Comparer des méthodes

	SIBTEST	Tests adaptatifs informatisés diagnostiques cognitifs	Modèle DINA
Domaine	Psychométrie	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure	Latent structure
Année d'origine	1993	2007	2001
Auteur d'origine	Richard Shealy, William F. Stout	Xueli Xu, Jean-Paul Fox	Brian Junker, Klaas Sijtsma
Type	Differential item functioning (DIF) assessment	Skill-adaptive testing with psychometric diagnostic classification	Discrete latent class model
Source fondatrice	Shealy, R., & Stout, W. F. (1993). A model-based standardization approach that separates true bias/DIF from group differences and detects test bias/DTF. Psychometrika, 58(2), 159-194.	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.	Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25(3), 258-272.
Alias	—	CD-CAT	DINA
Apparentées	5	5	4
Résumé	SIBTEST (Simultaneous Item Bias Test) is a non-parametric method for detecting differential item functioning (DIF) and differential test functioning (DTF) developed by Shealy and Stout (1993). Unlike parametric approaches, SIBTEST does not assume a particular item response model and directly tests whether groups differ in their probability of correct responses at equal levels of overall ability.	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 DINA Model (Deterministic Inputs, Noisy Outputs) is a cognitive diagnostic model developed by Junker and Sijtsma (2001) that classifies examinees into latent skill classes based on their item response patterns. DINA assumes a deterministic relationship between skill mastery and correct responses, with probabilistic error accounting for guessing and slips.