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A Bayesian Analysis of a Cognitive-Behavioral Therapy Intervention for High-Risk People on Probation

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  • SeungHoon Han
  • Jordan M. Hyatt
  • Geoffrey C. Barnes
  • Lawrence W. Sherman

Abstract

This analysis employs a Bayesian framework to estimate the impact of a Cognitive-Behavioral Therapy (CBT) intervention on the recidivism of high-risk people under community supervision. The study relies on the reanalysis of experimental datal using a Bayesian logistic regression model. In doing so, new estimates of programmatic impact were produced using weakly informative Cauchy priors and the Hamiltonian Monte Carlo method. The Bayesian analysis indicated that CBT reduced the prevalence of new charges for total, non-violent, property, and drug crimes. However, the effectiveness of the CBT program varied meaningfully depending on the participant's age. The probability of the successful reduction of drug offenses was high only for younger individuals ( 26Â years old). In general, the probability of the successful reduction of new charges was higher for the older group of people on probation. Generally, this study demonstrates that Bayesian analysis can complement the more commonplace Null Hypothesis Significance Test (NHST) analysis in experimental research by providing practically useful probability information. Additionally, the specific findings of the reestimation support the principles of risk-needs responsivity and risk-stratified community supervision and align with related findings, though important differences emerge. In this case, the Bayesian estimations suggest that the effect of the intervention may vary for different types of crime depending on the age of the participants. This is informative for the development of evidence-based correctional policy and effective community supervision programming.

Suggested Citation

  • SeungHoon Han & Jordan M. Hyatt & Geoffrey C. Barnes & Lawrence W. Sherman, 2024. "A Bayesian Analysis of a Cognitive-Behavioral Therapy Intervention for High-Risk People on Probation," Evaluation Review, , vol. 48(6), pages 991-1023, December.
    • Handle: RePEc:sae:evarev:v:48:y:2024:i:6:p:991-1023
    DOI: 10.1177/0193841X231203737
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    References listed on IDEAS

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    1. Richard Berk & Lawrence Sherman & Geoffrey Barnes & Ellen Kurtz & Lindsay Ahlman, 2009. "Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 191-211, January.
    2. Kitty Lymperopoulou & Jon Bannister & Karolina Krzemieniewska-Nandwani, 2022. "Inequality in Exposure to Crime, Social Disorganization and Collective Efficacy: Evidence from Greater Manchester, United Kingdom," The British Journal of Criminology, Centre for Crime and Justice Studies, vol. 62(4), pages 1019-1035.
    3. Tao Hu & Xinyan Zhu & Lian Duan & Wei Guo, 2018. "Urban crime prediction based on spatio-temporal Bayesian model," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-18, October.
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