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Objectives <p>Despite its utility, one of the only existing typologies of hate crime offending in the American context was first created more than three decades ago. Whether the variation between offenses that was identified several decades ago—and in only one city—is generalizable to contemporary hate crimes thus remains an open empirical question. The current study seeks to establish an updated typology focusing on incident-based characteristics as a fundamental differentiator.</p> Methods <p>The current study uses data from the National Incident-Based Reporting System (NIBRS) on 17,622 hate crimes occurring across America to empirically identify classes of hate crimes using Latent Class Analysis (LCA) techniques.</p> Results <p>Findings indicate that the best fitting solution for categorizing heterogeneity in violent hate crime offending is a seven-class solution, focused on variation across two critical dimensions: bias motivation and offense severity.</p> Conclusions <p>Taken together, our results suggest the importance of considering who bias violence has targeted, as well as various dimensions of offense severity—such as weapon use, injuries sustained, and the crime committed—in order to better understand differences between hate crimes.</p>

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Hate-Motivated Violence in America: Conceptualizing an Expanded and Updated Typology Using Latent Class Analysis

  • 期刊论文

  • 发表日期：

    2025年6月26日

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  • Brendan Lantz,
  • Matthew Vanden Bosch,
  • Jack M. Mills,
  • Marin R. Wenger,
  • Zachary T. Malcom

摘要

Objectives

Despite its utility, one of the only existing typologies of hate crime offending in the American context was first created more than three decades ago. Whether the variation between offenses that was identified several decades ago—and in only one city—is generalizable to contemporary hate crimes thus remains an open empirical question. The current study seeks to establish an updated typology focusing on incident-based characteristics as a fundamental differentiator.

Methods

The current study uses data from the National Incident-Based Reporting System (NIBRS) on 17,622 hate crimes occurring across America to empirically identify classes of hate crimes using Latent Class Analysis (LCA) techniques.

Results

Findings indicate that the best fitting solution for categorizing heterogeneity in violent hate crime offending is a seven-class solution, focused on variation across two critical dimensions: bias motivation and offense severity.

Conclusions

Taken together, our results suggest the importance of considering who bias violence has targeted, as well as various dimensions of offense severity—such as weapon use, injuries sustained, and the crime committed—in order to better understand differences between hate crimes.