Salīdzināt metodes

	Robust Path Analysis	Ceļu analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1998	1921
Autors	Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)	Sewall Wright
Tips	Causal path modeling with robust estimation	Causal / mediation model
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.	Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.
Citi nosaukumi	robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling	PA, path coefficient analysis, observed-variable SEM, causal path modeling
Saistītās	6	5
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.	Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference.