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| Eksploracyjna analiza czynnikowa dla danych politomicznych× | Kwantitativna analiza czynnikowa (CFA)× | |
|---|---|---|
| Dziedzina | Psychometria | Psychometria |
| Rodzina | Latent structure | Latent structure |
| Rok powstania≠ | 1978 | 1969 |
| Twórca≠ | Bengt Muthén | Karl Gustav Jöreskog |
| Typ≠ | Latent variable / dimension reduction | Hypothesis-testing latent variable model |
| Źródło pierwotne≠ | Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491. DOI ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ |
| Inne nazwy | EFA for ordered-categorical data, polychoric EFA, ordinal exploratory factor analysis, polytomous factor analysis | CFA, confirmatory FA, measurement model, restricted factor analysis |
| Pokrewne | 4 | 4 |
| Podsumowanie≠ | Polytomous exploratory factor analysis extends standard EFA to ordered categorical (Likert-type) response data by replacing the Pearson correlation matrix with a polychoric correlation matrix. It recovers the latent continuous variable that each polytomous item is assumed to reflect, yielding more accurate factor loadings and better-defined factor structures than treating ordinal scores as if they were continuous. | 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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