Сравнение методов

	Робастный анализ медиации	Робастный анализ путей
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	2008–2014	1998
Автор метода	Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008)	Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)
Тип	Causal inference / indirect effects	Causal path modeling with robust estimation
Основополагающий источник	Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20.	Yuan, K.-H. & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88.
Другие названия	robust indirect effects, outlier-resistant mediation, robust causal mediation	robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling
Связанные	5	6
Сводка	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 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.