Stephen M. Haas

Assessing the “Statistical Accuracy” of the National Incident-Based Reporting System Hate Crime Data

The current study introduces a method to assess hate crime classification error in a state Incident-Based Reporting System. The study identifies and quantifies the “statistical accuracy” of aggregate hate crime data and provides insight from frontline officers about thought processes involved with classifying bias offenses. Random samples of records from two city and two county agencies provided data for the study. A systematic review of official case narratives determined hate crime classification error using state and federal definitions. A focus group sought to inquire about officers’ handling of hate crimes. Undercounting of hate crimes in official data was evident. When error rates were extrapolated, National Incident-Based Reporting System Group A hate crimes were undercounted by 67%. Officers’ responses validated complications involved with classifying hate crimes, particularly, incidents motivated “in part” by bias. Classification errors in reporting hate crimes have an impact on the statistical accuracy of official hate crime statistics. Officers’ offense descriptions provided greater awareness to issues with accurately interpreting and classifying hate crimes. The results yield useful information for officer training, understanding the true magnitude of these crimes, and a precursor for adjusting crime statistics to better estimate the “true” number of hate crimes in the population.

Predicting Client Success in Day Report Centers: The Importance of Risk and Needs Assessment

Research indicates that day report center (DRC) clients who complete their programs successfully are less likely to recidivate and tend to experience better postrelease outcomes. However, little is currently known about the predictive factors associated with successful program completion. Using a statewide sample of more than 2,000 clients drawn from 22 DRC programs in a mid-Atlantic state, this study investigates the relationship between client characteristics and case outcome (success vs. failure), and examines how the manner in which clients exited the program affected their risk of recidivism during a 24-month period following their release. The results identify several predictors of client success, of which the most powerful is offender risk based on the Level of Service/Case Management Inventory (LS/CMI). These findings contribute to an emerging literature investigating the sources of success for offenders in community corrections programs and indicate that the LS/CMI is a valid predictor of a variety of recidivism outcomes.

Assessing the Risk of Nonsexual and Sexual Victimization Using Incident-Based Police Reports

Abstract Current research has affirmed that black women are most at risk for rape, assault, and intimate partner violence in the United States. These findings are often based on statistics from surveys like the National Crime Victimization Survey (NCVS). The NCVS collects data from a stratified sample of households in the United States from which one can establish victimization risk and rates at the national level. We know very little about a persons risk of violent crime victimization from police records at the local and state level because until recently the data were not available. This study, therefore, adds to current victimization research by utilizing state-level police data to examine violent crime victimization patterns. Specifically, using data from the U.S. Census Bureau and West Virginia Incident-Based Reporting System (WVIBRS), we construct a model to examine the risk of nonsexual and sexual victimization over a lifetime by sex and race. Our findings indicate that black females in West Virginia have the highest probability of experiencing a nonsexual and sexual victimization over their lifetime. They also have the highest risk of multiple victimizations for these crimes.