JOURNAL ARTICLE
Attributions of Sexual Assault: Effects of Victim and Perpetrator Stereotypes, Presentation Order, and Participant Characteristics.
Published In: Journal of Interpersonal Violence, 2025, v. 40, n. 3/4. P. 629 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Dickinson, Olivia B.; Roberts, Michael E. 3 of 3
Abstract
This article examines how victim and perpetrator stereotypicality, the order in which their accounts are presented, and participant characteristics influence judgments of sexual assault scenarios. Two studies using diverse samples recruited via the online platform Prolific manipulated these factors in written assault vignettes, finding that victim stereotypicality consistently affected attributions of blame and credibility, while perpetrator stereotypicality showed significant effects only in the second study with stronger manipulations. Presentation order also influenced judgments, supporting a spreading activation model where multiple factors interactively shape evaluations. Additionally, participant gender and race impacted responses, with women generally attributing less victim blame and more perpetrator blame, and racial differences observed in blame attributions and credibility ratings. The findings highlight the potential implications for legal proceedings and media coverage, suggesting that irrelevant victim and perpetrator characteristics and the sequence of information presentation can bias perceptions of sexual assault.
Additional Information
- Source:Journal of Interpersonal Violence. 2025/02, Vol. 40, Issue 3/4, p629
- Document Type:Article
- Subject Area:Law
- Publication Date:2025
- ISSN:0886-2605
- DOI:10.1177/08862605241253035
- Accession Number:181917275
- Copyright Statement:Copyright of Journal of Interpersonal Violence is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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