than on many campuses (Sloan, 1994), and so a larger percent of municipal police-initiated encounters may be in reaction to a serious offense. As less serious offenses are generally sanctioned less severely, the relationship between mobilization and sanction severity may be attenuated among campus police.

In considering the study’s results, the data’s limitations should be kept in mind. For one, the non-random sampling (i.e., convenience sampling) of days and shifts means the findings may not represent the population of encounters at the study site. Relatedly, because a supervising officer selected officers to the ride-alongs, the participants may not be representative of all the patrol officers. Third, officers may have altered their behavior in the presence of the researcher, thereby skewing the results (see e.g., Spano, 2007); to minimize this problem, participants were promised confidentiality, which researchers have found to be an effective way of managing reactivity (see e.g., Mastrofski et al., 1998b). Also, any behaviors and comments that seemed unusual or incomplete were questioned in order to uncover details and understand inconsistencies within and between participants. Because the observations and debriefings were conducted by a lone researcher, it may mean that the findings’ reliability is threatened; to minimize such threats, the researcher used a highly structured observation checklist to record the observations and a semi-structured guide for the debriefings. Finally, it is important to keep in mind that while the current study explored how situational characteristics influenced campus officers’ sanctioning decisions, other factors may have imparted an effect (e.g., organizational characteristics, officer characteristics, officer attitudes, community-level factors).

Conclusion

This paper’s springboard is the observation that it is largely unknown whether findings on municipal police are generalizable to campus police. After all, the two groups often operate in different contexts, police different kinds of populations, and have somewhat different responsibilities. Moon and Corley’s (2007) study and this one suggest that campus and municipal police are neither wholly alike nor entirely different in their sanctioning decisions. Of course, more research is needed on the topic before any strong assessments should be made. One way to further this area of inquiry is to overcome this study’s limitations, detailed above. Moving forward, perhaps the best path is to systematically gather encounter-level data in multiple jurisdictions from both campus and municipal police, as only that research design will help to isolate effects.

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Notes

[1] For brevity’s sake, only the factors included in the analysis are reviewed; this section is not a comprehensive review of all influences on officer sanctioning decisions.

[2] To protect the confidentiality of participants, the university’s name cannot be revealed. Therefore, a full reference for the university’s statistics is not provided.

Table 1: Descriptive Statistics for Campus Police-Citizen Encounters (n=95)





 

Variables

%

SD

M

Range

SANCTION SEVERITY


1.74

3.24

6

     No Sanction

10.50%




     Verbal Warning

7.40%




     Suspect Info/Trespass

4.20%




     Written Warning

43.20%




     Student Disciplinary Action

5.30%




     Citation

18.90%




     Arrest

10.50%




PENALTY


0.48

0.35


     Sanction with a Penalty

34.70%




     Sanction without a Penalty

65.30%




Structural Factors





MOBILIZATION


0.33

0.87


     Proactive

87.40%




     Reactive

12.60%




NUMBER OF BYSTANDERS


2.18

0.62

18

NUMBER OF OFFICERS


1.11

2.05

6

Suspect Characteristics





RACE


0.41

0.21


     Minority

21.10%




     White

78.90%




SEX


0.48

0.65


     Male

65.30%




     Female

34.70%




STUDENT STATUS


0.45

0.73


     Student

72.60%




     Non-student

27.40%




DEMEANOR


0.32

0.12


     Negative Demeanor

11.60%




     Positive Demeanor

88.40%




ALCOHOL INTOXICATION


0.42

0.23


     Visibly Intoxicated

23.20%

 

 

 

     Not Visibly Intoxicated

76.80%

 

 

 

Table 2: Correlations of Predictors (n=95)

 

 

 











 

Variables

1

2

3

4

5

6

7

8

 


 

 

 

 

 

 

 

 

 

1. Alcohol Intoxication

 

-0.167

0.338**

0.201

-0.283**

0.086

-0.055

0.191

 

2. Mobilization

-0.167*

 

-0.447**

-0.357**

-0.037

-0.211*

-0.091

-0.159

 

3. Number of Bystanders

0.338**

-0.447**

 

0.106

-0.076

0.056

0.056

0.033

 

4. Number of Officers

0.201

-0.357**

0.106

 

0.069

0.337**

-0.100

0.162

 

5. Race

-0.283**

-0.037

-0.076

0.069

 

-0.057

-0.146

-0.025

 

6. Sex

0.086

-0.211*

0.056

0.337**

-0.057

 

-0.150

0.195

 

7. Student Status

-0.055

-0.091

0.056

-0.100

-0.146

-0.150

 

-0.073

 

8. Demeanor

0.191

-0.159

0.033

0.162

-0.025

0.195

-0.073

 

 











*P<0.05. **P<0.01.










Table 3: Logistic Regression of Penalty (n=95)

 

 




Variable

b

S.E.

Odds Ratios





Constant

-3.115**







Structural Factors




Mobilization

0.837

1.026

2.310

Number of Bystanders

0.490*

0.264

1.632

Number of Officers

0.298

0.274

1.347





Suspect Characteristics




Race

-0.270

0.752

0.764

Sex

0.624

0.630

1.867

Student Status

-0.186

0.605

0.830

Demeanor

3.071***

1.163

21.567

Alcohol Intoxication

1.136*

0.682

3.114





Model Chi-Square

34.519***



-2 Log Likelihood

88.183



Nagelkerke R Square

0.420







*p<0.10, **p<0.05, ***p<0.01




 

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