পদ্ধতির তুলনা করুন

	DINA মডেল	ডিনো মডেল	নেসেসারি কন্ডিশন অ্যানালাইসিস (Necessary Condition Analysis)
ক্ষেত্র	মনোমিতি	মনোমিতি	মনোমিতি
পরিবার	Latent structure	Latent structure	Latent structure
উদ্ভবের বছর	2001	2006	2016
প্রবর্তক	Brian Junker, Klaas Sijtsma	James Templin, Russell Henson	Jan Dul
ধরন	Discrete latent class model	Disjunctive latent class model	Set-theoretic configurational analysis
মৌলিক উৎস	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.	Templin, J., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11(3), 287-305.	Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of "necessary but not sufficient" causality. Organizational Research Methods, 19(1), 10-52.
অপর নাম	DINA	DINO	NCA
সম্পর্কিত	4	4	5
সারসংক্ষেপ	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.	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.	Necessary Condition Analysis (NCA) is a set-theoretic method developed by Dul (2016) that identifies conditions necessary (but not necessarily sufficient) for an outcome to occur. Unlike regression, which estimates average effects, NCA identifies absolute thresholds: conditions that must be present at a certain level for the outcome to be possible, regardless of other factors.