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Cognitive abilities and antisocial behavior in prison: A longitudinal assessment using a large state-wide sample of prisoners

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  • Silver, Ian A.
  • Nedelec, Joseph L.

Abstract

Cognitive abilities have been shown to have both direct and indirect effects on antisocial behavior in a wide variety of contexts, including inmate misconduct. Nevertheless, although the findings have been robust, no assessments have offered an examination of the association between cognitive abilities and longitudinal variation in the frequency of inmate misconduct during imprisonment. In an effort to address this gap within the literature, the current study directly examines the longitudinal association between cognitive abilities and the frequency of inmate misconduct during imprisonment. Analyses were conducted using data collected during the state-wide Evaluation of Ohio's Prison Programs. The analytical sample of N = 88,145 and the 5 ½ year period represent one of the largest and longest assessments of the frequency of inmate misconduct clusters within prison and the first to examine the influence of cognitive abilities on such clustering. The results of growth curve analyses (GCA) indicated that higher cognitive abilities were associated with a lower intercept and a more gradual decline in the frequency of misconduct over time when compared to individuals with lower cognitive abilities. This pattern was also partially supported by the misconduct clusters estimated during latent class growth analysis (LCGA). Overall, the findings indicate that cognitive abilities affect both the clustering and the frequency of prison misconduct.

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  • Silver, Ian A. & Nedelec, Joseph L., 2018. "Cognitive abilities and antisocial behavior in prison: A longitudinal assessment using a large state-wide sample of prisoners," Intelligence, Elsevier, vol. 71(C), pages 17-31.
    • Handle: RePEc:eee:intell:v:71:y:2018:i:c:p:17-31
    DOI: 10.1016/j.intell.2018.09.004
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    References listed on IDEAS

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    1. Morris, Robert G. & Carriaga, Michael L. & Diamond, Brie & Piquero, Nicole Leeper & Piquero, Alex R., 2012. "Does prison strain lead to prison misbehavior? An application of general strain theory to inmate misconduct," Journal of Criminal Justice, Elsevier, vol. 40(3), pages 194-201.
    2. Hochstetler, Andy & DeLisi, Matt, 2005. "Importation, deprivation, and varieties of serving time: An integrated-lifestyle-exposure model of prison offending," Journal of Criminal Justice, Elsevier, vol. 33(3), pages 257-266.
    3. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
    4. Cihan, Abdullah & Sorensen, Jonathan & Chism, Kimberly A., 2017. "Analyzing the offending activity of inmates: Trajectories of offense seriousness, escalation, and de-escalation," Journal of Criminal Justice, Elsevier, vol. 50(C), pages 12-18.
    5. Steiner, Benjamin & Butler, H. Daniel & Ellison, Jared M., 2014. "Causes and correlates of prison inmate misconduct: A systematic review of the evidence," Journal of Criminal Justice, Elsevier, vol. 42(6), pages 462-470.
    6. Reidy, Thomas J. & Cihan, Abdullah & Sorensen, Jon R., 2017. "Women in prison: Investigating trajectories of institutional female misconduct," Journal of Criminal Justice, Elsevier, vol. 52(C), pages 49-56.
    7. Ttofi, Maria M. & Farrington, David P. & Piquero, Alex R. & Lösel, Friedrich & DeLisi, Matthew & Murray, Joseph, 2016. "Intelligence as a protective factor against offending: A meta-analytic review of prospective longitudinal studies," Journal of Criminal Justice, Elsevier, vol. 45(C), pages 4-18.
    8. Silver, Ian A. & Nedelec, Joseph L., 2018. "The moderating effects of intelligence: An examination of how IQ influences the association between environmental factors and antisocial behavior," Journal of Criminal Justice, Elsevier, vol. 54(C), pages 62-75.
    9. Boccio, Cashen M. & Beaver, Kevin M. & Schwartz, Joseph A., 2018. "The role of verbal intelligence in becoming a successful criminal: Results from a longitudinal sample," Intelligence, Elsevier, vol. 66(C), pages 24-31.
    10. Karwowski, Maciej & Kaufman, James C. & Lebuda, Izabela & Szumski, Grzegorz & Firkowska-Mankiewicz, Anna, 2017. "Intelligence in childhood and creative achievements in middle-age: The necessary condition approach," Intelligence, Elsevier, vol. 64(C), pages 36-44.
    11. Wright, Richard A. & Miller, J. Mitchell, 1998. "Taboo until today? The coverage of biological arguments in criminology textbooks, 1961 to 1970 and 1987 to 1996," Journal of Criminal Justice, Elsevier, vol. 26(1), pages 1-19.
    12. Boutwell, Brian B. & Meldrum, Ryan C. & Petkovsek, Melissa A., 2017. "General intelligence in friendship selection: A study of preadolescent best friend dyads," Intelligence, Elsevier, vol. 64(C), pages 30-35.
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    1. O'Connell, Michael & Marks, Gary N., 2021. "Are the effects of intelligence on student achievement and well-being largely functions of family income and social class? Evidence from a longitudinal study of Irish adolescents," Intelligence, Elsevier, vol. 84(C).

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