How well do dynamic needs predict recidivism? Implications for risk assessment and risk reduction

Caudy, Michael S.; Durso, Joseph M.; Taxman, Faye S.

doi:10.1016/j.jcrimjus.2013.08.004

Cited by 82 publications

(44 citation statements)

References 37 publications

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“…For specific domains, 6 of the 10 were under .60 (an AUC of .50 suggest no relationship between the predictor and criterion). Other studies (see Caudy, Durso, & Taxman, 2013;Ostermann & Herrschaft, 2013;Ragusa-Salerno, Ostermann, & Thomas, 2013) also found similarly moderate relationships between recidivism and the LSI-R. Results with the YLS/CMI have also shown mixed results, with some studies showing substantial AUCs (Catchpole & Gretton, 2003;Viljoen, Elkovitch, Scalora, & Ullman, 2009) and others indicating more moderate scores (see Chu, Ng, Fong, & Teoh, 2012;Onifade et al, 2008).…”

Section: The Need For Additional Studies On Reliabilitymentioning

confidence: 93%

In the eye of the beholder? An examination of the inter-rater reliability of the LSI-R and YLS/CMI in a correctional agency

Rocque

Plummer-Beale

2014

Journal of Criminal Justice

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Section: The Need For Additional Studies On Reliabilitymentioning

confidence: 93%

In the eye of the beholder? An examination of the inter-rater reliability of the LSI-R and YLS/CMI in a correctional agency

Rocque

Plummer-Beale

2014

Journal of Criminal Justice

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“…As the analyses of parolee performance here indicated, the risk tools employed by both the parole and corrections in Pennsylvania could both withstand a good deal of improvement. Those analyses more generally also underscore the continuing relevance of the efforts in the risk-focused literature that emphasize the evolving risks and needs of the correctional populations (e.g., Andrews et al 2006;Caudy et al 2013;Kelly and Welsh 2008;Taxman and Caudy 2015). Thus, validating the risk tools that jurisdictions use on their own populations and recalibrating them over time should be at the forefront of the efforts that empirically-attuned jurisdictions undertake in order to implement evidence-based practices.…”

Section: Discussionmentioning

confidence: 96%

“…Burke 1990; Holsinger et al 2001;Langton 2006;Latessa 2011;Lowenkamp et al 2006). Moreover, growing awareness about the potential to affect greater change by targeting dynamic risk factors, more so than static factors that cannot be changed, has led to the wide adoption of the risk-and-needs responsivity model (Andrews et al 2006) and evolving discussions about the complexity and variation in offender criminogenic and reentry needs (e.g., Bauman et al 2014;Caudy et al 2013;Kelly and Welsh 2008;Taxman and Caudy 2015;Van Voorhis et al 2010). …”

Section: The Parole Mechanism In Pennsylvania and The Discretionary Natmentioning

confidence: 99%

Studying Parole in the “Spotlight”: Lessons from a Large American Jurisdiction

Vîlcică

2015

Eur J Crim Policy Res

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In September 2008 the Governor of Pennsylvania, USA, imposed a moratorium on all parole (early conditional) releases from the state prisons, which lasted until March 2009. As a political measure, the moratorium was triggered by a series of violent incidents involving recently released parolees culminating with the killings of several police officers. This paper documents the impact of the moratorium on the parole and correctional processes in the state and discusses its implications for the legitimacy of the two justice agencies affected, the Board of Parole and the Department of Corrections. The paper also describes the research undertaking of the team tasked with conducting the comprehensive review of parole and corrections at the Governor's request, which circumscribed the lifting of the moratorium on the results of the investigation. In addition to qualitative data, the study employed quantitative methods to investigate through predictive analyses both parole decisions and parolee performance upon release. The challenges of conducting action research in the "spotlight" are also highlighted. The nature of the study and its setting in a large state in the United States should provide a useful illustration of problems and potential approaches to dealing with them that similar crime prevention tasks may face in other jurisdictions around the world relying on conditional release as a means of prison release.

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“…Consistent with research findings that have long shown criminal history is a strong predictor of future criminal behavior, instruments that consist entirely of static (mostly criminal history) measures can achieve adequate predictive validity (Hamilton et al., ). Most results reported in the published risk assessment literature suggest that dynamic factors can also increase predictive validity, although there have been instances in which dynamic items have not improved predictive performance, at least for the LSI‐R (Caudy et al, ; Duwe and Rocque, ). Results from the Duwe and Kim () study suggest that use of continuous measures can provide a modest bump in predictive performance compared with ordinal measures, which in turn generally yield better performance than do binary items .…”

Section: Reliability and Validity Of Risk Assessment Instrumentsmentioning

confidence: 99%

Effects of Automating Recidivism Risk Assessment on Reliability, Predictive Validity, and Return on Investment (ROI)

Duwe

Rocque

2017

Criminology & Public Policy

108

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Research Summary The relationship between reliability and validity is an important but often overlooked topic of research on risk assessment tools in the criminal justice system. By using data from the Minnesota Screening Tool Assessing Recidivism Risk (MnSTARR), a risk assessment instrument the Minnesota Department of Corrections (MnDOC) developed and began using in 2013, we evaluated the impact of inter‐rater reliability (IRR) on predictive performance (validity) among offenders released in 2014. After comparing the reliability of a manual scoring process with an automated one, we found the MnSTARR was scored with a high degree of consistency by MnDOC staff as intraclass correlation (ICC) values ranged from 0.81 to 0.94. But despite this level of IRR, we still observed a degradation in predictive validity given that automated assessments significantly outperformed those that had been scored manually. Additional analyses revealed that the more inter‐rater disagreement increased, the more predictive performance decreased. The results from our cost–benefit analyses, which examined the anticipated impact of the MnDOC's efforts to automate the MnSTARR, showed that for every dollar to be spent on automation, the estimated return will be at least $4.35 within the first year and as much as $21.74 after the fifth year. Policy Implications Although it is unclear the degree to which our findings, which are somewhat preliminary, are generalizable to other offender populations and correctional systems, we believe the results are sufficiently promising to warrant greater interest in automating the assessment of risk and need. We anticipate many, if not most, correctional systems may need to invest in upgrading their IT infrastructure to support the use of automated instruments. But we also anticipate this investment would deliver a favorable return for our results suggest that automation reduces inter‐rater disagreement, which in turn improves predictive performance. Even if automation did not improve performance, the increased efficiency it produces would create reinvestment opportunities within correctional systems.

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How well do dynamic needs predict recidivism? Implications for risk assessment and risk reduction

Cited by 82 publications

References 37 publications

In the eye of the beholder? An examination of the inter-rater reliability of the LSI-R and YLS/CMI in a correctional agency

In the eye of the beholder? An examination of the inter-rater reliability of the LSI-R and YLS/CMI in a correctional agency

Studying Parole in the “Spotlight”: Lessons from a Large American Jurisdiction

Effects of Automating Recidivism Risk Assessment on Reliability, Predictive Validity, and Return on Investment (ROI)

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