方法对比

	稳健调节分析	稳健中介分析
领域	统计学	统计学
方法族	Latent structure	Latent structure
起源年份	2007	2008–2014
提出者	Hayes & Cai; Wilcox	Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008)
类型	Robust regression-based interaction test	Causal inference / indirect effects
开创性文献	Hayes, A. F. & Cai, L. (2007). Using heteroscedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39(4), 709–722.	Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20.
别名	robust interaction analysis, robust moderated regression, HC-corrected moderation, outlier-resistant interaction testing	robust indirect effects, outlier-resistant mediation, robust causal mediation
相关	5	5
摘要	Robust moderation analysis tests whether the effect of a predictor on an outcome depends on the level of a moderator variable, using estimation methods that remain valid under non-normality, heteroscedasticity, or the presence of influential outliers. It is the preferred approach when standard ordinary least squares assumptions cannot be trusted.	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.