विधियों की तुलना करें

	मजबूत अन्वेषी कारक विश्लेषण	अन्वेषणात्मक कारक विश्लेषण (EFA)
क्षेत्र	मनोमिति	सांख्यिकी
परिवार	Latent structure	Latent structure
उद्भव वर्ष	2000–2003	—
प्रवर्तक	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	—
प्रकार	Latent variable / dimension reduction (robust)	Latent variable / dimension reduction
मौलिक स्रोत	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.
उपनाम	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	common factor analysis, açımlayıcı faktör analizi, factor analysis
संबंधित	4	4
सारांश	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.