Salīdzināt metodes

	Robustā robustā faktoru analīze	Daudzlīmeņu apstiprinošā faktoru analīze (MCFA)
Nozare	Statistika	Psihometrija
Saime	Latent structure	Latent structure
Izcelsmes gads	1984–1994	1994
Autors	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)	Bengt O. Muthen
Tips	Confirmatory latent variable model with robust estimation	Latent variable model / measurement 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.	Muthen, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398.
Citi nosaukumi	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA	MCFA, multilevel measurement model, two-level CFA, hierarchical CFA
Saistītās	6	6
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.	Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the standard tool for validating psychometric scales administered within natural groups such as classrooms, clinics, or organisations.