Skip to main content
SpringerLink
Log in

The Andrews’ principles of risk, needs, and responsivity as applied in drug treatment programs: meta-analysis of crime and drug use outcomes

  • Published:
Journal of Experimental Criminology Aims and scope Submit manuscript

Abstract

Objectives

The purpose of the present meta-analysis was to answer the question: Can the Andrews principles of risk, needs, and responsivity, originally developed for programs that treat offenders, be extended to programs that treat drug abusers?

Methods

Drawing from a dataset that included 243 independent comparisons, we conducted random-effects meta-regression and ANOVA-analog meta-analyses to test the Andrews principles by averaging crime and drug use outcomes over a diverse set of programs for drug abuse problems.

Results

For crime outcomes, in the meta-regressions, the point estimates for each of the principles were substantial, consistent with previous studies of the Andrews principles. There was also a substantial point estimate for programs exhibiting a greater number of the principles. However, almost all the 95 % confidence intervals included the zero point. For drug use outcomes, in the meta-regressions, the point estimates for each of the principles was approximately zero; however, the point estimate for programs exhibiting a greater number of the principles was somewhat positive. All the estimates for the drug use principles had confidence intervals that included the zero point.

Conclusions

This study supports previous findings from primary research studies targeting the Andrews principles that those principles are effective in reducing crime outcomes, here in meta-analytic research focused on drug treatment programs. By contrast, programs that follow the principles appear to have very little effect on drug use outcomes. Primary research studies that experimentally test the Andrews principles in drug treatment programs are recommended.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. In a recent article, Andrews et al. (2011: 738) encourage the extension of the principles beyond corrections: “The theoretical and empirical base of RNR-based human service should be disseminated widely for purposes of enhanced crime prevention throughout the justice system and beyond (e.g., general mental health services).”

  2. We limited studies to those conducted the United States and Canada in part because we thought that studies from those two nations already entailed significant heterogeneity in correctional systems and drug abuse treatment approaches and in part to keep within the cost limits of the grant.

  3. The term “comparison group” is used throughout this paper rather than “control group” since it is more inclusive of the types of groups that were compared with the treatment group in the studies coded. Although some studies did have a “control group” typical of randomized control trials, others used other types of comparison groups.

  4. The codebook can be requested from the corresponding author.

  5. In the meta-regression models that included these methods covariates, the standardized regression coefficient is not equivalent to the correlation coefficient (r), as it is in the bivariate meta-regressions.

  6. Andrews’ criterion for high crime risk was whether most participants in a study had “penetrated the judicial system at the time of the study and had a prior criminal record” (Dowden and Andrews 2000: 455).

  7. A frequency table of these services can be obtained from the corresponding author.

  8. We did not identify and analyze services intended to address “drug use needs” (but see Pearson et al. 2012) To do so would have required a detailed review of the literature to identify “needs” associated with post-treatment drug use, which was beyond the scope of the study.

  9. One study in which the C group had more criminogenic-related services than the E group (resulting in a negative value) were dropped from analysis; this only applied to the analysis on drug outcomes, not to the analysis on crime outcomes.

  10. This method of coding responsivity differs from that used by Andrews. In his meta-analyses, responsivity is a dichotomous variable based on whether or not the program used with the E group used a social learning and/or cognitive-behavioral approach (see Andrews et al. 1990b; Dowden and Andrews 1999, 2000). Although our approach seems a stronger test of the Responsivity Principle, since it captures the difference in the responsivity of the two groups, a disadvantage is that studies are lost from analysis because of a differential pattern of missing data on the responsivity variable for the E condition and the C condition.

  11. In later meta-analyses, Andrews usually examined the principles separately (e.g., Andrews and Dowden 2006; Dowden and Andrews 1999).

