Сравнение методов

	Робастный эксплораторный факторный анализ	Эксплораторный факторный анализ (ЭФА)
Область	Психометрия	Статистика
Семейство	Latent structure	Latent structure
Год появления	2000–2003	—
Автор метода	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	—
Тип	Latent variable / dimension reduction (robust)	Latent variable / dimension reduction
Основополагающий источник	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.
Другие названия	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	common factor analysis, açımlayıcı faktör analizi, factor analysis
Связанные	4	4
Сводка	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.