Journal of Quantitative Criminology

, Volume 31, Issue 1, pp 149–181 | Cite as

Parolee Recidivism and Successful Treatment Completion: Comparing Hazard Models Across Propensity Methods

  • David J. Peters
  • Andy Hochstetler
  • Matt DeLisi
  • Hui-Ju Kuo
Original Paper



Ascertaining the effect of treatment on recidivism is a core area of investigation in criminology and corrections research. The two objectives of the current analysis are: (1) to determine the true effect of treatment regimen completion on time to recidivism controlling for propensity to complete treatment; and (2) to examine the sensitivity of results under various propensity score methods.


Drawing on the population (n = 1,270) of parolees residing in a Midwestern state, we examine the effect of completing a treatment regimen on days to recidivism (using two failure outcomes) over a 2-year period using proportional hazard models. We adjust for the propensity to complete a treatment regimen using the covariate adjustment, inverse weighting, case matching, and strata methods.


Completing a treatment regimen has a sizable effect at reducing recidivism risk, which grows stronger the longer offenders are on parole. This effect is consistent across treatment propensity methods. It is driven mainly by completion of alcohol and drug treatment regimens. Treatment effects are stable across two measures of recidivism (arrest/prison-return and prison-return only).


Discussion centers on the implications for assessing treatment success in the parole population as well as on methodological implications for researchers conducting similar research. In the current analysis propensity scores produce stable results regardless of propensity method. Guidance is provided on selecting propensity methods based on data distortion, technical expertise, and presentation of results. We conclude that the covariate adjustment method is best suited for novice researchers, and the case matching method for expert researchers. The strata method is recommended for supplemental analyses. Future research should examine treatment effects reporting at least two propensity methods.


Recidivism Treatment Corrections Propensity score Parole Hazard models 


  1. Ahlin EM, Zador PL, Rauch WJ, Howard JM, Duncan GD (2011) First-time DWI offenders are at risk of recidivating regardless of sanctions imposed. J Crim Just 39:137–142CrossRefGoogle Scholar
  2. Anderson J (2002) Overview of the Illinois DOC high-risk parolee reentry program and 3-year recidivism outcome of program participation. Cogn Behav Treat Rev 11:4–6Google Scholar
  3. Andrews DA, Bonta J (1999) The psychology of criminal conduct. Anderson, Cincinnati, OHGoogle Scholar
  4. Andrews DA, Bonta J, Hoge RD (1990) Classification for effective rehabilitation: rediscovering psychology. Crim Justice Behav 17:19–52CrossRefGoogle Scholar
  5. Apel RJ, Sweeten G (2010) Propensity score matching in criminology and criminal justice. In: Piquero A, Weisburd D (eds) Handbook of quantitative criminology. Springer, New York, pp 543–562CrossRefGoogle Scholar
  6. Arbogast PG, Seeger JD (2012) Summary variables in observational research: propensity scores and disease risk scores. Agency for healthcare research and quality, publication no. 11. Agency for Healthcare Research and Quality, Rockville, MDGoogle Scholar
  7. Austin PC (2009) The error rates, coverage of confidence intervals, and variance estimation in propensity-score matched analysis. Intl J Biostat 5:1557–1569CrossRefGoogle Scholar
  8. Austin PC (2011) An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res 46:399–424CrossRefGoogle Scholar
  9. Bales WD, Piquero A (2012) Assessing the impact of imprisonment on recidivism. J Exp Criminol 8:71–101CrossRefGoogle Scholar
  10. Bouffard JA, Bouffard LA (2011) What works (or doesn’t) in a DUI court? An example of expedited case processing. J Crim Just 39:320–328CrossRefGoogle Scholar
  11. Braga AA, Piehl AM, Hureau D (2009) Controlling violent offenders released to the community: an evaluation of the Boston Reentry Initiative. J Res Crim Delinq 46:411–436CrossRefGoogle Scholar
  12. Cullen FT (2005) The twelve people who saved rehabilitation: how the science of criminology made a difference. Criminology 43:1–42CrossRefGoogle Scholar
  13. Cullen FT, Gendreau P (2000) Assessing correctional rehabilitation policy: practice and prospects. Policies, processes and decisions of the criminal justice system. U.S. Department of Justice, Washington, DC, pp 109–174Google Scholar
  14. Cullen FT, Smith P, Lowenkamp CT, Latessa EJ (2009) Nothing works revisited: deconstructing Farabee’s Rethinking Rehabilitation. Vict Offenders 4:101–123CrossRefGoogle Scholar
  15. Diamond B, Morris RG, Caudill JW (2011) Sustaining families, dissuading crime: the effectiveness of a family preservation program with male delinquents. J Crim Just 39:338–343CrossRefGoogle Scholar
  16. Dowden C, Andrews DA (2000) Effective correctional treatment and violent reoffending: a meta-analysis. Can J Criminol 42:449–476Google Scholar
  17. Duwe G (2010) Prison-based chemical dependency treatment in Minnesota: an outcome Evaluation. J Exp Crim 6:57–81CrossRefGoogle Scholar
  18. Gendreau P, Little P, Goggin C (2006) A meta-analysis of the predictors of adult offender recidivism: what works. Criminology 34:575–608CrossRefGoogle Scholar
  19. Gibson CL, Swatt ML, Miller JM, Jennings WG, Gover AR (2012) The causal relationship between gang joining and violent victimization: a critical review and directions for future research. J Crim Just 40:490–501CrossRefGoogle Scholar
  20. Gu XS, Rosenbaum PR (1993) Comparison of multivariate matching methods: structures, distances, and algorithms. J Comput Graph Stat 2:405–420Google Scholar
  21. Hamilton Z (2010) Do reentry courts effect recidivism? Results from the Harlem Parole Reentry Court. Center for Court Innovation, New YorkGoogle Scholar
  22. Haviland A, Nagin DS, Rosenbaum PR (2007) Combining propensity-score matching and group based trajectory analysis in an observational study. Psychol Methods 27:247–267CrossRefGoogle Scholar
  23. Hilller ML, Knight K, Simpson DD (1999) Risk factors that predict dropout from corrections-based treatment for drug abuse. Prison J 79:411–430CrossRefGoogle Scholar
  24. Hosmer DW, Lemeshow S, May S (2008) Applied survival analysis. Wiley, Hoboken, NJCrossRefGoogle Scholar
  25. Illesca SR, Sanchez-Meca J, Genoves VG (2001) Treatment of offenders and recidivism: assessment of the effectiveness of programs applied in Europe. Psychol Spain 5:47–62Google Scholar
  26. Imai K, van Dyk DA (2004) Causal inference with general treatment regimes: generalizing the propensity score. J Am Stat Assoc 99:854–866CrossRefGoogle Scholar
  27. Jones AS, D’Agostino RB, Gondolf EW, Heckert A (2004) Assessing the effect of batterer program completion on reassault using propensity scores. J Interpers Violence 19:1002–1020CrossRefGoogle Scholar
  28. Jordan KL (2012) Juvenile transfer and recidivism: a propensity score matching approach. J Crim Just 35:53–67CrossRefGoogle Scholar
  29. Kim RH, Clark D (2013) The effect of prison-based college education programs on recidivism: propensity score matching approach. J Crim Just 41:196–204CrossRefGoogle Scholar
  30. Krebs CP, Strom KJ, Lattimore PK (2009) Impact of residential and nonresidential drug treatment on recidivism among drug-involved probationers: a survival analysis. Crime Delinq 55:442–471CrossRefGoogle Scholar
