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      Understanding (non)involvement in terrorist violence: What sets extremists who use terrorist violence apart from those who do not?

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      Criminology & Public Policy
      Wiley

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          Abstract

          Research summary

          We compare European and North American radicalization trajectories that led to involvement in terrorist violence ( n = 103) with those for which this outcome did not occur ( n = 103). Regression analyses illustrate how involvement in terrorist violence is determined not only by the presence of risk, but also the absence of protective factors. Bivariate analyses highlight the importance of considering the temporality of these factors; i.e., whether they are present before or after radicalization onset. The most salient risk factors identified were alignment with a group or movement with an exclusively violent strategic logic, and access to weapons. In terms of protective factors, parenting children during radicalization, self‐control, and participation in extremist groups with a strategic logic that was not exclusively focused on violent means were all associated with noninvolvement in terrorist violence.

          Policy implications

          Different patterns of risk and protective factors influence whether radicalization will, or will not, lead to involvement in terrorist violence. One‐size‐fits‐all radicalization‐prevention efforts may therefore be less effective than programs tailored to address a particular outcome. Even when terrorist violence is prevented, the targeted individual is likely to remain radicalized. Preventative efforts must carefully assess whether the measures used to avert terrorist violence in the short‐term risk contributing to a longer term societal threat. The efficacy of preventative efforts depends in part on when they are deployed, that is, before or after radicalization onset.

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          Most cited references155

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          The Measurement of Observer Agreement for Categorical Data

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            Applied Logistic Regression

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              Purposeful selection of variables in logistic regression

              Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.
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                Author and article information

                Contributors
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                Journal
                Criminology & Public Policy
                Criminology & Public Policy
                Wiley
                1538-6473
                1745-9133
                June 06 2023
                Affiliations
                [1 ] Institute of Security and Global Affairs Leiden University The Hague The Netherlands
                Article
                10.1111/1745-9133.12626
                8ed56933-a024-458c-9b0f-908f3e935b61
                © 2023

                http://creativecommons.org/licenses/by/4.0/

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