Studying Radical Attitudes via Intensive Repeated Measure Techniques

Project ID: 2228cd1321 (You will need this ID for your application)

Under Offer

Research Theme: Information and Communication Technologies

UCL Lead department: Security and Crime Science

Department Website

Lead Supervisor: Paul Gill

Project Summary:

Existing research on violent extremism risk factors is largely focused upon cross-sectional prevalence rates of certain risk factors across different cohorts. Because such designs lack a sense of temporality, we know very little about the ‘functional relevance’ of these factor to the genesis of violent extremism. Unpacking this requires more methodologically sophisticated research designs. Current work within the project team involves a longitudinal 3-wave study of 2000+ participants measured on 190 survey items over a 24-month period. This proposed project instead utilises a much smaller sample, with approximately 20 items, measured intensively (e.g. 30 times) over a small-temporal period (e.g. a month). This affords for quantitative ideographic research designs that intensely monitor intra-individual change over a short period of time. As part of the project, the app to deploy such intensive longitudinal data would be developed. The data would be analysed via dynamic SEM models to individual participants. These techniques allow researchers to specify whether group-level factor models can be generalised to individual cases or not, and pinpoint trait- and state-based risk factors by demonstrating changes in contingencies over time. Additionally, the project utilises group iterative multiple model estimation (GIMME). This approach combines ideographic and nomothetic approaches by producing person-specific maps (for the former) that contain a group-level structure (for the latter). GIMME helps identify those risk factors which have a common generalisable impact, and those which are case-specific and maps variables as a directed network structure to show how one variable linearly (or contemporaneously) affects another within a person-specific network over a short period of time (e.g. how stress immediately impacts emotions and how this then affects attitudinal dispositions). The analyses help us measure very short-term shifts in violent extremist beliefs, attitudes, and intentions and their lagged relationship with daily experiences. Such analyses are highly valuable to real-world interventions.