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Латентно-классовый анализ (LCA)×Моделирование структурными уравнениями (SEM)×
ОбластьСтатистикаСтатистика
СемействоLatent structureLatent structure
Год появления19501970
Автор методаPaul F. LazarsfeldKarl Jöreskog (LISREL framework, 1970s)
ТипLatent variable / probabilistic clusteringLatent variable / causal modeling
Основополагающий источникHagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press. ISBN: 978-0521594516Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Другие названияGizil Sınıf Analizi (LCA), latent class model, latent structure analysisYapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Связанные35
СводкаLatent 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.
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ScholarGateСравнение методов: LCA · SEM. Получено 2026-06-15 из https://scholargate.app/ru/compare