A Pathway Approach to the Study of Bias Crime Offenders, United States, 1990-2018 (ICPSR 38157)

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The purpose of this project was to advance research on bias crime by providing investigators, practitioners, and policymakers with a dataset of bias crime offenders that is based on a national sample. The Bias Incidents and Actors Study (BIAS) dataset includes offenders who committed violent and non-violent bias crimes that were motivated by one or more of the bias categories recognized by the FBI's Hate Crime Statistics Program (Uniform Crime Reports, 2018). These include bias based on (1) race, ethnicity, or ancestry; (2) religion; (3) sexual orientation, gender, or gender identity; and (4) disability. Given its presence as a bias category in many state and local hate crime laws, the BIAS data also include offenders who were motivated by age discrimination.

An overarching goal of this project was to inform criminal justice policy in the United States by assisting in the development of risk assessment tools for hate crime offenders. The data were designed to be of particular use for the identification of offenders who may be at an increased risk of committing violent crimes or mass casualty attacks. Specific research questions for this project included:

1. What are the most common characteristics of United States bias crime offenders?
2. How do bias crime offenders differ across offender types (e.g., violent/non-violent, primary motivation)?
3. What explains why some individuals who harbor hateful beliefs engage in violent crimes while others do not?
4. What characteristics distinguish mass casualty hate crime offenders from other offender types?
5. Is it possible to expand on existing hate crime offender typologies by identifying new types or by further conceptualizing the characteristics and differences of offenders?

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The Bias Incidents and Actors Study (BIAS) builds on the National Institute of Justice-funded project, Empirical Assessment of Domestic Radicalization (EADR), which produced the Profiles of Individual Radicalization in the United States (PIRUS) database (Jensen et al., 2020). Bias crime was defined as a criminal offense that is at least partially motivated by some form of identity-based prejudice. The research team used open sources to identify and code the relevant attributes of hate crime offenders. The following criteria were established for inclusion in the BIAS dataset:

1. The subject was arrested or indicted for committing a criminal offense in the United States from 1990-2018;
2. The subject was 18 years of age or older at the time of engaging in the criminal act;
3. The subject was residing in the United States at the time of engaging in the criminal act;
4. There is substantial evidence that the subject committed or escalated the criminal act because of bias against the victim or target's real or perceived identity characteristics (e.g., race, nationality, sexual orientation, religious affiliation, etc.);
5. There is enough information about the subject in open-source materials to code the relevant details of their crimes and, at a minimum, the majority of their demographic traits.

Potential cases were identified using a variety of approaches. First, the research team reviewed all of the individuals in PIRUS according to the inclusion criteria, which yielded over 300 qualifying cases, all of which were ultimately included in the BIAS dataset. Second, following the original PIRUS development model, the research team conducted Boolean searches in several news media aggregators to construct a name list of potential subjects. Approximately 35,000 news articles were reviewed, yielding an initial list of nearly 3,800 subjects for further review. The research team also searched watchdog reports and other criminal databases to identify additional subjects for consideration. Finally, targeted searches were conducted to identify potential names for inclusion in particularly small populations of offenders, including female perpetrators, as well as those who may have been motivated by views that do not garner as much news attention, such as offenders targeting people with disabilities, Native Americans, and transgender persons.

Using open-source materials, including court records, news articles, biographies, transcribed interviews, and personal statements, the research team recorded the relevant details about offenders using a structured coding template and detailed codebook. Approximately 15 percent of the cases were double coded to ensure inter-coder reliability, and a systematic approach was used for addressing missing data.

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The study included a semi-random sample drawn from a name list of 4,000 potential subjects. The name list was generated through Boolean searches in news aggregators for hate crime offenses from 1990-2018.

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Universe View help for Universe

Bias crime offenders in the United States, 1990-2018.

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Original Release Date View help for Original Release Date

2024-07-30

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