The database for this study is entitled "Profiles of Individual Radicalization in the United States" (PIRUS). It includes 147 variables that cover demographic, background, group affiliation, and ideological information for 1,473 violent and non-violent extremists from across the ideological spectrum. This study analyzed the database using comparative descriptive statistics and multivariate logistic regression techniques. It produced 56 life-course narratives of individuals who were radicalized in the United States. These narratives were analyzed using fuzzy-set qualitative comparative analysis, a methodology that makes it possible to determine the causal conditions and pathways most salient for explaining radicalization to violence. Findings show the importance of considering age and gender when designing prevention and intervention programs. Older individuals were found to be especially prevalent among far-right individuals and single-issue milieus. Women were found in large numbers on the far left. Intervention and prevention programs should also target the face-to-face and virtual social networks that mobilize lone and group-based extremists to commit violent acts. This study also found that pre-radicalization criminal activity and post-radicalization clique membership are strongly associated with violent behaviors among radicalized individuals. This suggests attention to individuals with criminal histories and those known to associate with others holding extreme views. Individuals with a far-right ideology and those motivated by Salafi jihadist ideologies are more likely than animal-rights and environmental activists to engage in violence. The majority of extremists are driven by psychological and emotional vulnerabilities that stem from lost significance, personal trauma, and collective crises. Interventions that focus on particular communities are likely to be counterproductive, since they stimulate feelings of collective victimization. 27 tables, 4 figures, 159 references, and appended methodological details
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