Võrdle meetodeid

	Robust Path Analysis	Robustne mediatsioonianalüüs
Valdkond	Statistika	Statistika
Perekond	Latent structure	Latent structure
Tekkeaasta	1998	2008–2014
Looja	Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)	Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008)
Tüüp	Causal path modeling with robust estimation	Causal inference / indirect effects
Algallikas	Yuan, K.-H. & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88.	Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20.
Rööpnimetused	robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling	robust indirect effects, outlier-resistant mediation, robust causal mediation
Seotud	6	5
Kokkuvõte	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.	Robust mediation analysis estimates the indirect effect of an independent variable on an outcome through one or more mediators using estimators that resist the influence of outliers and non-normal error distributions. By combining robust regression (such as median or M-estimation) with percentile or bias-corrected bootstrap confidence intervals, it yields trustworthy conclusions when standard ordinary-least-squares mediation would be distorted by extreme observations.