Replication Data for: Explaining Recruitment to Extremism: A Bayesian Hierarchical Case-Control Approach

Who joins extremist movements? Answering this question is beset by method- ological challenges as survey techniques are infeasible and selective samples pro- vide no counterfactual. Recruits can be assigned to contextual units, but this is vulnerable to problems of ecological inference. In this article, we elaborate a tech- nique that combines survey and ecological approaches. The Bayesian hierarchical case-control design that we propose allows us to identify individual-level and contextual factors patterning the incidence of recruitment to extremism, while accounting for spatial autocorrelation, rare events, and contamination. We empir- ically validate our approach by matching a sample of Islamic State (ISIS) fighters from nine MENA countries with representative population surveys enumerated shortly before recruits joined the movement. High status individuals in their early twenties with university education were more likely to join ISIS. There is more mixed evidence for relative deprivation. The accompanying extremeR package provides functionality for applied researchers to implement our approach.

哪些人会加入极端主义运动？解答这一问题常面临方法论层面的诸多困境：调查技术难以实施，而选择性抽样无法提供反事实依据。若将招募对象分配至情境单位，则易陷入生态推断（ecological inference）的相关问题。本文提出一种结合调查法与生态推断法的研究技术：我们所提出的贝叶斯分层病例对照设计，能够帮助我们识别影响极端主义招募发生率的个体层面与情境层面因素，同时兼顾空间自相关性（spatial autocorrelation）、稀有事件（rare events）与混杂偏倚。我们通过将9个中东与北非（Middle East and North Africa，简称MENA）国家的伊斯兰国（Islamic State，ISIS）参战人员样本，与参战人员加入该组织前不久开展的代表性人口调查数据进行匹配，从实证层面验证了本研究方法的有效性。研究结果显示，年龄在二十岁出头、拥有大学学历的社会地位较高群体，加入ISIS的概率更高；而关于相对剥夺感（relative deprivation）的相关实证证据则较为混杂。配套的extremeR软件包可为应用研究者提供实现本研究方法的相关功能。