Journal of Experimental Criminology

, Volume 7, Issue 1, pp 57–71 | Cite as

Predicting criminal recidivism: A research note

  • William RhodesEmail author


Criminal justice researchers often develop prediction instruments as a practitioner tool for improving the allocation of resources in community corrections administration. Although best practices have emerged for developing predictions, those best practices lead to predictions that fail to distinguish risk factors from control and correctional responses to risk. The consequence is that predictions fail to predict what they purport to predict, and this limits the utility of those predictions for public policy. This note argues that when properly done, predictions pertain to a latent, unobservable population. Given that perspective, some best practices advocated for prediction should be abandoned, and new best practices should be adopted.


Risk prediction Risk factors Criminal recidivism Evidence-based probation practices Survival analysis Program evaluation 


  1. Andrews, D., Bonta, J., & Wormith, J. (2006). The Recent Past and Near Future of Risk and/or Need Assessment. Crime & Delinquency , 7-27.Google Scholar
  2. Angrist, J., & Pischke, J. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.Google Scholar
  3. Bushway, S., & Smith, J. (2007). Sentencing Using Statistical Treatment Rules: What We Don't Know Can Hurt Us. Journal of Quantitative Criminology , 377-387.Google Scholar
  4. Cameron, A., & Trivedi, P. (2005). Microeconometrics: Methods and Applications. Cambridge, UK: Cambridge University Press.Google Scholar
  5. Crime & Justice Institute. (2004). Implementing Evidence-Based Practices in Community Corrections: The Principles of Effective Interventions. Washington, D.C.: National Institute of Corrections.Google Scholar
  6. Gendreau, P., Little, T., & Goggin, C. (1996). Predicting Adult Offender Recidivism: What Works! User Report Ottawa: Solicitor General of Canada.Google Scholar
  7. Gottfredson, S., & Moriarty, L. (2006). Statistical Risk Assessment: Old Problems and New Applications. Crime & Delinquency , 28-37.Google Scholar
  8. Greene, W. (2008). Econometric Analysis (6th ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
  9. Hjorth, J. (1994). Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap. Boca Rotan: Chapman & Hall.Google Scholar
  10. Imbens, G. (2004). Noparametric Estimation of Average Treatment Effects under Exogeneity: A Review. Review of Economics and Statistics , 4-29.Google Scholar
  11. Imbens, G., & Wooldridge, J. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1) .Google Scholar
  12. Kalbfleisch, J., & Prentice, R. (1980). The Statistical Analysis of Failure Time Data. New York, NY: John Wiley and Sons.Google Scholar
  13. Lancaster, T. (1990). The Econometric Analysis of Transition Data. Cambridge, UK: Cambridge University Press.Google Scholar
  14. Lee, M. (2005). Micro-Econometrics for Policy, Program and Treatment Effects. Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
  15. Lowenkamp, C., Latesse, E., & Holsinger, A. (2006). The Risk Principal in Action: What Have We Learned from 13, 676 Offenders and 97 Correctional Programs? Crime and Delinquency, 52(1), 77–93.CrossRefGoogle Scholar
  16. Morgan, S., & Winship, C. (2007). Counterfactuals and Causal Inferences: Methods and Principals for Social Research. Cambridge, UK: Cambridge University Press.Google Scholar
  17. Rhodes, W. (1986). A Survival Model with Dependent Competing Events and Right-Hand Censoring: Probation and Parole as an Illustration. Journal of Quantitative Criminology , 113-137.Google Scholar
  18. Rosenbaum, P. (2002). Observational Studies, 2nd Edition. Springer-Verlag.Google Scholar
  19. Taxman, F. (2008). No Illusions: Offender and Organizational Change in Maryland's Proactive Community Supervision Efforts. Criminology & Public Policy , 275-302.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Abt Associates IncCambridgeUSA

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