  12. A reviewer asked whether the findings support the NIDA treatment effectiveness principle of matching: “Treatment varies depending on the type of drug and the characteristics of the patients. Matching treatment settings, interventions, and services to an individual’s particular problems and needs is critical to his or her ultimate success in returning to productive functioning in the family, workplace, and society” (NIDA 2012: 2). Although the Andrews’ principles and the NIDA principle are both concerned with the general concept of matching, the Andrews’ principles are more specific in defining the characteristics and needs of offenders on which matching should occur. The NIDA description of matching is much more general. Given these differences, using the findings from this paper to support (or not) the NIDA principle does not seem appropriate.

References

  • Andrews, D. A. (2006). Enhancing adherence to risk-need-responsivity: making quality a matter of policy. Criminology & Public Policy, 5(3), 595–602.

    Article  Google Scholar 

  • Andrews, D. A., & Bonta, J. (1998). The psychology of criminal conduct. Cincinnati: Anderson.

    Google Scholar 

  • Andrews, D. A., & Bonta, J. (2010). The psychology of criminal conduct (5th ed.). New Providence: Matthew Bender.

    Google Scholar 

  • Andrews, D. A., & Dowden, C. (2006). Risk principle of case classification in correctional treatment: a meta-analytic investigation. International Journal of Offender Therapy and Comparative Criminology, 50(1), 88–100.

    Article  Google Scholar 

  • Andrews, D. A., Bonta, J., & Hoge, R. D. (1990a). Classification for effective rehabilitation: rediscovering psychology. Criminal Justice and Behavior, 17, 19–52.

    Article  Google Scholar 

  • Andrews, D. A., Zinger, I., Hoge, R. D., Bonta, J., Gendreau, P., & Cullen, F. T. (1990b). Does correctional treatment work? A clinically-relevant and psychologically-informed meta-analysis. Criminology, 28(3), 369–404.

    Article  Google Scholar 

  • Andrews, D. A., Bonta, J., & Wormith, J. S. (2006). The recent pat and near future of risk and/or need assessment. Crime & Delinquency, 52(1), 7–27.

    Article  Google Scholar 

  • Andrews, D. A., Bonta, J., & Wormith, J. S. (2011). The risk-need-responsivity (RNR) model: does adding the good lives model contribute to effective crime prevention? Criminal Justice and Behavior, 38(7), 735–755.

    Google Scholar 

  • Bartol, C., & Bartol, A. (2011). Current perspectives in forensic psychology and criminal behavior (3rd ed.). Thousand Oaks: Sage.

    Google Scholar 

  • Begg, C. B. (1994). Publication bias. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 399–409). New York: Russell Sage.

    Google Scholar 

  • Belenko, S. (2006). Assessing released inmates for substance-abuse-related service needs. Crime & Delinquency, 52(1), 94–113.

    Article  Google Scholar 

  • Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York: Wiley.

    Book  Google Scholar 

  • Belsley, D. A., Kuh, E., & Welsch, R. E. (2004). Regression diagnostics: Identifying influential data and sources of collinearity 2nd edn. New York: Wiley.

  • Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2005). Comprehensive meta-analysis version 2. Englewood: Biostat.

    Google Scholar 

  • Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester: Wiley.

    Book  Google Scholar 

  • Byrne, J. M. (2012). New directions in community supervision: should we target high risk offenders, high risk times, and high risk locations? European Journal of Probation, 4(2), 77–101.

    Google Scholar 

  • CDCR [California Department of Corrections and Rehabilitation]. (2007). Expert panel on adult offender reentry and recidivism reduction programs. Sacramento: CDCR.

    Google Scholar 

  • Cécile, M., & Born, M. (2009). Intervention in juvenile delinquency: danger of iatrogenic effects? Children and Youth Services Review, 31(12), 1217–1221.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  • Colorado Division of Probation Services. (2010). Colorado probation and evidence-based practices: A systemic view of the past, present & future of EBP in Colorado probation. Retrieved December 17, 2012 from http://www.courts.state.co.us/userfiles/file/Administration/Probation/APPA_Presentation_Material/EBP_Report.pdf.

