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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Théorie de la généralisabilité longitudinale× | Analyse factorielle exploratoire (AFE)× | |
|---|---|---|
| Domaine≠ | Psychométrie | Statistique |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 1990s–2000s | — |
| Auteur d'origine≠ | Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations | — |
| Type≠ | Variance components / reliability estimation | Latent variable / dimension reduction |
| 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. link ↗ | Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗ |
| Alias≠ | longitudinal G-theory, longitudinal GT, repeated-measures generalizability theory, G-theory for longitudinal designs | common factor analysis, açımlayıcı faktör analizi, 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. | Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance. |
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