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| Eksploracyjna analiza czynnikowa (EFA)× | Kwantitativna analiza czynnikowa (CFA)× | Analiza Głównych Składowych× | |
|---|---|---|---|
| Dziedzina≠ | Statystyka | Psychometria | Uczenie maszynowe |
| Rodzina≠ | Latent structure | Latent structure | Machine learning |
| Rok powstania≠ | — | 1969 | 2002 |
| Twórca≠ | — | Karl Gustav Jöreskog | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Typ≠ | Latent variable / dimension reduction | Hypothesis-testing latent variable model | Unsupervised dimensionality reduction |
| Źródło pierwotne≠ | 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 ↗ | Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Inne nazwy≠ | common factor analysis, açımlayıcı faktör analizi, factor analysis | CFA, confirmatory FA, measurement model, restricted factor analysis | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Pokrewne≠ | 4 | 4 | 3 |
| Podsumowanie≠ | 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. | 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. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
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