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| Bayesianische Messinvarianzprüfung× | Exploratorische Faktorenanalyse (EFA)× | |
|---|---|---|
| Fachgebiet≠ | Psychometrie | Statistik |
| Familie | Latent structure | Latent structure |
| Entstehungsjahr≠ | 2013 | — |
| Urheber≠ | Bengt Muthen, Tihomir Asparouhov, Rens Van de Schoot | — |
| Typ≠ | Bayesian multigroup latent variable test | Latent variable / dimension reduction |
| Wegweisende Quelle≠ | Van de Schoot, R., Kluytmans, A., Tummers, L., Lugtig, P., Hox, J., & Muthen, B. (2013). Facing off with Scylla and Charybdis: a comparison of scalar, partial, and the novel possibility of approximate measurement invariance. Frontiers in Psychology, 4, 770. 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 ↗ |
| Aliasnamen≠ | Bayesian MI, approximate measurement invariance, Bayesian multigroup CFA invariance, BSEM measurement invariance | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | Bayesian measurement invariance testing evaluates whether a scale's factor loadings and item intercepts are equivalent across groups, using a Bayesian framework that allows parameters to deviate from strict equality by a small, probabilistically specified amount rather than imposing an exact constraint. | 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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