Salīdzināt metodes

	Robustā robustā faktoru analīze	Apstiprinošā faktoru analīze (AFA)
Nozare	Statistika	Psihometrija
Saime	Latent structure	Latent structure
Izcelsmes gads	1984–1994	1969
Autors	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)	Karl Gustav Jöreskog
Tips	Confirmatory latent variable model with robust estimation	Hypothesis-testing latent variable model
Pirmavots	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.
Citi nosaukumi	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA	CFA, confirmatory FA, measurement model, restricted factor analysis
Saistītās	6	4
Kopsavilkums	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.