  • Correctional Service Canada. (2012). Federal community corrections strategy: Vision to 2020. Ottawa: Correctional Service Canada.

    Google Scholar 

  • Crawley, M. J. (2005). Statistics: An introduction using R. Chichester: Wiley.

    Book  Google Scholar 

  • Dodge, K. A., Dishion, T. J., & Lansford, J. E. (2006). Deviant peer influences in programs for youth: Problems and solutions. New York: Guilford.

    Google Scholar 

  • Dowden, C., & Andrews, D. A. (1999). What works for female offenders? A meta-analytic review. Crime and Delinquency, 45(4), 438–452.

    Article  Google Scholar 

  • Dowden, C., & Andrews, D. A. (2000). Effective correctional treatment and violent reoffending: a meta-analysis. Canadian Journal of Criminology, 42, 449–467.

    Google Scholar 

  • Gendreau, P., Little, T., & Goggin, C. (1996). A meta-analysis of the predictors of adult offender recidivism: what works! Criminology, 34(4), 575–607.

    Article  Google Scholar 

  • Guastaferro, W. P. (2012). Using the level of service inventory-revised to improve assessment and treatment in drug court. International Journal of Offender Therapy and Comparative Criminology, 56(5), 769–789.

    Article  Google Scholar 

  • Hannah-Moffat, K. (2005). Criminogenic needs and the transformative risk subject: hybridizations of risk/need in penalty. Punishment & Society, 7(1), 29–51.

    Article  Google Scholar 

  • Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. San Diego: Academic.

    Google Scholar 

  • Jolley, J. M., & Kerbs, J. J. (2010). Risk, need, and responsivity: unrealized potential for the international delivery of substance abuse treatment in prison. International Criminal Justice Review, 20(3), 280–301.

    Article  Google Scholar 

  • Koehler, J. A., Lösel, F., Akoensi, T. D., & Humphreys, D. K. (2013). A systematic review and meta-analysis on the effects of young offender treatment programs in Europe. Journal of Experimental Criminology, 9(1), 19–43.

    Article  Google Scholar 

  • Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA:Sage.

  • Lowenkamp, C.T., & Latessa, E.J. (2004). Understanding the risk principle: How and why correctional interventions can harm low-risk offenders. Topics in Community Corrections (Annual Issue 2004: Assessment Issues for Managers), pp. 3–8.

  • Lowenkamp, C. T., Latessa, E. J., & Holsinger, A. M. (2006). The risk principle in action: what have we learned from 13,676 offenders and 97 correctional programs? Crime & Delinquency, 52(1), 77–93.

    Article  Google Scholar 

  • MacKenzie, D. L. (2006). What works in corrections: Reducing the criminal activities of offenders and delinquents. New York: Cambridge University Press.

    Book  Google Scholar 

  • Marsch, L. A. (1998). The efficacy of methadone maintenance interventions in reducing illicit opiate use, HIV risk behaviors and criminality: a meta-analysis. Addiction, 93(4), 515–532.

    Article  Google Scholar 

  • McGuire, J. (2004). Understanding psychology and crime: Perspectives on theory and action. Berkshire: Open University Press.

    Google Scholar 

  • NIDA [National Institute on Drug Abuse]. (2012). Principles of drug addiction treatment: A research-based guide (3rd ed.). Bethesda: National Institute on Drug Abuse.

    Google Scholar 

  • Ogloff, J. R., & Davis, M. R. (2004). Advances in offender assessment and rehabilitation: contributions of the risk-need-responsivity approach. Psychology, Crime, and Law, 10(3), 229–242.

    Google Scholar 

  • Pearson, F.S., Prendergast, M., Podus, D., Vazan, P., Greenwell, L., Hamilton, Z. (2012). Meta-analyses of seven of NIDA’s principles of drug addiction treatment. Journal of Substance Abuse Treatment, 43, 1–11.

