Usporedite metode

	Analiza latentnih klasa (LCA)	Diskriminacijska analiza
Područje	Statistika	Statistika
Obitelj	Latent structure	Latent structure
Godina nastanka	1950s–1968	1936
Tvorac	Paul F. Lazarsfeld	Ronald A. Fisher
Vrsta	Latent variable / person-centered classification	Supervised classification and dimension reduction
Temeljni izvor	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Drugi nazivi	LCA, latent class model, latent categorical analysis, finite mixture of multinomials	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
Srodne	6	4
Sažetak	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.	Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error.