Сравнение на методи

	Анализ на латентните класове (LCA)	Експлораторният факторен анализ (EFA)	Моделиране на структурни уравнения (МСУ)
Област	Статистика	Статистика	Статистика
Семейство	Latent structure	Latent structure	Latent structure
Година на възникване	1950	—	1970
Създател	Paul F. Lazarsfeld	—	Karl Jöreskog (LISREL framework, 1970s)
Тип	Latent variable / probabilistic clustering	Latent variable / dimension reduction	Latent variable / causal modeling
Основополагащ източник	Hagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press. ISBN: 978-0521594516	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.	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Други названия	Gizil Sınıf Analizi (LCA), latent class model, latent structure analysis	common factor analysis, açımlayıcı faktör analizi, factor analysis	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Свързани	3	4	5
Резюме	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.	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.	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.