Comparer des méthodes

	Modèle DINA	Analyse des conditions nécessaires
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2001	2016
Auteur d'origine	Brian Junker, Klaas Sijtsma	Jan Dul
Type	Discrete latent class model	Set-theoretic configurational analysis
Source fondatrice	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.
Alias	DINA	NCA
Apparentées	4	5
Résumé	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.