Salīdzināt metodes

	Robust Path Analysis	Modelēšana ar strukturālām vienādojumiem
Nozare	Statistika	Pētniecības statistika
Saime	Latent structure	Process / pipeline
Izcelsmes gads	1998	1921
Autors	Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)	Sewall Wright
Tips	Causal path modeling with robust estimation	Method
Pirmavots	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.
Citi nosaukumi	robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling	SEM, path analysis, latent variable modeling, causal modeling
Saistītās	6	3
Kopsavilkums	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.