方法对比
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| 稳健有调节的中介分析× | 稳健路径分析× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 2007–2013 | 1998 |
| 提出者≠ | Hayes, A. F.; building on Preacher, Rucker & Hayes (2007) for moderated mediation and robust bootstrap inference | Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation) |
| 类型≠ | Conditional indirect effect model with robust inference | Causal path modeling with robust estimation |
| 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, K.-H. & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88. DOI ↗ |
| 别名 | robust conditional process analysis, robust mediated moderation, robust moderated indirect effects, robust conditional indirect effects | robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling |
| 相关≠ | 5 | 6 |
| 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 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. |
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