Identifying Three Types of Violent Offenders and Predicting Violent Recidivism While on Probation: A Classification Tree Analysis

By Loretta J. Stalans, P. Yarnold, Magnus J. Seng, David E. Olson, M. Repp. 2004

85 citations recorded by the source corpus.

Subfields: Clinical Psychology, Health, 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. Min Yang
2. Jeremy Coid
3. Jiaming Zeng
4. Berk Ustun
5. Cynthia Rudin

In Health

1. Min Yang
2. Jeremy Coid

In Sociology and Political Science

1. Min Yang
2. Jeremy Coid
3. George E. Tita
4. Jiaming Zeng
5. Berk Ustun
6. Cynthia Rudin
7. Kristen M. Zgoba

Citations in the corpus

• Interpretable classification models for recidivism prediction (2015, methodology): “Stalans et al. (2004) also found that decision trees are better than logistic regression for prediction of violent recidivism, and Silver and Chow-Martin (2002) used trees as a meta-learner over multiple existing recidivism models.”
• Predicting Recidivism in Homicide Offenders Using Classification Tree Analysis (2011, methodology): “…et al., 2000; Classification Tree Analysis [CTA]—Stalans et al., 2004; or random-forests— Berk et al., 2009) with traditional actuarial methods (such as the Burgess method— nonweighted summation of risk factor scores—and logistic regressions; Silver et al., 2000; Stalans et al., 2004).”
• A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending (2011, methodology): “This finding is consistent with that of previous studies in forensic settings (Gardner et al. 1996; Hartvig et al. 2006; Rosenfeld and Lewis 2005; Stalens et al. 2004; Thomas et al. 2005) and in the broader medical field (Harper 2005 ;T u1996).”