Porównaj metody

	Analiza klas ukrytych (LCA)	Analiza skupień	Modelowanie równań strukturalnych (SEM)
Dziedzina	Statystyka	Statystyka	Statystyka
Rodzina	Latent structure	Latent structure	Latent structure
Rok powstania	1950	1939–1967	1970
Twórca	Paul F. Lazarsfeld	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means	Karl Jöreskog (LISREL framework, 1970s)
Typ	Latent variable / probabilistic clustering	Unsupervised classification / grouping	Latent variable / causal modeling
Źródło pierwotne	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	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Inne nazwy	Gizil Sınıf Analizi (LCA), latent class model, latent structure analysis	clustering, unsupervised classification, data clustering, numerical taxonomy	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Pokrewne	3	5	5
Podsumowanie	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.	Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.