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Růstový směsný model (GMM)×Modelování pomocí strukturálních rovnic (SEM)×
OborStatistikaStatistika
RodinaLatent structureLatent structure
Rok vzniku19991970
TvůrceBengt O. Muthén & Kerby SheddenKarl Jöreskog (LISREL framework, 1970s)
TypLatent class / longitudinal growth modelLatent variable / causal modeling
Původní zdrojMuthén, B. O. & Shedden, K. (1999). Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55(2), 463–469. DOI ↗Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Další názvyBüyüme Karışım Modeli (Growth Mixture Model — GMM), GMM, latent class growth analysis extension, mixture latent growth curve modelYapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Příbuzné55
ShrnutíThe Growth Mixture Model, introduced by Muthén and Shedden in 1999, is a longitudinal latent variable method that identifies distinct subpopulations — latent trajectory classes — each following its own growth curve over time. It extends the standard Latent Growth Curve (LGC) model by allowing the sample to be composed of an unknown mixture of classes with different intercepts, slopes, and variance structures.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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ScholarGatePorovnat metody: GMM · SEM. Získáno 2026-06-17 z https://scholargate.app/cs/compare