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

Datos Bibliográficos

ID3756744
AutoresJ Nolan (0000-0002-7817-1403, West Virginia University, autor de correspondencia), James J Nolan (West Virginia University, Morgantown, WV, USA), Stephen M Haa (0000-0001-5426-1861, West Virginia Bureau of Senior Services), Stephen M Haas (West Virginia Division of Justice and Community Services, Office of Research and Strategic Planning, Charleston, WV, USA), Erica Turley (West Virginia Division of Justice and Community Services, Office of Research and Strategic Planning, Charleston, WV, USA), Jake Stump (West Virginia University, Morgantown, WV, USA), Christina R Lavalle (0000-0003-1529-4761, West Virginia Division of Justice and Community Services, Office of Research and Strategic Planning, Charleston, WV, USA)
Año2015
Volumen59
Número12
Páginas1562-1587
Fecha de publicación2015-11-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaAmerican Behavioral Scientist (JOURNAL)
Identificadores de la revistaISSN: 0002-7642 • E-ISSN: 1552-3381
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/0002764215588813
OpenAlexW2159510200
IdiomaEN
Citas recibidas6
Referencias citadas11

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

Computer security · Criminology · Hate crime · Incident report · Officer · Political science · Computer Science · Crime Patterns and Interventions · Criminal Justice and Corrections Analysis · Gun Ownership and Violence Research · Law · Psychology