Methoden vergleichen

	Robuste Latent-Profil-Analyse	Latente Klassenanalyse (LCA)
Fachgebiet	Statistik	Statistik
Familie	Latent structure	Latent structure
Entstehungsjahr	2010s	1950s–1968
Urheber	Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s)	Paul F. Lazarsfeld
Typ	Person-centered mixture model with robust estimation	Latent variable / person-centered classification
Wegweisende Quelle	Vermunt, J. K. & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied Latent Class Analysis (pp. 89–106). Cambridge University Press. ISBN: 978-0521594035	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Aliasnamen	RLPA, robust LPA, robust mixture model for continuous indicators, outlier-robust latent profile analysis	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Verwandt	5	6
Zusammenfassung	Robust latent profile analysis identifies latent subgroups of individuals based on their continuous multivariate indicators while protecting parameter estimates from distortion by outliers or atypical observations. It extends standard latent profile analysis by replacing the Gaussian component densities with heavier-tailed or contaminated-normal alternatives that down-weight extreme cases during estimation.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.