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| Latente Klassenanalyse (LCA)× | Exploratorische Faktorenanalyse (EFA)× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Latent structure | Latent structure |
| Entstehungsjahr≠ | 1950s–1968 | — |
| Urheber≠ | Paul F. Lazarsfeld | — |
| Typ≠ | Latent variable / person-centered classification | Latent variable / dimension reduction |
| Wegweisende Quelle≠ | Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. 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≠ | LCA, latent class model, latent categorical analysis, finite mixture of multinomials | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data. | 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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