Порівняння методів

	Робастний аналіз латентних класів	Надійна експлораторна факторна аналіза
Галузь	Статистика	Психометрія
Родина	Latent structure	Latent structure
Рік появи	2000s	2000–2003
Автор методу	Building on Hennig (2004) and Vermunt & Magidson (2004)	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)
Тип	Robust latent variable / mixture model	Latent variable / dimension reduction (robust)
Основоположне джерело	Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.
Інші назви	robust LCA, outlier-resistant latent class analysis, trimmed-likelihood latent class analysis	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation
Пов'язані	6	4
Підсумок	Robust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimation, or downweighting — so that atypical response patterns do not distort the recovered class structure or class membership probabilities.	Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings.