手法を比較

	頑健探索的因子分析	因子分析（EFA）
分野	心理測定学	統計学
系統	Latent structure	Latent structure
提唱年	2000–2003	—
提唱者	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	—
種類	Latent variable / dimension reduction (robust)	Latent variable / dimension reduction
原典	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
別名	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	common factor analysis, açımlayıcı faktör analizi, factor analysis
関連	4	4
概要	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.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.