手法を比較

	ロバスト潜在クラス分析	頑健探索的因子分析
分野	統計学	心理測定学
系統	Latent structure	Latent structure
提唱年	2000s	2000–2003
提唱者	Building on Hennig (2004) and Vermunt & Magidson (2004)	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)
種類	Robust latent variable / mixture model	Latent variable / dimension reduction (robust)
原典	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.
別名	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
関連	6	4
概要	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.