Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse factorielle confirmatoire multiniveau (MCFA)× | Analyse factorielle exploratoire (AFE)× | |
|---|---|---|
| Domaine≠ | Psychométrie | Statistique |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 1994 | — |
| Auteur d'origine≠ | Bengt O. Muthen | — |
| Type≠ | Latent variable model / measurement model | Latent variable / dimension reduction |
| Source fondatrice≠ | Muthen, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. DOI ↗ | 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≠ | MCFA, multilevel measurement model, two-level CFA, hierarchical CFA | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Apparentées≠ | 6 | 4 |
| Résumé≠ | Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the standard tool for validating psychometric scales administered within natural groups such as classrooms, clinics, or organisations. | 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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