เปรียบเทียบวิธี

	ระเบียบวิธีปริภูมิแห่งกฎ	โมเดล DINA	Fuzzy-Set Qualitative Comparative Analysis
สาขาวิชา	การวัดทางจิตวิทยา	การวัดทางจิตวิทยา	การวัดทางจิตวิทยา
ตระกูล	Latent structure	Latent structure	Latent structure
ปีกำเนิด	1983	2001	2000
ผู้ริเริ่ม	Kikumi K. Tatsuoka	Brian Junker, Klaas Sijtsma	Charles Ragin
ประเภท	IRT-based diagnostic classification	Discrete latent class model	Set-theoretic configurational method
แหล่งต้นตำรับ	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 Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign.	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.
ชื่อเรียกอื่น	RSM	DINA	fsQCA, FSQCA
ที่เกี่ยวข้อง	5	4	4
สรุป	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 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.