Σύγκριση μεθόδων

	Ρόμπουστ Επιβεβαιωτική Ανάλυση Παραγόντων	Επαληθευτική Παραγοντική Ανάλυση (Confirmatory Factor Analysis - CFA)
Πεδίο	Στατιστική	Ψυχομετρία
Οικογένεια	Latent structure	Latent structure
Έτος προέλευσης	1984–1994	1969
Δημιουργός	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)	Karl Gustav Jöreskog
Τύπος	Confirmatory latent variable model with robust estimation	Hypothesis-testing latent variable model
Θεμελιώδης πηγή	Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Εναλλακτικές ονομασίες	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA	CFA, confirmatory FA, measurement model, restricted factor analysis
Συναφείς	6	4
Σύνοψη	Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable.	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.