方法对比

	稳健有调节的中介分析	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	2007–2013	2008–2014
提出者	Hayes, A. F.; building on Preacher, Rucker & Hayes (2007) for moderated mediation and robust bootstrap inference	Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008)
类型	Conditional indirect effect model with robust inference	Causal inference / indirect effects
开创性文献	Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). Guilford Press. ISBN: 978-1462549030	Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20.
别名	robust conditional process analysis, robust mediated moderation, robust moderated indirect effects, robust conditional indirect effects	robust indirect effects, outlier-resistant mediation, robust causal mediation
相关	5	5
摘要	Robust moderated mediation tests whether the indirect effect of X on Y through a mediator M varies as a function of a moderator W, while using robust estimation (percentile or bias-corrected bootstrap, heteroscedasticity-consistent standard errors, or M-estimation) to protect inference against non-normality, outliers, and heteroscedasticity in the data.	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.