The accuracy of recidivism risk assessments for sexual offenders: a meta-analysis of 118 prediction studies.

By R. Hanson, Kelly E Morton-Bourgon, Tony Beech, Susanne Bengtson, J. Bonta, L. Craig, J. Endrass, D. Epperson, G. Harris, Mark Hanlon and 15 more. 2009

1,009 citations recorded by the source corpus.

Subfields: Clinical Psychology, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Clinical Psychology

1. F. Lösel
2. Martin Schmucker
3. J. Bonta
4. L. Helmus
5. M. Campbell
6. R. K. Hanson
7. A. Harris
8. D. Thornton

In Sociology and Political Science

1. J. Bonta
2. F. Lösel
3. Martin Schmucker
4. M. Campbell
5. L. Helmus
6. R. K. Hanson
7. A. Harris
8. Demetra Andrews

Citations in the corpus

• The accuracy, fairness, and limits of predicting recidivism (2018, background): “For example, a large-scale meta-analysis of approaches to predicting recidivism of sexual offenders (11) found that actuarial measures, in which explicit data and explicit combination rules are used to combine the data into a single score, provide more accurate predictions than unstructured measures”
• Interpretable classification models for recidivism prediction (2015, methodology): “There has been continued interest in more principled evaluation methods for these programs in recent years (see for instance the review papers of Skeem and Monahan 2011, Hanson and Morton-Bourgon 2009).”
• The effects of sexual offender treatment on recidivism: an international meta-analysis of sound quality evaluations (2015, background): “Although there is much research on risk factors f r reoffending and structured assessment instruments (e.g. Hanson & Morton-Bourgon, 2009), the knowledge about the origins and causal mechanisms is less clear (e.g. Mann et al., 2010; Ward et al., 2005).”
• Rehabilitating criminal justice policy and practice. (2010, background): “…than unstructured clinical judgment in general (Ægisdóttir et al., 2006; Grove, Zald, Lebow, Snitz, & Nelson, 2000) and this conclusion holds equally in the assessment of offender risk for further crime (Andrews, Bonta, & Wormith, 2006; Bonta, Law, & Hanson, 1998; Hanson & Morton-Bourgon, 2009).”
• Assessment of Reoffense Risk in Adolescents Who Have Committed Sexual Offenses (2009, methodology): “However, a recent meta-analysis concluded that risk factors appear similar for adolescent and adult sexual offending (Hanson & Morton-Bourgon, 2009).”
• Comparative Analyses of the YLS/CMI, SAVRY, and PCL:YV in Adolescent Offenders: A 10-year Follow-Up Into Adulthood (2011, background): “For example, a meta-analysis of sex offender samples by Hanson and Morton-Bourgon (2009) found that the predictive validity of SPJ fell between unstructured clinical judgments of risk and actuarial risk predictions of sexual, violent, and general recidivism.”
• Sexual offender treatment for reducing recidivism among convicted sex offenders: a systematic review and meta‐analysis (2017, background): “Although there is much research on static and dynamic risk factors for reoffending and structured risk assessment instruments (e.g. Hanson & Morton-Bourgon, 2009), the knowledge about the origins and causal mechanisms is less clear (e.g. Mann et al., 2010; Ward et al., 2005).”
• Predictors of sex offender treatment dropout: psychopathy, sex offender risk, and responsivity implications (2011, methodology): “Results from the Hanson and Morton-Bourgon (2009) meta-analysis supported the predictive accuracy of the Static 99 for sexual (d 0.67, k 81) and violent (d 0.51, k 35) recidivism.”
• Assessing the Risk and Needs of Supervised Sexual Offenders (2015, background): “…officers, such as the Level of Service Assessments (Andrews, Bonta, & wormith, 2010) and the Statistical Information on Recidivism–Revised (SIR-R1; Nafekh & Motiuk, 2002), which also predict general recidivism among sexual offenders (Hanson & Morton-Bourgon, 2009; wormith, Hogg, & Guzzo, 2012).”
• False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks" (2016, neutral): “…that actuarial risk assessments are superior to unstructured professional judgment in terms of predicting the likelihood of both general recidi-vism and even specific recidivism outcomes (Grove, Zald, Lebow, Snitz, & Nelson, 2000), including future sex offending (Hanson & Morton-Bourgon, 2009).”
• Improving the Predictive Accuracy of Static-99 and Static-2002 With Older Sex Offenders (2012, positive): “…& Handel, 2006; Jackson & Hess, 2007; McGrath, Cumming, Burchard, Zeoli, & Ellerby, 2010) and they are more accurate than unstructured risk assessment methods (Bonta, Law, & Hanson, 1998; Dawes et al., 1989; Grove, Zald, Lebow, Snitz, & Nelson, 2000; Hanson & Morton-Bourgon, 2009; Mossman, 1994).”