方法对比

	稳健探索性因子分析	验证性因子分析（CFA）
领域	心理测量学	心理测量学
方法族	Latent structure	Latent structure
起源年份	2000–2003	1969
提出者	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	Karl Gustav Jöreskog
类型	Latent variable / dimension reduction (robust)	Hypothesis-testing latent variable model
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.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
别名	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	CFA, confirmatory FA, measurement model, restricted factor analysis
相关	4	4
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.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.