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                            Funding & Awards
 Examining Radicalization's Risk and Protective Factors: A Case-control Study of Violent Extremists, Non-criminal Extremists & Regular Homicide Offenders
Award Information
Awardee
              MICHIGAN STATE UNIVERSITY
Award #
              2020-ZA-CX-0001
Funding Category
              Competitive Discretionary
Location
                    EAST LANSING, MI
Congressional District
              7
Status
              Past Project Period End Date
Funding First Awarded
              2020
Total funding (to date)
              $771,610
Original Solicitation
              Research and Evaluation of Domestic Terrorism Prevention, Fiscal Year 2020
Description of original award (Fiscal Year 2020, $771,610)
The proposed project aims to better understand which risk and protective factors increase or mitigate an individuals pathway to politically motivated violence, how individuals move from non-violent to violent participation, and the degree to which an individuals connection to a group impacts terrorist outcomes. This research proposes to add significant depth of understanding of the risk and protective factors related to violent extremism by exploring the direct effects, as well as how different factors work in combination, across four comparison groups: (1) violent extremist criminals, (2) non-violent extremist criminals, (3) non-criminal extremists, and (4) non-extremist homicide and attempted homicide offenders.
The project proposes to apply a case control design. The applicants will randomly select approximately 250 jihadist, far right, and far left extremists who committed or planned to commit an ideologically motivated violent crime and 250 who committed or planned to commit an ideologically motivated nonviolent financial crime between 2008 and 2017 in the United States. Next, 250 noncriminal extremists and 250 regular homicide or attempted homicide offenders (from the same period and residing in the same metropolitan area) will be selected to serve as the controls. The samples will be drawn from the Extremist Crime Database (ECDB) and Supplemental Homicide Reports (SHR), as well as social media and open-source data.
Sampling frames will be made comparable through stratified random sampling. Multiple data modeling schemes, including Bayesian Belief Networks, will be utilized in order to determine the relationship between various variables (i.e. demographic, socioeconomic status, abuse, psychological concerns, family relationships, and internet usage, among many others) and their role in predicting an attack.
Results will be disseminated to law enforcement and intelligence officials for the purposes of refining risk assessments, providing them with empirically informed comparative knowledge to determine which behaviors are most likely to mobilize into violence, and to help direct investigative priorities and guide prevention/intervention programming.
Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14). CA/NCF

  
Date Created: September 16, 2020
    
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