Porovnat metody

	Metodologie stavového prostoru pravidel	Kognitivně diagnostické adaptivní testování	Fuzzy-Set Qualitative Comparative Analysis
Obor	Psychometrika	Psychometrika	Psychometrika
Rodina	Latent structure	Latent structure	Latent structure
Rok vzniku	1983	2007	2000
Tvůrce	Kikumi K. Tatsuoka	Xueli Xu, Jean-Paul Fox	Charles Ragin
Typ	IRT-based diagnostic classification	Skill-adaptive testing with psychometric diagnostic classification	Set-theoretic configurational method
Původní zdroj	Hartz, S. M. (2002). A Bayesian framework for the unified treatment of assessing dimensionality, assessing local dependence, and estimating ability for unidimensional and multidimensional item response data. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign.	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.	Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press.
Další názvy	RSM	CD-CAT	fsQCA, FSQCA
Příbuzné	5	5	4
Shrnutí	Rule Space Methodology (RSM) is a diagnostic classification approach developed by Tatsuoka (1983) that uses Item Response Theory and geometric methods to classify examinees into knowledge states based on their response patterns. Unlike classical scoring, RSM identifies which specific skills or competencies an examinee possesses or lacks, enabling targeted educational interventions.	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.	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.