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Predicting arrest probability across time: An exploration of competing risk perspectives

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  • Bolger, Michelle A.

Abstract

Criminal involvement is non-randomly distributed across individuals and across groups, resulting in differential probabilities of arrest. Thus, various predictors of arrest probability across time were examined for different groups.

Suggested Citation

  • Bolger, Michelle A., 2018. "Predicting arrest probability across time: An exploration of competing risk perspectives," Journal of Criminal Justice, Elsevier, vol. 59(C), pages 92-109.
    • Handle: RePEc:eee:jcjust:v:59:y:2018:i:c:p:92-109
    DOI: 10.1016/j.jcrimjus.2018.05.008
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    References listed on IDEAS

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    1. Vermunt, Jeroen K. & Magidson, Jay, 2003. "Latent class models for classification," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 531-537, January.
    2. Carmichael, Jason T., 2010. "Sentencing disparities for juvenile offenders sentenced to adult prisons: An individual and contextual analysis," Journal of Criminal Justice, Elsevier, vol. 38(4), pages 747-757, July.
    3. DeLisi, Matt & Vaughn, Michael G., 2014. "Foundation for a temperament-based theory of antisocial behavior and criminal justice system involvement," Journal of Criminal Justice, Elsevier, vol. 42(1), pages 10-25.
    4. Pollock, Wendi & Menard, Scott & Elliott, Delbert S. & Huizinga, David H., 2015. "It's Official: Predictors of Self-Reported vs. Officially Recorded Arrests," Journal of Criminal Justice, Elsevier, vol. 43(1), pages 69-79.
    5. Beaver, Kevin M. & Wright, John Paul & DeLisi, Matt & Vaughn, Michael G., 2008. "Genetic influences on the stability of low self-control: Results from a longitudinal sample of twins," Journal of Criminal Justice, Elsevier, vol. 36(6), pages 478-485, November.
    6. Stolzenberg, Lisa & D'Alessio, Stewart J., 2004. "Sex differences in the likelihood of arrest," Journal of Criminal Justice, Elsevier, vol. 32(5), pages 443-454.
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