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| Potwierdzająca Analiza Czynnikowa dla Danych Politomicznych× | Eksploracyjna analiza czynnikowa dla danych politomicznych× | |
|---|---|---|
| Dziedzina | Psychometria | Psychometria |
| Rodzina | Latent structure | Latent structure |
| Rok powstania≠ | 1984 | 1978 |
| Twórca≠ | Bengt Muthen | Bengt Muthén |
| Typ≠ | Latent variable / confirmatory measurement model | Latent variable / dimension reduction |
| 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 ↗ | 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 ↗ |
| Inne nazwy | CFA for ordered categories, ordinal CFA, categorical CFA, WLSMV-CFA | EFA for ordered-categorical data, polychoric EFA, ordinal exploratory factor analysis, polytomous factor analysis |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | Polytomous confirmatory factor analysis (CFA) tests a pre-specified factor structure when items have three or more ordered response categories (e.g., Likert scales). By working with polychoric correlations and robust estimators such as WLSMV, it avoids the distortions that arise when ordered categorical data are treated as continuous. | 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. |
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