  31. Langan PA, Levin DJ (2002) Recidivism of prisoners released in 1994. Bureau of Justice Statistics, Washington, DCGoogle Scholar
  32. Lattimore PK, Steffey DM (2010) The multi-site evaluation of SVORI: methodology and analytic approach. Department of Justice, Washington, DCGoogle Scholar
  33. Lipsey MW, Cullen FT (2007) The effectiveness of correctional rehabilitation: a review of systematic reviews. Ann Rev Law Soc Sci 3:297–320CrossRefGoogle Scholar
  34. Losel F (1995) The efficacy of correctional treatment: a review and synthesis of meta evaluations. In McGuire J (ed) What works: reducing offending. Wiley, West, Sussex, England, pp 79–111Google Scholar
  35. Losel F, Schmucker M (2005) The effectiveness of treatment for sexual offenders: a comprehensive meta-analysis. J Exp Crim 1:117–146CrossRefGoogle Scholar
  36. Lowenkamp CT, Latessa EJ, Holsinger AM (2006) The risk principle in action: what have we learned from 13,676 offenders and 97 correctional programs? Crime Delinq 52:77–93CrossRefGoogle Scholar
  37. MacKenzie DL (2000) Evidence-based corrections: identifying what works. Crime Delinq 46:457–471CrossRefGoogle Scholar
  38. Marlowe DB, Festinger DS, Dugosh KL, Caron A, Podkopacz MR, Clements NT (2011) Targeting dispositions for drug-involved offenders: a field trial of the Risk and Needs Triage (RANT)™. J Crim Just 39:253–260CrossRefGoogle Scholar
  39. Martinson R (1974) What works? Questions and answers about prison reform. The public interest. Spring, pp 22–54Google Scholar
  40. McNiel DE, Binder R (2007) Effectiveness of mental health court in reducing criminal recidivism. Am J Psychiat 164:1395–1403CrossRefGoogle Scholar
  41. Mears DP, Cochran JC, Greenman SJ, Bhati AS, Greenwald MA (2011) Evidence on the effectiveness of juvenile court sanctions. J Crim Just 39:509–520CrossRefGoogle Scholar
  42. Mears DP, Cochran JC, Siennick SE, Bales WD (2012) Prison visitation and recidivism. Justice Q 29:888–918CrossRefGoogle Scholar
  43. Mitchell O, Wilson DB, Eggers A, MacKenzie DL (2012) Assessing the effectiveness of drug courts on recidivism: a meta-analytic review of traditional and non-traditional drug courts. J Crim Just 40:60–71CrossRefGoogle Scholar
  44. Mitchell-Herzfeld S, Shady TA, Mayo J, Han Kim D, Marsh K, Dorabawila V, Rees F (2008) Effects of multisystemic therapy on recidivism among juvenile delinquents in the state of New York. Office of Children and Family Services, New YorkGoogle Scholar
  45. Morenoff, JD, Harding DJ (2011) Final technicanl report: neighborhoods, recidivism, and employment among returning prisoners. U.S. Department of Justice, Washington, DCGoogle Scholar
  46. Morgan SL, Todd JL (2008) A diagnostic routine for the detection of consequential heterogeneity of causal effects. Sociol Methodol 38:231–281Google Scholar
  47. Pearson FS, Lipton DS, Cleland CM, Yee DJ (2002) The effects of behavioral/cognitive behavioral programs on recidivism. Crime Delinq 48:476–496CrossRefGoogle Scholar
  48. Rosenbaum PR (2010) Design of observational studies. Springer, New YorkCrossRefGoogle Scholar
  49. Rosenbaum PR, Rubin DB (1984) Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 79:515–524Google Scholar
  50. Sampson, RJ, Laub JH, Wimer C. (2006) Does marriage reduce crime? A counterfactual approach to within-individual causal effects. Criminology 44:465–508Google Scholar
  51. Shover N, Einstadter WJ (1988) Analyzing American corrections. Wadsworth, Belmont, CAGoogle Scholar
  52. Thoemmes FJ, Kim ES (2011) A systematic review of propensity score methods in the social sciences. Multivar Behav Res 46:90–118CrossRefGoogle Scholar
  53. Tillyer MS, Vose B (2011) Social ecology, individual risk, and recidivism: a multilevel examination of main and moderating influences. J Crim Just 39:452–459CrossRefGoogle Scholar
  54. Tripodi SJ, Bender K (2011) Substance abuse treatment for juvenile offenders: a review of quasi-experimental and experimental research. J Crim Just 39:246–252CrossRefGoogle Scholar
  55. Vose B, Lowenkamp CT, Smith P, Cullen FT (2009) Gender and the predictive validity of the LSI-R: a study of parolees and probationers. J Contemp Crim Just 25:459–471CrossRefGoogle Scholar
  56. Wermink H, Blokland A, Nieubeerta P, Tollenaar N (2010) Comparing the effects of community service and short-term imprisonment on recidivism: a matched samples approach. J Exp Criminol 8:71–101Google Scholar
  57. Wilson DB, Gallagher CA, MacKenzie DL (2000) A meta-analysis of corrections based education, vocation, and work programs for adult offenders. J Res Crime Delinq 37:347–368CrossRefGoogle Scholar
  58. Wimer C, Sampson RJ, Laub JH (2008) Estimating time-varying outcomes with application to incarceration and crime. In: Cohen P (ed) Applied data analytic techniques for turning points research. Routledge-Taylor and Francis, New York, pp 38–58Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • David J. Peters
    • 1
  • Andy Hochstetler
    • 2
  • Matt DeLisi
    • 3
  • Hui-Ju Kuo
    • 4
  1. 1.SociologyIowa State UniversityAmesUSA
  2. 2.SociologyIowa State UniversityAmesUSA
  3. 3.SociologyIowa State UniversityAmesUSA
  4. 4.SociologyIowa State UniversityAmesUSA

Personalised recommendations