Comparer des méthodes

	Modèle de Mélange de Croissance (GMM)	Analyse de classes latentes (ACL)
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1999	1950
Auteur d'origine	Bengt O. Muthén & Kerby Shedden	Paul F. Lazarsfeld
Type	Latent class / longitudinal growth model	Latent variable / probabilistic clustering
Source fondatrice	Muthén, B. O. & Shedden, K. (1999). Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55(2), 463–469.	Hagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press. ISBN: 978-0521594516
Alias	Büyüme Karışım Modeli (Growth Mixture Model — GMM), GMM, latent class growth analysis extension, mixture latent growth curve model	Gizil Sınıf Analizi (LCA), latent class model, latent structure analysis
Apparentées	5	3
Résumé	The 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.	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.