Salīdzināt metodes

	Robustas mediācijas analīze	Robust Structural Equation Modeling
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	2008–2014	1994
Autors	Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008)	Albert Satorra & Peter M. Bentler
Tips	Causal inference / indirect effects	Latent variable / path model with robust inference
Pirmavots	Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20.	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 (pp. 399–419). Sage.
Citi nosaukumi	robust indirect effects, outlier-resistant mediation, robust causal mediation	Robust SEM, SEM with robust standard errors, Satorra-Bentler SEM, non-normal SEM
Saistītās	5	5
Kopsavilkums	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.	Robust structural equation modeling (Robust SEM) applies the full SEM framework — simultaneous estimation of measurement and structural relations among latent variables — while using corrected test statistics and sandwich standard errors that remain valid when observed data depart from multivariate normality. The Satorra-Bentler scaled chi-square is the most widely used correction.