Compară metode

	Analiza robustă a căilor	Modelarea ecuațiilor structurale
Domeniu	Statistică	Statistică pentru cercetare
Familie	Latent structure	Process / pipeline
Anul apariției	1998	1921
Autorul original	Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)	Sewall Wright
Tip	Causal path modeling with robust estimation	Method
Sursa seminală	Yuan, K.-H. & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88.	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.
Denumiri alternative	robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling	SEM, path analysis, latent variable modeling, causal modeling
Înrudite	6	3
Rezumat	Robust path analysis applies robust estimation — such as sandwich standard errors or M-estimation — to path models that specify directed causal relationships among observed variables. It preserves valid inference about path coefficients and indirect effects when data violate normality, contain outliers, or exhibit heteroscedasticity that would distort conventional standard errors.	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.