Systematic Reviews and Meta-Analyses of Crime Prevention Evaluations

Abstract

Abstract Systematic reviews and meta-analyses have become a focal point of evidence-based policy in criminology. Systematic reviews use explicit and transparent processes to identify, retrieve, code, analyze, and report on existing research studies bearing on a question of policy or practice. Meta-analysis can combine the results from the most rigorous evaluations identified in a systematic review to provide policymakers with the best evidence on what works for a variety of interventions relevant to reducing crime and making the justice system fairer and more effective. The steps of a systematic review using meta-analysis include specifying the topic area, developing management procedures, specifying the search strategy, developing eligibility criteria, extracting data from the studies, computing effect sizes, developing an analysis strategy, and interpreting and reporting the results. In a systematic review using meta-analysis, after identifying and coding eligible studies, the researchers create a measure of effect size for each experimental versus control contrast of interest in the study. Most commonly, reviewers do this by standardizing the difference between scores of the experimental and control groups, placing outcomes that are conceptually similar but measured differently (e.g., such as re-arrest or reconviction) on the same common scale or metric. Though these are different indices, they do measure a program’s effect on some construct (e.g., criminality). These effect sizes are usually averaged across all similar studies to provide a summary of program impact. The effect sizes also represent the dependent variable in the meta-analysis, and more advanced syntheses explore the role of potential moderating variables, such as sample size or other characteristics related to effect size. When done well and with full integrity, a systematic review using meta-analysis can provide the most comprehensive assessment of the available evaluative literature addressing the research question, as well as the most reliable statement about what works. Drawing from a larger body of research increases statistical power by reducing standard error; individual studies often use small sample sizes, which can result in large margins of error. In addition, conducting meta-analysis can be faster and less resource-intensive than replicating experimental studies. Using meta-analysis instead of relying on an individual program evaluation can help ensure that policy is guided by the totality of evidence, drawing upon a solid basis for generalizing outcomes.

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