Designed to Fit

Abstract

Recidivism risk assessment tools have been utilized for decades. Although their implementation and use have the potential to touch nearly every aspect of the correctional system, the creation and examination of optimal development methods have been restricted to a small group of instrument developers. Furthermore, the methodological variation among common instruments used nationally is substantial. The current study examines this variation by reviewing methodologies used to develop several existing assessments and then tests a variety of design variations in an attempt to isolate and select those which provide improved content and predictive performance using a large sample (N = 44,010) of reentering offenders in Washington State. Study efforts were completed in an attempt to isolate and identify potential incremental performance achievements. Findings identify a methodology for improved prediction model performance and, in turn, describe the development and introduction of the Washington State Department of Correction’s recidivism prediction instrument—the Static Risk Offender Need Guide for Recidivism (STRONG-R).

A More Consistent Application of the RNR Model

“…Specifically, once an offender’s responses have been collected on all items in the STRONG assessment item pool (AIP), these responses are weighted and scored. Computations provided by STRONG-R algorithms (see Hamilton et al, 2016) place offenders in one of five risk levels—(5) High Violent, (4) High Property, (3) High Drug, (2) Moderate (general) Felony, and (1) Low (general) Felony. 7 This hierarchical ranking of risk was established based on the WADOC priority of recidivism prevention.…”

“…Empirically static items were removed, resulting in a total of 63 dynamic need items for potential inclusion. A list of STRONG items and responses was previously provided by Hamilton and colleagues (2016). Similar to the instruments discussed, offender needs items are separated based primarily on RNR’s conception of primary content categories (see Girard & Wormith, 2004): (a) Education, (b) Employment, (c) Friends/Peers, (d) Residential, (e) Family, (f) Substance Abuse, (g) Mental Health, (h) Aggression, and (i) Attitudes/Behaviors.…”

“…Current assessment instruments commonly create an individual’s global risk score by blending dynamic criminogenic needs with static risks, essentially treating needs items as risk factors. Much of the field agrees that when combining risk and needs into a single instrument, one is provided with a more accurate prediction of recidivism risk (e.g., Andrews & Bonta, 2010; Hamilton et al, 2016); however, critics attuned to the everyday workings of correctional assessment have found many needs variables included on current instruments to be irrelevant when predicting recidivism (Baird, 2009; Barnoski & Aos, 2003; Caudy, Durso, & Taxman, 2013; Hamilton & van Wormer, 2015). Essentially, some instruments collect needs items that are not associated with any measure of recidivism (Austin, Coleman, Peyton, & Johnson, 2003).…”

“…A common need for individuals in one population may be less prevalent in another (Brennan et al, 2012; Daly, 1992; Salisbury & Van Voorhis, 2009; Sampson, 2014). This is one reason researchers have attempted to demonstrate that the accuracy of prediction can be substantially improved by weighting model coefficients for the intended population and outcome (Austin et al, 2003; Barnoski & Aos, 2003; Brennan, 2007; Duwe, 2014; Einhorn & Hogarth, 1975; Hamilton et al, 2016; Silver, Smith, & Banks, 2000). Tailoring assessments to a population (e.g., state, parole, probation, etc.)…”

“…Tailoring assessments to a population (e.g., state, parole, probation, etc.) by weighting coefficients allows the instrument to be more predictive, take into account empirical and theoretical nuances of the population 3 (Hamilton et al, 2016; Latessa et al, 2010; Van Voorhis, Wright, Salisbury, & Bauman, 2010; Wright, Clear, & Dickson, 1984), and more accurately assist clinicians performing case management in a given jurisdiction (Brennan & Oliver, 2000; Hannah-Moffat, 2006; Taylor & Blanchette, 2009; Van Voorhis et al, 2010). We contend that this concept can be consistently tested and applied to both risk and needs tools.…”