Abstract
Reductions in criminal behavior are best achieved when offenders are placed in appropriate treatment programming options matched to the levels of risk and specific criminogenic needs. A major concern of the existing system is that the services and programs currently offered to offenders in either institutional (prison and jail) or community settings do not match the needs of the populations. In this chapter, we define the major variables used in a simulation model of the risk, need, responsivity framework. As discussed in Chap. 4, it is important to identify the core measures since there is no agreement on how to measure these particular constructs. In order to simulate the impact of different risk and need profiles of offenders placed in various responsivity levels, the model must be sensitive to the changes in parameters related to the concepts of treatment matching. We use this chapter to describe the variety of data sources used to create parameters to inform the RNR Simulation Model. This chapter begins with a description of the data sources and how they were prepared to create a synthetic database. (This refers to a constructed data file that has demographic characteristics of offenders, risk and need profiles, and expected outcomes.) This is followed by a discussion of the variables used to create the static risk and criminogenic need factors. Next is a discussion of measures of recidivism and the process used to combine risk, need, and recidivism information for offenders into a single database. This chapter concludes with a description of procedures used to validate the full synthetic database with information from several jurisdictions in the USA.
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Notes
- 1.
Bureau of Justice Statistics manuals were consulted for each data set to exclude certain subsets of data for which there were large amounts of missing data (more than 40% missing for any one variable). Means replacement was used to adjust other variables with minimal amounts of missing data.
- 2.
While the survey questions for these two samples were nearly identical, they were assigned different variable numbers in each data set. To combine the data from the two sources, the variables of interest in each were recoded with the same name. The SILJ data was then added to the SISFCF data using the “Add Data” function in SPSS.
- 3.
History of arrest was used as a proxy measure of recidivism in the Survey of Inmates data prior to matching with the actual Recidivism data.
- 4.
Numbers reported here reflect the results of the ROC analyses performed using rearrest after 3 years as the outcome variable. Results of the initial ROC analyses can be obtained from the author.
- 5.
An algorithm was applied to the synthetic data to adjust for the low prevalence of mental illness within the community sample.
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Ainsworth, S.A., Taxman, F.S. (2013). Creating Simulation Parameter Inputs with Existing Data Sources: Estimating Offender Risks, Needs, and Recidivism. 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_5
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