Comparer des méthodes

	Théorie de la généralisabilité appliquée aux tests adaptatifs informatisés	Théorie de la généralisabilité (Théorie G)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1972 (G-theory); CAT application 1990s–2000s	1963–1972
Auteur d'origine	Lee J. Cronbach (G-theory); applied to CAT by Brennan and others	Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam
Type	Reliability / generalizability analysis	Variance-components reliability model
Source fondatrice	Brennan, R. L. (2001). Generalizability Theory. Springer. ISBN: 978-0387952826	Cronbach, L. J., Gleser, G. C., Nanda, H. & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. Wiley.
Alias	CAT G-theory, adaptive test generalizability, G-theory in CAT, computerized adaptive generalizability analysis	G-theory, G-study / D-study framework, variance components reliability
Apparentées	6	4
Résumé	Generalizability theory (G-theory) applied to computerized adaptive testing (CAT) evaluates the dependability of adaptive test scores by decomposing score variance across measurement facets such as persons, items, and occasions. Unlike classical test theory, G-theory quantifies multiple simultaneous sources of measurement error, offering a richer reliability picture for adaptively administered assessments.	Generalizability Theory is a psychometric framework that decomposes observed score variance into multiple sources — persons, items, raters, occasions, and their interactions — using analysis of variance. It replaces the single reliability coefficient of classical test theory with a family of coefficients that tell researchers how well scores generalize across different measurement conditions.