方法对比

	稳健探索性因子分析	稳健的验证性因子分析
领域	心理测量学	统计学
方法族	Latent structure	Latent structure
起源年份	2000–2003	1984–1994
提出者	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)
类型	Latent variable / dimension reduction (robust)	Confirmatory latent variable model with robust estimation
开创性文献	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.	Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.
别名	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA
相关	4	6
摘要	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.	Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable.