Сравнение методов

	Методология пространства правил	Модель DINO	Fuzzy-Set Qualitative Comparative Analysis
Область	Психометрия	Психометрия	Психометрия
Семейство	Latent structure	Latent structure	Latent structure
Год появления	1983	2006	2000
Автор метода	Kikumi K. Tatsuoka	James Templin, Russell Henson	Charles Ragin
Тип	IRT-based diagnostic classification	Disjunctive latent class model	Set-theoretic configurational method
Основополагающий источник	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 Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign.	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.
Другие названия	RSM	DINO	fsQCA, FSQCA
Связанные	5	4	4
Сводка	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.	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.