A study on the journey to homicide and offender characteristics in Spain

2021 ◽  
Author(s):  
Jorge Santos‐Hermoso ◽  
David Villalba‐García ◽  
Miguel Camacho‐Collados ◽  
Ricardo Tejeiro ◽  
José L. González‐Álvarez
Keyword(s):  
Offender Characteristics

Court Dispositions and Rearrest for Intimate Assault

2005 ◽  
Vol 51 (1) ◽  
pp. 75-102 ◽  
Author(s):  
John Wooldredge ◽  
Amy Thistlethwaite
Keyword(s):  
Substance Abuse ◽  
Domestic Violence ◽  
Residential Stability ◽  
Mandated Treatment ◽  
Entire Sample ◽  
Offender Characteristics ◽  
Court Mandated ◽  
Court Dispositions ◽  
Incidence Prevalence ◽  
Hamilton County

Researchers examining court dispositions and domestic violence recidivism have argued that disposition effectiveness varies by offender characteristics. We extended this research with analyses of 3,662persons arrested for misdemeanor assaults on intimates in Hamilton County, Ohio. The incidence, prevalence, and time to rearrest are examined for arrestees with no filed charges, subsequently dropped charges, court-mandated treatment, probation, jail, and split sentences. No filed charges and probation correspond with significant differences in all outcomes across the entire sample. Moreover, every disposition coincides with differences in rearrest for particular subgroups of arrestees (distinguished by violent histories, substance abuse, cohabitation, race, education, residential stability, and characteristics of neighborhood populations).

scholarly journals Bayesian Crime Investigations: Integrating Actuarial And Expert Models

2021 ◽  
Author(s):  
Jared C. Allen
Keyword(s):  
Regression Analysis ◽  
Prediction Accuracy ◽  
Linear Trend ◽  
Regression Equations ◽  
Criminal Offender ◽  
Offender Characteristics ◽  
Subjective Input ◽  
Expert Ratings ◽  
Analysis Models ◽  
Expert Models

In response to concerns that some of the most methodologically rigorous predictive studies of criminal offender characteristics may yet be less generalizable and applicable than advertised or assumed, this research first tests how well seven regression analysis models (represented by 28 equations) predict characteristics across three conditions: familiar cases (used to create the regressions), less familiar cases (native to the sample used to create the regressions) and foreign cases (from a similar but novel sample). Here a linear trend shows overfitting of the models to their own sample: a drop-off in prediction accuracy relative to simple mean-based prediction as cases become more foreign (ηp 2 = .646). In response to hopes that subjective input from expert police investigators could be integrated into the models to correct for this overfitting bias, this research also tests an algorithm combining expert ratings with the regression equations. Here moderate and significant improvement in novel-case prediction is observed overall (p = .036, r = .44) and equations for all twelve expert participants are shown to improve prediction to varying degrees. These results suggest that current best methods would perform poorly in the field, but can be improved by expert insight.

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