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The Empirical Basis for the RNR Model with an Updated RNR Conceptual Framework

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Simulation Strategies to Reduce Recidivism

Abstract

The risk-need-responsivity framework is based on a review of the empirical literature on offender factors that affect recidivism. The emphasis is on responsivity or the alignment of the risk and need profile with programs that are suitable to reduce recidivism. The empirical basis for the three-part framework is reviewed in this chapter with attention to integrating intervention science into a definition of responsivity. A review of the research literature since the original design of RNR (in the late 1980s) reveals that not all of the eight static and dynamic (criminogenic needs) risk components have a direct relationship with recidivism and that there are clinically relevant factors that need to be included when considering the effectiveness of an intervention, program, service, or control strategy. This chapter provides a different “sorting” of the risk and need factors besides the inclusion of gender and developmental (age) factors and clinically relevant factors (i.e., mental health status and substance abuse) that affect recidivism. The purpose is to lay the foundation for further chapters that describe the parameter and inputs into the RNR simulation model. This chapter illustrates the robustness of the original conceptual framework while demonstrating the elasticity to reframe the model based on new and emerging literature on factors that affect recidivism. Focusing attention on recidivism, and recent efforts to identify programmatic factors for different risk-need profiles, provides an opportunity to further refine the RNR model to be compatible with current research.

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Notes

  1. 1.

    The term programs will be used to refer to supervision strategies, treatment programs, services, and control techniques.

  2. 2.

    BJS is currently conducting a new study on a 2005 cohort, but these results are not yet available.

  3. 3.

    Prior arrest history is just one ingredient of a risk assessment tool.

  4. 4.

    These numbers do not control for criminal justice history.

  5. 5.

    While extralegal factors such as demographics may be empirically linked to recidivism outcomes, they are not generally included in risk assessments due to the potential for bias. Our conceptualization of the RNR model considers age and gender as key components of responsivity rather than risk.

  6. 6.

    There are versions of the ORAS for pretrial, prison, and reentry that have slightly fewer domains; see Latessa and Lovins (2010).

  7. 7.

    The discussion of criminogenic needs excludes a history of antisocial behavior and substance abuse. History of antisocial behavior is equivalent to criminal history risk. Both risk and substance use disorders are covered in detail above.

  8. 8.

    As discussed above, we suggest that substance dependence on hard drugs is a criminogenic need but substance abuse is a non-criminogenic need. Housing and mental health status are not included as dynamic needs in Andrews and Bonta’s model.

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Authors and Affiliations

  1. Department of Criminology, Law and Society, George Mason University, 10900 University Boulevard, Fairfax, VA, 20110, USA

    Faye S. Taxman, Michael S. Caudy & Joseph Durso

  2. School of Criminology and Justice Studies, University of Massachusetts Lowell, University Avenue 1, Lowell, MA, 01854, USA

    April Pattavina & James Byrne

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  1. Faye S. Taxman
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Correspondence to Faye S. Taxman .

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  1. Department of Criminology, Law, and Soci, George Mason University, University Blvd. 10900, Fairfax, 20110, Virginia, USA

    Faye S. Taxman

  2. , School of Criminology and Justice Studie, University of Massachusetts Lowell, University Avenue 1, Lowell, 01854, Massachusetts, USA

    April Pattavina

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© 2013 Springer Science+Business Media New York

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Taxman, F.S., Pattavina, A., Caudy, M.S., Byrne, J., Durso, J. (2013). The Empirical Basis for the RNR Model with an Updated RNR Conceptual Framework. In: Taxman, F., Pattavina, A. (eds) Simulation Strategies to Reduce Recidivism. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6188-3_4

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