Methoden vergleichen

	Latent Class Analysis (LCA)	Clusteranalyse	Exploratorische Faktorenanalyse (EFA)
Fachgebiet	Statistik	Statistik	Statistik
Familie	Latent structure	Latent structure	Latent structure
Entstehungsjahr	1950	1939–1967	—
Urheber	Paul F. Lazarsfeld	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means	—
Typ	Latent variable / probabilistic clustering	Unsupervised classification / grouping	Latent variable / dimension reduction
Wegweisende Quelle	Hagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press. ISBN: 978-0521594516	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913	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.
Aliasnamen	Gizil Sınıf Analizi (LCA), latent class model, latent structure analysis	clustering, unsupervised classification, data clustering, numerical taxonomy	common factor analysis, açımlayıcı faktör analizi, factor analysis
Verwandt	3	5	4
Zusammenfassung	Latent class analysis is a probabilistic model-based clustering technique that identifies unobserved subgroups — latent classes — within a population on the basis of patterns of categorical, binary, or ordinal indicator responses. Originating in sociological measurement theory with Lazarsfeld's latent structure work around 1950 and formalised computationally by Goodman in the 1970s, it is widely used in the social, health, and behavioural sciences to reveal hidden population heterogeneity.	Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in 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.