Salīdzināt metodes

	Latent Class Analysis (LCA)	Eksploratīvā faktoru analīze (EFA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1950s–1968	—
Autors	Paul F. Lazarsfeld	—
Tips	Latent variable / person-centered classification	Latent variable / dimension reduction
Pirmavots	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.	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.
Citi nosaukumi	LCA, latent class model, latent categorical analysis, finite mixture of multinomials	common factor analysis, açımlayıcı faktör analizi, factor analysis
Saistītās	6	4
Kopsavilkums	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.