Abstract

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<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>The purpose of this paper is to evaluate the ability of a comprehensive set of covariates to distinguish and predict juvenile sex offenders (JSOs) from non-sexual juvenile offenders (NSJOs) using demographic traits, criminality covariates, childhood trauma, and psychopathologies in a sample of male and female juvenile offenders in the USA.</jats:p>
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<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>A multivariate binary logistic regression will be conducted on a total of 64,329 juvenile offenders in Florida to determine what demographic, criminal history, childhood traumas, and psychopathologies make a difference in identifying sexual and NSJOs while controlling for the other key predictors in the model.</jats:p>
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<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>Results indicate that having an earlier age of criminal onset and more felony arrests, experiencing sexual abuse or being male, having low empathy, high impulsivity, depression, and psychosis all significantly increase the risk of sexual vs non-sexual offending among the male and female juvenile offenders, even while controlling for all other key covariates in the analysis.</jats:p>
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<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>This study uncovered many new findings regarding the key distinguishing traits of juvenile sex offending vs non-sexual offending, using a comprehensive list of predictors, a large sample of male and female offenders, and a rigorous statistical methodology.</jats:p>
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