方法对比

	稳健对应分析	稳健探索性因子分析
领域	统计学	心理测量学
方法族	Latent structure	Latent structure
起源年份	2000s (robust extensions of CA developed since the early 2000s)	2000–2003
提出者	Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)
类型	Robust dimension reduction for contingency tables	Latent variable / dimension reduction (robust)
开创性文献	Croux, C. & Ruiz-Gazen, A. (2005). High breakdown estimators for principal components: the projection-pursuit approach revisited. Journal of Multivariate Analysis, 95(1), 206–226.	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.
别名	RCA, outlier-resistant correspondence analysis, robust CA	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation
相关	5	4
摘要	Robust Correspondence Analysis (RCA) extends classical correspondence analysis to contingency tables that contain outlying rows or columns. By replacing the standard singular value decomposition with a robust alternative, RCA produces biplots and coordinate maps that accurately reflect the dominant association structure even when atypical cells or categories exert undue influence on the standard solution.	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.