Comparer des méthodes

	Théorie de la généralisabilité longitudinale	Analyse Factorielle Confirmatoire (AFC)
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1990s–2000s	1969
Auteur d'origine	Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations	Karl Gustav Jöreskog
Type	Variance components / reliability estimation	Hypothesis-testing latent variable model
Source fondatrice	Webb, N. M., Shavelson, R. J., & Harrigan, E. H. (2007). Generalizability theory: Overview. In C. R. Rao & S. Sinharay (Eds.), Handbook of Statistics, Vol. 26: Psychometrics (pp. 1–43). Elsevier.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Alias	longitudinal G-theory, longitudinal GT, repeated-measures generalizability theory, G-theory for longitudinal designs	CFA, confirmatory FA, measurement model, restricted factor analysis
Apparentées	4	4
Résumé	Longitudinal generalizability theory extends classical G-theory to repeated-measures and longitudinal designs, decomposing score variance across persons, measurement occasions, raters, and items simultaneously. It quantifies how reliably scores can be generalized across time points, evaluators, and conditions — information that is invisible to cross-sectional reliability indices.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.