Porovnať metódy

	Kognitívne diagnostické adaptívne testovanie pomocou počítača	Model DINA	Fuzzy-Set Qualitative Comparative Analysis
Odbor	Psychometria	Psychometria	Psychometria
Rodina	Latent structure	Latent structure	Latent structure
Rok vzniku	2007	2001	2000
Tvorca	Xueli Xu, Jean-Paul Fox	Brian Junker, Klaas Sijtsma	Charles Ragin
Typ	Skill-adaptive testing with psychometric diagnostic classification	Discrete latent class model	Set-theoretic configurational method
Pôvodný zdroj	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.	Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press.
Ďalšie názvy	CD-CAT	DINA	fsQCA, FSQCA
Príbuzné	5	4	4
Zhrnutie	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.	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.