Bandingkan metode

	Analisis Faktor Konfirmatori Robust	Pemodelan Persamaan Struktural
Bidang	Statistika	Statistika Penelitian
Keluarga	Latent structure	Process / pipeline
Tahun asal	1984–1994	1921
Pencetus	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)	Sewall Wright
Tipe	Confirmatory latent variable model with robust estimation	Method
Sumber perintis	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., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Alias	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA	SEM, path analysis, latent variable modeling, causal modeling
Terkait	6	3
Ringkasan	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.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.