Clemmow, Caitlin; (2020) Risk factors and indicators for engagement in violent extremism. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Research on terrorism is increasingly empirical and a number of significant advancements have been made. One such evolution is the emergent understanding of risk factors and indicators for engagement in violent extremism. Beyond contributing to academic knowledge, this has important real-world implications. Notably, the development of terrorism risk assessment tools, as well as behavioural threat assessment in counterterrorism. This thesis makes a unique contribution to the literature in two key ways. First, there is a general consensus that no single, stable profile of a terrorist exists. Relying on profiles of static risk factors to inform judgements of risk and/or threat may therefore be problematic, particularly given the observed multi- and equi-finality. One way forward may be to identify configurations of risk factors and tie these to the theorised causal mechanisms they speak to. Second, there has been little attempt to measure the prevalence of potential risk factors for violent extremism in a general population, i.e. base rates. Establishing general population base rates will help develop more scientifically rigorous putative risk factors, increase transparency in the provision of evidence, minimise potential bias in decision-making, improve risk communication, and allow for risk assessments based on Bayesian principles. This thesis consists of four empirical chapters. First, I inductively disaggregate dynamic person-exposure patterns (PEPs) of risk factors in 125 cases of lone-actor terrorism. Further analysis articulates four configurations of individual-level susceptibilities which interact differentially with situational, and exposure factors. The PEP typology ties patterns of risk factors to theorised causal mechanisms specified by a previously designed Risk Analysis Framework (RAF). This may be more stable grounds for risk assessment however than relying on the presence or absence of single factors. However, with no knowledge of base rates, the relevance of seemingly pertinent risk factors remains unclear. However, how to develop base rates is of equal concern. Hence, second, I develop the Base Rate Survey and compare two survey questioning designs, direct questioning and the Unmatched Count Technique (UCT). Under the conditions described, direct questioning yields the most appropriate estimates. Third, I compare the base rates generated via direct questioning to those observed across a sample of lone-actor terrorists. Lone-actor terrorists demonstrated more propensity, situational, and exposure risk factors, suggesting these offenders may differ from the general population in measurable ways. Finally, moving beyond examining the prevalence rates of single factors, I collect a second sample in order to model the relations among these risk factors as a complex, dynamic system. To do so, the Base Rate Survey: UK is distributed to a representative sample of 1,500 participants from the UK. I introduce psychometric network modelling to terrorism studies which visualises the interactions among risk factors as a complex system via network graphs.

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