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A Field-Wide Systematic Review and Meta-analysis of Putative Risk and Protective Factors for Radicalization Outcomes

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Abstract

Objectives

This systematic review sought to collate and synthesize the risk and protective factors for different outcomes of radicalization. We aimed to firstly quantify the effects of all factors for which rigorous empirical data exists, and secondly, to differentiate between factors related to radical attitudes, intention, and behaviors. The goal was to develop a rank-order of factors based on their pooled estimates in order to gain a better understanding of which factors may be most important, and the differential effects on the different outcomes.

Methods

Random effects meta-analysis pooled primarily bivariate effect sizes to calculate pooled estimates for each factor. Meta-regression was used to examine the effects of a range of study-level characteristics, including the effects of using partial effects sizes as supplementary effect sizes where bivariate estimates were unavailable. Subgroup analysis was used to further analyze the extent to which the combining of effect sizes from different sources contributed to heterogeneity and estimate inflation. Leave-one-out sensitivity analysis was used to identify cases where a single study was a significant source of heterogeneity.

Results

Extensive searches in English, German and Dutch resulted in the screening of more than 10,000 items, and a final inclusion of 57 publications published between 2007 and 2018 from which 62 individual level factors were identified across three radicalization outcomes: attitudes, intentions, and actions. Effect sizes ranged from z − 0.621 to 0.572. The smallest estimates were found for sociodemographic factors, while the largest effect sizes were found for traditional criminogenic and criminotrophic factors such as low self-control, thrill-seeking, and attitudinal factors, with radical attitudes having the largest effect on radical intentions and behaviors.

Conclusions

The most commonly researched factors, sociodemographic factors, have exceptionally small effects, even when effect sizes are derived from bivariate relationships. The finding regarding the effects of radical attitudes on intentions and actions provide empirical support for existing theoretical frameworks. The consistency among the clustering of familiar criminogenic factors within the rank-order could have implications for the development of a more evidence based approach to risk assessment and counter violent extremism policies.

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Notes

  1. Putative factors are those factors for which there is evidence to suggest that they play a role, through correlational data operating in the theorized direction, but for which the evidence does not meet the established criteria for classifying them as risk or causal risk factors (Kraemer et al. 1997, 2001). A more extensive description can be found on pages 6–7.

  2. By including confirmed/convicted terrorists as a dependent variable, we make no distinction between violent and non-violent terrorists. However, as some studies compare non-violent and violent terrorist offenders, in such cases, our focus is on the violent offenders. To the best of our knowledge there are no studies comparing non-violent terrorist offenders to samples of non-terrorists.

  3. Samples that included criminals (e.g. Decker and Pyrooz 2019) were included for radical attitudes or intentions since these samples provide variation on the dependent variable. Samples of criminals as a comparison group for terrorists were excluded however since we believe that identifying factors that differentiate terrorist from ordinary criminal offenders represents a different topic of inquiry.

  4. It can also be noted that in recent years Turkey has had an eroding financial, social, and political situation. According to the Sustainable Governance Indicators report for 2018 it has lowest quality of democracy ranking of all OECD and EU countries (Genckaya et al. 2018).

  5. Such as the calculation of correlation coefficients using either bivariate approaches (e.g. Pearson's r), or multivariate regression models, such as linear regression, logistic regression etc.

  6. While most non-English studies are indexed in English, supplementary searches were also conducted in German and Dutch. General searches were also conducted on the Google scholar and search engine to try and identify grey literature.

  7. For example, we contacted renowned experts from the START center at the University of Maryland, as well as the NSCR at the Vrije University in the Netherlands.

  8. We would like to thank the anonymous reviewer for directing us to two additional studies that met the inclusion criteria and which were not included in our original search results.

  9. We acknowledge that some studies using OLS methods may actually have a discrete, and not truly continuous variable. Nevertheless, whilst these studies' model choice may not have been the most appropriate, we nevertheless adhere to this approach.

  10. While this latter approach is imperfect, it is preferable to discarding studies based on the source of effect size (Borenstein et al. 2011; Pratt et al. 2014; Pyrooz et al. 2016).

  11. Which includes a χ2 significance test.

  12. The minimum number of studies for indicating the presence of publication bias was the number of included studies, n * 5 + 10 (Rosenthal 1979). The Fail-Safe N method has been previously used in systematic reviews where multiple factors and outcomes are explored and for which a relatively small number of studies exist for each factor (e.g. Assink 2017).

  13. The majority of these studies were excluded based on the nature of their dependent variable or the sample. For example, a study by Tol and Akbaba (2016) examined risk factors of membership in Milli Görüş, a legal and non-violent group. Other studies, such as those on foreign fighters, lacked a comparison group (e.g. Verwimp 2016; Bakker and de Bont 2016). We also excluded two additional studies based on the RADIMER dataset for their inability to provide unique effect sizes (Schils and Pauwels 2016; Pauwels and Svensson 2017). We note that Lieven Pauwels, was kind enough to calculate and share bivariate correlations with us for a series of studies that were included. While each of the studies is based on the same dataset, they use different subsamples which vary greatly in size and makeup, and report on different factors (Pauwels and de Waele 2014; Pauwels and Schils 2016).

  14. We would like to thank one of the anonymous reviewers who provided us with direction towards two of these studies and who we include here when we refer to experts contacted.

  15. For radical attitudes only one study included 'Anomie' (Pauwels and De Waele 2014). For radical behaviors there were single effect sizes for 'religious convert' (Krueger 2008), and for 'second generation immigrant' (Weerman et al. 2018).

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 699824. We would like to thank David B. Wilson, Hannah Rothstein, Michael Borenstein, as well as the anonymous reviewers for their helpful comments and advice in preparing the manuscript for publication.

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  1. Institute of Criminology, Faculty of Law, Hebrew University of Jerusalem, Mount Scopus, Jerusalem, Israel

    Michael Wolfowicz, Yael Litmanovitz, David Weisburd & Badi Hasisi

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  1. Michael Wolfowicz
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Appendix

Appendix

See Table 8.

Table 8 Examples of measures of constructs of identified factors
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Wolfowicz, M., Litmanovitz, Y., Weisburd, D. et al. A Field-Wide Systematic Review and Meta-analysis of Putative Risk and Protective Factors for Radicalization Outcomes. J Quant Criminol 36, 407–447 (2020). https://doi.org/10.1007/s10940-019-09439-4

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