This study introduces a method for assessing hate crime classification error in a State Incident-Based Reporting System.
The study identified and quantified the "statistical accuracy" of aggregate hate-crime data and provided insight from frontline officers about thought processes involved in 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 inquired 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 percent. 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 of issues in accurately interpreting and classifying hate crimes. The results provide 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. (Publisher abstract modified)