The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The efficacy of violence prediction: A meta-analytic comparison of nine risk assessment tools.
Abstract
Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their predictive efficacies for violence. The effect sizes were extracted from 28 original reports published between 1999 and 2008, which assessed the predictive accuracy of more than one tool. We used a within-subject design to improve statistical power and multilevel regression models to disentangle random effects of variation between studies and tools and to adjust for study features. All 9 tools and their subscales predicted violence at about the same moderate level of predictive efficacy with the exception of Psychopathy Checklist--Revised (PCL-R) Factor 1, which predicted violence only at chance level among men. Approximately 25% of the total variance was due to differences between tools, whereas approximately 85% of heterogeneity between studies was explained by methodological features (age, length of follow-up, different types of violent outcome, sex, and sex-related interactions). Sex-differentiated efficacy was found for a small number of the tools. If the intention is only to predict future violence, then the 9 tools are essentially interchangeable; the selection of which tool to use in practice should depend on what other functio…
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A meta-analysis of nine algorithmic approaches found only moderate levels of predictive accuracy across all approaches and concluded that these techniques should not be solely used for criminal justice decision-making, particularly in decisions of preventative detention.
“In addition, a meta-analysis of nine algorithmic approaches found only moderate levels of predictive accuracy across all approaches and concluded that these techniques should not be solely used for criminal justice decision-making, particularly in decisions of preventative detention (14).”
From The accuracy, fairness, and limits of predicting recidivism (2018) · cites this paper as [14]
Citations
Cites
Methods of Meta-Analysis: Correcting Error and Bias in Research Findings
Schmidt, F. L., & Hunter, J. E. (2015) · doi:10.4135/9781483398105 · Cited by 5,542
Hunter, J., & Schmidt, F. (2004) · doi:10.4135/9781412985031 · Cited by 3,023
“Clinical versus Actuarial Judgment.”
Dawes, R. M., Faust, D., & Meehl, P. E. (1989) · Science · doi:10.1126/science.2648573 · Cited by 2,298
QUANTITATIVE METHODS IN THE REVIEW OF EPIDEMIOLOGIC LITERATURE1
GREENLAND, S. (1987) · Epidemiologic Reviews · doi:10.1093/oxfordjournals.epirev.a036298 · Cited by 1,644 · cited 1×
“This approach has followed the principle of meta-regression methods (Greenland, 1987) for observational data with measurable moderators in epidemiology.”
MethodComparing effect sizes in follow-up studies: ROC Area, Cohen's d, and r.
Rice, M. E., & Harris, G. T. (2005) · Law and Human Behavior · doi:10.1007/s10979-005-6832-7 · Cited by 1,608 · cited 3×
“For eight studies that reported only the AUC values, a direct conversion of the AUC value to the d value was carried out on the basis of the table of M. E. Rice and Harris (2005).”
BackgroundMethodInfluentialHunter, J. E., & Schmidt, F. L. (2000) · International Journal of Selection and Assessment · doi:10.1111/1468-2389.00156 · Cited by 833 · cited 1×
“Meta-analyses conducted with random-effects models are now considered to be a standard approach for dealing with heterogeneity among studies and have, in many cases, superseded fixedeffects models (see Hunter & Schmidt, 2000).”
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The accuracy, fairness, and limits of predicting recidivism
Dressel, J., & Farid, H. (2018) · Science Advances · doi:10.1126/sciadv.aao5580 · Cited by 1,071
RISK, RACE, AND RECIDIVISM: PREDICTIVE BIAS AND DISPARATE IMPACT*
SKEEM, J. L., & LOWENKAMP, C. T. (2016) · Criminology · doi:10.1111/1745-9125.12123 · Cited by 317
Risk Assessment in Criminal Sentencing
Monahan, J., & Skeem, J. L. (2016) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-021815-092945 · Cited by 256
Saunders, J., Hunt, P., & Hollywood, J. S. (2016) · Journal of Experimental Criminology · doi:10.1007/s11292-016-9272-0 · Cited by 193
Layers of bias: A unified approach for understanding problems with risk assessment
Eckhouse, L., Lum, K., Conti-Cook, C., & Ciccolini, J. (2019) · Criminal Justice and Behavior · doi:10.1177/0093854818811379 · Cited by 156
Interpretable classification models for recidivism prediction
Zeng, J., Ustun, B., & Rudin, C. (2017) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12227 · Cited by 220
Racist Algorithms or Systemic Problems? Risk Assessments and Racial Disparities
Vincent, G. M., & Viljoen, J. L. (2020) · Criminal Justice and Behavior · doi:10.1177/0093854820954501 · Cited by 68
Santamaría-García, H., Baez, S., Aponte-Canencio, D. M., Pasciarello, G. O., Donnelly-Kehoe, P. A., Maggiotti, G., Matallana, D., Hesse, E., Neely, A., Zapata, J. G., et al. (2021) · Patterns · doi:10.1016/j.patter.2020.100176 · Cited by 18
Metadata
- Authors
- Yang, M., Wong, S. C. P., & Coid, J.
- Venue
- Psychological Bulletin
- Published
- 2010
- Volume
- 136
- Issue
- 5
- Pages
- 740-767
- ISSN
- 1939-1455
- 0033-2909
- Cited by
- 525
- References
- 6
- Language
- en
- Funders
- National Institute for Health Research (PP-PG-6407-10500)
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