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Background: Corrections agencies frequently place offenders into risk categories, within which offenders receive different levels of supervision and programming. This supervision strategy is seldom evaluated but often can be through routine use of a regression discontinuity design (RDD). This article argues that RDD provides a rigorous and cost-effective method for correctional agencies to evaluate and improve supervision strategies and advocates for using RDD routinely in corrections administration. The objective is to better employ correctional resources. Method: This article uses a Neyman–Pearson counterfactual framework to introduce readers to RDD, to provide intuition for why RDD should be used broadly, and to motivate a deeper reading into the methodology. The article also illustrates an application of RDD to evaluate an intensive supervision program for probationers. Result: Application of the RDD, which requires basic knowledge of regressions and some special diagnostic tools, is within the competencies of many criminal justice evaluators. RDD is shown to be an effective strategy to identify the treatment effect in a community corrections agency using supervision that meets the necessary conditions for RDD. Conclusion: The article concludes with a critical review of how RDD compares to experimental methods to answer policy questions. The article recommends using RDD to evaluate whether differing levels of control and correction reduce criminal recidivism. It also advocates for routine use of RDD as an administrative tool to determine cut points used to assign offenders into different risk categories based on the offenders' risk scores.

The gap between what people admit about their behavior and what is actually true has plagued social scientists and survey methodologists for decades. This gap would not matter if it did not play an important role in estimation of the extent of the consumption of illegal drugs and/or changing trends in illegal use, both data critical for developing public policy and determining the effects of intervention programming and policy changes. The Arrestee Drug Abuse Monitoring (ADAM) survey matches anonymous self-reported interview information to a urine test for nine drugs in a probability-based sample of adult male arrestees conducted within 48 hr of their arrest. Using data from 2000-2003 and 2007-2011 collected in 10 U.S. counties, this article looks at how the gap between the truth and reality in self-report varies by the drug reported, by the region of the country, over time, and by characteristics of the user, and discusses the relevance of these findings to policy.