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| Recherche hiérarchique confirmatoire× | Analyse Factorielle Confirmatoire (AFC)× | |
|---|---|---|
| Domaine≠ | Conception de la recherche | Psychométrie |
| Famille≠ | Process / pipeline | Latent structure |
| Année d'origine≠ | 1980s–2000s | 1969 |
| Auteur d'origine≠ | Raudenbush & Bryk; Hox; Goldstein | Karl Gustav Jöreskog |
| Type≠ | Quantitative confirmatory research design | Hypothesis-testing latent variable model |
| Source fondatrice≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Alias | multilevel confirmatory research, nested confirmatory design, hierarchical hypothesis-testing research, HCR | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Hierarchical confirmatory research is a quantitative design that tests pre-specified hypotheses about relationships or group differences in data that have a natural nested (hierarchical) structure — such as students clustered within classrooms, patients within hospitals, or employees within organizations. By explicitly modeling the hierarchy, it avoids the inflation of Type I error that occurs when nested data are analyzed as though observations were independent. | 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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