    Google Scholar 

  • Prendergast, M. L., Podus, D., Chang, E., & Urada, D. (2002). The effectiveness of drug abuse treatment: a meta-analysis of comparison group studies. Drug and Alcohol Dependence, 67(1), 53–72.

    Article  Google Scholar 

  • R Development Core Team. (2009). R: A language and environment for statistical computing, reference index version 2.10.0. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0 [Web Page]. Retrieved November 4, 2009, from www.R-project.org.

    Google Scholar 

  • Rosenthal, R. (1979). The “file drawer problem” and tolerance for null results. Psychological Bulletin, 86, 638–641.

    Article  Google Scholar 

  • Sharpe, D. (1997). Of apples and oranges, file drawers and garbage: why validity issues in meta-analysis will not go away. Clinical Psychology Review, 17, 881–901.

    Article  Google Scholar 

  • Taxman, F. S., & Thanner, M. (2006). Risk, need, and responsivity (RNR): it all depends. Crime & Delinquency, 52(1), 28–51.

    Article  Google Scholar 

  • Thanner, M. H., & Taxman, F. S. (2003). Responsivity: the value of providing intensive services to high-risk offenders. Journal of Substance Abuse Treatment, 24(2), 137–147.

    Article  Google Scholar 

  • Viechtbauer, W. (2009). Package “metafor” [Web Page]. Retrieved July 18, 2011, from www.metafor-project.org.

  • Viechtbauer, W., & Cheung, M. W.-L. (2010). Outlier and influence diagnostics for meta-analysis. Research Synthesis Methods, 1, 112–125.

    Article  Google Scholar 

  • Walfish, S. (2006). “A review of statistical outlier methods” in pharmaceutical technology [Web Page]. Retrieved July 18, 2011, from www.statisticaloutsourcingservices.com/Outlier2.pdf.

  • Ward, T., Melser, J., & Yates, P. M. (2007). Reconstructing the risk-need-responsivity model: a theoretical elaboration and evaluation. Aggression and Violent Behaviour, 12(2), 208–228.

    Article  Google Scholar 

  • Wilson, D.B. (2006). Meta-analysis stuff [Web Page]. Retrieved July 18, 2011, from http://mason.gmu.edu/~dwilsonb/ma.html.

Download references

Acknowledgments

This study was funded by the National Institute on Drug Abuse, grant R01 DA016600. The contents of this report are solely the responsibility of the authors and do not necessarily represent the views of the Department of Health and Human Services or the National Institute on Drug Abuse. We greatly appreciate the contributions of Aaron Brownstein, Ph.D., Anna Hyun, Ph.D., and Stephanie Kovalchik, Ph.D., the coders on the EPT project at UCLA, of Peter Vazan, Ph.D., a coder at NDRI, and of Stacy Calhoun, M.A., research associate on the UCLA team. Kory van Unen provided assistance with preparation of the paper. We are also grateful to Associate Editor David Wilson and to three anonymous reviewers for their comments and suggestions, which improved the paper.

Author information

Authors and Affiliations

  1. UCLA Integrated Substance Abuse Programs, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, 90025, USA

    Michael L. Prendergast & Deborah Podus

  2. National Development and Research Institutes, Inc., New York, NY, 10010, USA

    Frank S. Pearson

  3. Washington State University, Spokane, WA, 99210, USA

    Zachary K. Hamilton

  4. Office of Planning and Data Analytics, Los Angeles County Department of Health Services, Los Angeles, CA, 90012, USA

    Lisa Greenwell

  5. UCLA Integrated Substance Abuse Programs, 11075 Santa Monica Blvd., Suite 100, Los Angeles, CA, 90025, USA

    Michael L. Prendergast

Authors
  1. Michael L. Prendergast
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank S. Pearson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deborah Podus
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zachary K. Hamilton
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lisa Greenwell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael L. Prendergast.

Additional information

Studies included in each of the meta-analyses are available from the corresponding author.

