Is Everyone Rated Equal? An Examination of Factors Related to Sexual Risk in Ethnically Diverse Male Adolescents Who Have Sexually Offended

As the juvenile justice system has evolved, there has been a need for clinicians to make judgments about risk posed by adolescents who have committed sexual offenses. There are inherent difficulties in attempting to assess risk for violence among adolescents due to the developmental changes taking place and the absence of well-validated instruments to guide risk prediction judgments. With minority groups increasing in numbers in the U.S., it is likely that professionals will encounter minority individuals when conducting risk assessments. Overall questions regarding race/ethnicity have been neglected and there are few if any published research that explores risk factors with minority juvenile sex offenders. The present study examined whether differences exist between Caucasian and racial/ethnic minority adolescent sexual offenders on four risk assessment measures (J-SORRAT-II, J-SOAP-II, SAVRY, and ERASOR). The sample of 207 male adolescent sexual offenders was drawn from treatment facilities in a Midwestern state. Overall results indicated that minority adolescent sex offenders had fewer risk factors endorsed than Caucasian youth across all risk assessment tools. Exploration of interactions between race and factors such as: family status, exposure to family violence, and family history of criminality upon the assessment tools risk ratings yielded non-significant findings. Limitations, suggestions for future directions, and clinical implications are discussed.

Space–time zero-inflated count models of Harbor seals

Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space–time ZIP and hurdle models in a Bayesian hierarchical model. Space–time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space–time ZIP and hurdle models.