JOURNAL ARTICLE
Distinguishing lone from group actor terrorists: A comparison of attitudes, ideologies, motivations, and risks.
Published In: Journal of Forensic Sciences, 2023, v. 68, n. 1. P. 198 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Dhumad, Saleh; Candilis, Philip J.; Cleary, Sean D.; Dyer, Allen R.; Khalifa, Najat R. 3 of 3
Abstract
The increasing recognition of the risks posed by lone‐actor terrorists provides the impetus for understanding the psychosocial and ideological characteristics that distinguish lone from group actors. This study examines differences between lone and group actor terrorists in two domains: (i) attitudes toward terrorism, ideology, and motivation for terrorist acts; and (ii) empirically derived risk factors for terrorism. Using a cross‐sectional research design and primary source data from 160 men convicted of terrorism in Iraq, this study applied bivariate and logistic regression analyses to assess group differences. It tested the hypothesis that there are no statistically significant differences between the groups. Bivariate analyses revealed that lone actors were less likely than group actors, to be unemployed, to cite personal or group benefit as the main motives for terrorist activity, and to believe that acts of terrorism achieved their goals. Regression analysis indicated that having an authoritarian father was the only factor that significantly predicted group membership, with group actors three times more likely to report this trait. Lone actors and group actors are almost indistinguishable except for certain differences in attitudes, motives, employment, and having an authoritarian father. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Forensic Sciences. 2023/01, Vol. 68, Issue 1, p198
- Document Type:Article
- Subject Area:Political Science
- Publication Date:2023
- ISSN:0022-1198
- DOI:10.1111/1556-4029.15154
- Accession Number:161029990
- Copyright Statement:Copyright of Journal of Forensic Sciences is the property of Wiley-Blackwell 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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