Open Access. Powered by Scholars. Published by Universities.®

Inequality and Stratification Commons

Open Access. Powered by Scholars. Published by Universities.®

SelectedWorks

Howard M Henderson

Articles 1 - 2 of 2

Full-Text Articles in Inequality and Stratification

On The Precipice Of Intersectionality: The Influence Of Race, Gender, And Offense Severity Interactions On Probation Outcomes, Kevin Steinmetz, Howard M. Henderson Apr 2015

On The Precipice Of Intersectionality: The Influence Of Race, Gender, And Offense Severity Interactions On Probation Outcomes, Kevin Steinmetz, Howard M. Henderson

Howard M Henderson

This analysis examines the impact of established predictors on probation failure utilizing a large randomly selected sample of adult probationers. Initial findings suggest that race, gender, location, offense severity as well as risk assessment scores significantly predict probation failure. This study then examines interaction effects between race and gender as well as race and offense severity. Results indicate such interactions may matter in studying probation failure, despite reason to be cautious about their interpretation. Importantly, the results of the interaction model suggest that the interaction between being an African American and male is a significant predictor of probation failure. Additionally, …


Psychometric Racial And Ethnic Predictive Inequities, Howard M. Henderson Apr 2015

Psychometric Racial And Ethnic Predictive Inequities, Howard M. Henderson

Howard M Henderson

Recent findings have held that offender behavioral assessments unfairly predict the probation outcomes of racial/ethnic minorities. To that end, this study examines the extent and degree to which a commonly used offender risk needs assessment instrument equitably predicts probationer success and distributes predictive error. Findings suggest that the risk needs instrument predicts most equitably for “higher risked” probationers and that error is more likely for under-classified Blacks and over-classified Whites. The discussion presents issues for consideration by policy makers, practitioners, and future researchers motivated by the minimization of predictive bias.