Comparer des méthodes

	Tests adaptatifs informatisés diagnostiques cognitifs	Modèle DINO	Analyse Comparative Qualitative par Ensembles Flous
Domaine	Psychométrie	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure	Latent structure
Année d'origine	2007	2006	2000
Auteur d'origine	Xueli Xu, Jean-Paul Fox	James Templin, Russell Henson	Charles Ragin
Type	Skill-adaptive testing with psychometric diagnostic classification	Disjunctive latent class model	Set-theoretic configurational method
Source fondatrice	Choi, K. M., Lee, Y. S., & Park, Y. S. (2015). What CDM can tell about examinees' strengths and weaknesses: Cognitive diagnostic information in TIMSS. Journal of Educational Evaluation for Policy Analysis, 24(1), 79-100.	Templin, J., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11(3), 287-305.	Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press.
Alias	CD-CAT	DINO	fsQCA, FSQCA
Apparentées	5	4	4
Résumé	Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) combines computerized adaptive testing (CAT) with cognitive diagnostic models (CDMs) to efficiently assess students' specific skill profiles. Rather than producing a single overall ability score, CD-CAT adaptively selects items to quickly identify which skills a student has mastered and which need development.	The DINO Model (Deterministic Inputs, Noisy Outputs—Disjunctive) is a cognitive diagnostic model that relaxes DINA's conjunctive (AND) skill requirement logic. DINO assumes an examinee only needs to master one of multiple possible skill pathways to answer an item correctly, making it suitable for scenarios where skills are substitutable or alternative routes to success exist.	Fuzzy-Set Qualitative Comparative Analysis (fsQCA) is a set-theoretic method developed by Charles Ragin in the early 2000s that combines the configurational logic of qualitative case studies with the mathematical rigor of fuzzy sets. It bridges qualitative and quantitative research by allowing researchers to examine causal complexity through combinations of conditions (configurations) rather than isolated variables.