Reliability and Validity of Risk Assessment Tools for Violent Extremism: A Systematic Review
Notice bibliographique
Résumé
Assessment of the risk of engaging in a violent radicalization/extremism trajectory has evolved quickly in the last 10 years. Guided by what has been achieved in psychology and criminology, scholars from the field of preventing violent extremism (PVE) have tried to import key lessons from violence risk assessment and management, while bearing in mind the idiosyncrasies of their particular field. However, risk tools that have been developed in the PVE space are relatively recent, and questions remain as to their level of psychometric validation. Namely, do these tools consistently and accurately assess risk of violent extremist acting out? To answer this question, we systematically reviewed evidence on the reliability and validity of violent extremism risk tools. The main objective of this review was to gather, critically appraise, and synthesize evidence regarding the appropriateness and utility of such tools, as validated with specific populations and contexts. Searches covered studies published up to December 31, 2021. They were performed in English and German across 17 databases, 45 repositories, Google, other literature reviews on violent extremism risk assessment, and references of included studies. Studies in all languages were eligible for inclusion in the review. We included studies with primary data resulting from the quantitative examination of the reliability and validity of tools used to assess the risk of violent extremism. Only tools usable by practitioners and intended to assess an individual's risk were eligible. We did not impose any restrictions on study design, type, method, or population. We followed standard methodological procedures outlined by the Campbell Collaboration for data extraction and analysis. Risk of bias was assessed using a modified version of the COSMIN checklist, and data were synthesized through meta-analysis when possible. Otherwise, narrative synthesis was used to aggregate the results. Among the 10,859 records found, 19 manuscripts comprising 20 eligible studies were included in the review. These studies focused on the Terrorist Radicalization Assessment Protocol (TRAP-18), the Extremism Risk Guidance Factors (ERG22+), the Multi-Level Guidelines (MLG-V2), the Identifying Vulnerable People guidance (IVP guidance), and the Violent Extremism Risk Assessment (VERA)—all structured professional judgment tools—as well as Der Screener—Islamismus , an actuarial scale. Studies mostly involved adult male participants susceptible to violent extremism ( N = 1106; M = 58.21; SD = 55.14). The types of extremist ideologies endorsed by participants varied, and the same was true for ethnicity and country/continent of provenance. Encouraging results were found concerning the inter-rater agreement of scales in research contexts (kappas between 0.76 and 0.93), but one of the two studies that examined it in a field setting obtained disappointing results (kappas ranging between of 0.47 and 0.80). Content validity studies indicated that PVE risk tools adequately cover the risk factors and offending processes of individuals who go on to commit extremist violence. Construct validity analyses were few and far between, with results indicating that empirical divisions of scales did not match their conceptual divisions. The internal consistency of subscales was lackluster (Cronbach's alphas between 0.19 and 0.85), whereas full scales demonstrated acceptable internal consistency when assessed (0.80 for the ERG22+ and 0.64 for the IVP guidance). Only one study examined convergent validity, and it revealed a lack of convergence, primarily due to particularities of the scale under study (the MLG-V2). Discriminant validity analyses were exploratory in nature, but suggested that PVE risk tools might not be ideology-specific and may apply to both group and lone actors. Finally, although the TRAP-18 showed a relatively strong postdictive effect size (pooled r = 0.62 [0.35–0.77], p = 0.000), the results were highly heterogeneous ( I 2 = 86%), and all studies used retrospective designs, meaning the outcome was already known at the time of assessment. As such, no included study evaluated true predictive validity (i.e., the ability to forecast future violent extremist outcomes based on prospective risk assessment). This represents a significant evidence gap. Threats to validity were substantial: (a) Many studies were case studies or had very small samples, (b) nearly all samples were constituted through the triangulation of publicly available data, and (c) convenience outcome measures were often used. Although having imperfect data is better than having no data, the current state of empirical validation precludes the recommendation of one tool over another for specific populations and contexts, and calls for higher-quality validation studies for PVE risk assessment tools. Nevertheless, these tools constitute useful checklists of relevant risk and protective factors that could be taken into account by evaluators who wish to assess the risk of violent extremism and identify intervention targets.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,050 | 0,269 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,010 | 0,010 |
| Bibliométrie | 0,028 | 0,020 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».