Following their Lead: Police Perceptions and their Effects on Crime Prevention

Abstract

This study examines the effect that individuals’ perceptions of police have on their adoption of crime prevention measures. Unlike past research that conceptualized police perceptions as inversely associated with crime prevention, we introduce a framework that distinguishes between the traditional policing and community policing/procedural justice models. We analyze multilevel data from Canada’s General Social Survey for 13 crime prevention measures (e.g. locking doors, installing burglar alarms), and estimate Item Response Theory models to account for differing levels of difficulty in the implementation of these measures. Results show that the effect of police perceptions on the adoption of crime prevention measures varies by policing model. Residents who have favorable perceptions of the police as to the performance of traditional policing duties are less inclined to take measures against crime. In contrast, those with favorable perceptions of the police as engaging in community policing/procedural justice are more inclined to take such measures.

Keywords:

• Crime prevention
• police perceptions
• community policing
• procedural justice
• Item Response Theory
• Canada’s General Social Survey

Acknowledgements

This study used Public Use Microdata from the Cycle 23 of Canada’s General Social Survey (Catalogue No. 12M0023X), obtained by the authors from Statistics Canada. We are grateful to Paul-Philippe Pare for his guidance on the survey and crime prevention context in Canada, as well as for providing helpful comments on earlier versions of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 We encountered missing data in the original dataset of the GSS due to missing responses for select respondents on four independent variables (household income, homeownership status, length of residence, and marital status). The use of listwise deletion in the analysis would have led to an 18% decrease in the sample (from N = 18,790 to N = 15,242) and open the possibility of bias. To address this issue, we performed the EM imputations (25 iterations) using all data available across cases. This method overcomes some of the limitations of other techniques, such as mean substitution or listwise deletion, which can generate biased estimates-and underestimate the standard errors (Graham, 2012).

2 We conducted our analyses in HLM 7.03.

3 Specifically, we controlled for age, gender, race, marital status, household income, residence length, homeownership, living with parent(s), living with child(ren), living in an urban community, and the geographical region of residence.

4 Considering our theoretical framework and the need to differentiate between community policing versus traditional policing/procedural justice to test our hypotheses, we conducted an exploratory factor analysis set for the extraction of two factors. The first extracted factor, which explained 36 percent of the variance, included four items resembling the traditional policing model (enforcing the laws, responding to calls, ensuring safety, and overall confidence in police). The remaining three items loaded into a second factor that explained 32 percent of the variance and captured the community-policing/procedural justice model (being approachable and easy to talk to, supplying info on crime prevention, and treating people fairly). We conducted a confirmatory factor analysis in Mplus, which indicated that this two-latent-factor model fit the data well (RMSEA = 0.00; CFI = 0.99).

5 We included 12 dummy variables to capture the crime prevention measures (“Do you routinely lock windows and doors at home?” was selected as the reference variable as it was the most common measure applied).

Additional information

Notes on contributors

Arelys Madero-Hernandez

Arelys Madero-Hernandez is an Assistant Professor in the Department of Criminal Justice at Shippensburg University. Her research focuses on the correlates of crime prevention, the link between race/ethnicity and victimization, and immigration effects on crime and victimization.

YongJei Lee

YongJei Lee is an Assistant Professor in the School of Public Affairs at University of Colorado Colorado Springs. His current research examines police effectiveness, spatio-temporal patterns of crime hot spots, crime place detection and forecasting algorithm using Geographic Information Systems (GIS), concentration of crime at places, offenders, and victims, and measures of crime concentration phenomenon.

Pamela Wilcox

Pamela Wilcox is Professor of Sociology and Criminology at the Pennsylvania State University. Her scholarship is focused on understanding and preventing crime and victimization across neighborhood and school contexts.

Bonnie S. Fisher

Bonnie S. Fisher is Professor at the School of Criminal Justice at the University of Cincinnati. Her research interests span a range of victimological topics from the measurement of interpersonal violence against college students to the identification of theory-based predictors of interpersonal victimization to the evaluation of crime prevention strategies, and most recently, the design and implementation of a longitudinal study of interpersonal violence against and by emerging adults.