Abstract
Background: Hot spots policing is one of the most influential police innovations, with a strong body of experimental research showing it to be effective in reducing crime and disorder. However, most studies have been conducted in major cities, and we thus know little about whether it is effective in smaller cities, which account for a majority of police agencies. The lack of experimental studies in smaller cities is likely in part due to challenges designing statistically powerful tests in such contexts.
Objectives: The current article explores the challenges of statistical power and ‘‘noise’’ resulting from low base rates of crime in smaller cities and provides suggestions for future evaluations to overcome these limitations.
Research Design: Data from a randomized experimental evaluation of broken windows policing in hot spots are used to illustrate the challenges that low base rates present for evaluating hot spots policing programs in smaller cities.
Results: Analyses show low base rates make it difficult to detect treatment effects. Very large effect sizes would be required to reach sufficient power, and random fluctuations around low base rates make detecting treatment effects difficult, irrespective of power, by masking differences between treatment and control groups.
Conclusions: Low base rates present strong challenges to researchers attempting to evaluate hot spots policing in smaller cities. As such, base rates must be taken directly into account when designing experimental evaluations. The article offers suggestions for researchers attempting to expand the examination of hot spots policing and other microplace-based interventions to smaller jurisdictions.
Original language | English |
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Pages (from-to) | 213-238 |
Number of pages | 26 |
Journal | Evaluation Review |
Volume | 37 |
Issue number | 3-4 |
DOIs | |
State | Published - Jun 2014 |
Bibliographical note
Publisher Copyright:© The Author(s) 2014.
Keywords
- Crime and justice
- Design
- Experimental methods
- Hot spots policing
- Methodology (if appropriate)
- Statistical power