Salīdzināt metodes

	Latentu klašu analīze (LCA)	Strukturālā vienādojumu modelēšana (SEM)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1950	1970
Autors	Paul F. Lazarsfeld	Karl Jöreskog (LISREL framework, 1970s)
Tips	Latent variable / probabilistic clustering	Latent variable / causal modeling
Pirmavots	Hagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press. ISBN: 978-0521594516	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Citi nosaukumi	Gizil Sınıf Analizi (LCA), latent class model, latent structure analysis	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Saistītās	3	5
Kopsavilkums	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.	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.