手法を比較

	頑健探索的因子分析	確認的因子分析（CFA）
分野	心理測定学	心理測定学
系統	Latent structure	Latent structure
提唱年	2000–2003	1969
提唱者	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	Karl Gustav Jöreskog
種類	Latent variable / dimension reduction (robust)	Hypothesis-testing latent variable model
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.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
別名	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	CFA, confirmatory FA, measurement model, restricted 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.	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.