Appendices

Appendix 1. Search terms

The following keywords and their combinations were used in the database searches, with the actual search strategy being adapted to each database: (substance or drug) and (abus* or misus* or use* or using or disorder* or depend* or addict*); (crack or cocain* or opiat* or heroin* or methadon* or cannabis* or marijuana or pcp or barbiturate* or benzodiazepin* or amphetamin* or methamphetamin* or polydrug*); (treatment* or rehabilitat* or intervention*); and (outcome* or result* or finding*).

Appendix 2. Coding for risk

Each study was coded for crime risk and drug use risk using the following guidelines. The examples for each risk level were intended to help anchor the categories, but coders were instructed to use any relevant baseline information to assess risk.

Crime risk

  • 1 = Low risk of committing a crime (Example 1: Less than 10 % of the subjects were in prison/jail or on parole/probation. Example 2: Less than 10 % had an arrest or a self-reported criminal act. Example 3: Less than 10 % were classified as having an antisocial personality. Example 4: Mean ASI legal composite score less than .05).

  • 2 = Medium risk of committing a crime (Example 1: 10 % to 39 % of the subjects were in prison/jail or on parole/probation, Example 2: 10 % to 39 % had an arrest or a self-reported criminal act. Example 3: 10 % to 39 % were classified as having an antisocial personality. Example 4: Mean ASI legal composite score was about .05 through .09).

  • 3 = High risk of committing a crime (Example 1: 40 % or more of the subjects were in prison/jail or on parole/probation. Example 2: 40 % or more had an arrest or a self-reported criminal act. Example 3: 40 % or more were classified as having an antisocial personality. Example 4: Mean ASI legal composite score was .10 or greater).

  • −8 = No information (or insufficient information) reported.

Drug use risk

  • 1 = Low risk of drug use (Example 1: Most subjects used an illicit drug AT MOST only 3 times per month. Example 2: Less than 10 % of the subjects had previously received treatment for drug abuse. Example 3: Mean ASI drug use composite score less than 0.05. Example 4: Used less than once a year).

  • 2 = Medium risk of drug use (Example 1: Typical subjects used an illicit drug roughly 4 to 9 times per month. Example 2: Roughly 10 % to 49 % of the subjects had previously received treatment for drug abuse. Example 3: Mean ASI drug use composite score was about .05 through .09. Example 4: Used drug for 2 or 3 years).

  • 3 = High risk of drug use (Example 1: Typical subjects used an illicit drug roughly 10 or more times per month. Example 2: Roughly 50 % to 100 % of the subjects had previously received treatment for drug abuse. Example 3: Mean ASI drug use composite score was .10 or greater. Example 4: Used drugs four or more years. Example 5: Has had a DSM diagnosis of drug abuse or dependence).

  • −8 = No information (or insufficient information) reported.

Appendix 3. Services coded as addressing criminogenic needs

Specific techniques to engage clients in treatment or motivate them for treatment

Specific “retention” techniques to keep clients in treatment

Clinically supervised sessions of positive peer/support groups

Techniques to change behavior by means of operant/reinforcement conditioning (even aversive conditioning), contingency management, contracting, and token economy programs

Techniques to change habits of thought and internal control, including self-reinforcement, cognitive skills, eliminating thinking errors, problem solving, self-instruction, self-rehearsal

Training in specific relapse prevention skills

Training to remedy deficits in education

Training to remedy deficits in vocational or employment skills

Training for parenting/child care

Specific negative consequences for specific behaviors, e.g., dirty urines causes revocation to incarceration

Rights and permissions

Reprints and permissions

About this article

Cite this article

Prendergast, M.L., Pearson, F.S., Podus, D. et al. The Andrews’ principles of risk, needs, and responsivity as applied in drug treatment programs: meta-analysis of crime and drug use outcomes. J Exp Criminol 9, 275–300 (2013). https://doi.org/10.1007/s11292-013-9178-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11292-013-9178-z

Keywords

Search

Navigation