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| Eksploracyjna analiza czynnikowa dla danych politomicznych× | Eksploracyjna analiza czynnikowa (EFA)× | |
|---|---|---|
| Dziedzina≠ | Psychometria | Statystyka |
| Rodzina | Latent structure | Latent structure |
| Rok powstania≠ | 1978 | — |
| Twórca≠ | Bengt Muthén | — |
| Typ | Latent variable / dimension reduction | 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 ↗ | 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 ↗ |
| Inne nazwy≠ | EFA for ordered-categorical data, polychoric EFA, ordinal exploratory factor analysis, polytomous factor analysis | common factor analysis, açımlayıcı faktör analizi, 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. | 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. |
| ScholarGateZbiór danych ↗ |
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