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	Model DINA	Analýza nevyhnutných podmienok
Odbor	Psychometria	Psychometria
Rodina	Latent structure	Latent structure
Rok vzniku	2001	2016
Tvorca	Brian Junker, Klaas Sijtsma	Jan Dul
Typ	Discrete latent class model	Set-theoretic configurational analysis
Pôvodný zdroj	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.	Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of "necessary but not sufficient" causality. Organizational Research Methods, 19(1), 10-52.
Ďalšie názvy	DINA	NCA
Príbuzné	4	5
Zhrnutie	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.	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.