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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Invariance de mesure multiniveaux× | Analyse Factorielle Confirmatoire (AFC)× | |
|---|---|---|
| Domaine | Psychométrie | Psychométrie |
| Famille | Latent structure | Latent structure |
| Année d'origine≠ | 2000s | 1969 |
| Auteur d'origine≠ | Muthén, Asparouhov, and colleagues | Karl Gustav Jöreskog |
| Type≠ | Measurement model evaluation | Hypothesis-testing latent variable model |
| Source fondatrice≠ | Muthén, B. O., & Asparouhov, T. (2009). Multilevel factor analysis of class and student achievement components. Journal of Educational and Behavioral Statistics, 34(2), 250–270. link ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Alias | MLMI, multilevel factorial invariance, cross-level measurement invariance, multilevel CFA invariance | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Apparentées≠ | 3 | 4 |
| Résumé≠ | Multilevel measurement invariance testing evaluates whether a latent construct is measured equivalently both within clusters (e.g., individuals within teams) and between clusters (e.g., team-level aggregates). It extends standard measurement invariance procedures to nested data structures commonly encountered in organisational, educational, and cross-cultural research. | 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. |
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