Comparer des méthodes

	Théorie de la généralisabilité multi-groupes	Théorie de la généralisabilité (Théorie G)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1963–2001	1963–1972
Auteur d'origine	Lee J. Cronbach and colleagues (Cronbach, Gleser, Nanda, Rajaratnam), extended to multi-group contexts by Brennan and others	Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam
Type	Variance component / reliability generalization	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	MG G-theory, multi-group G-theory, generalizability theory across groups, cross-group G-study	G-theory, G-study / D-study framework, variance components reliability
Apparentées	6	4
Résumé	Multi-group generalizability theory (MG G-theory) extends classical generalizability theory to estimate and compare variance components — attributable to persons, items, raters, occasions, and their interactions — simultaneously across two or more defined groups. It reveals whether a measurement procedure is equally reliable and generalizable for every group studied, supporting fair and equitable score interpretation.	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.