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| 頑健な調整済み媒介分析× | ロバスト調整効果分析× | |
|---|---|---|
| 分野 | 統計学 | 統計学 |
| 系統 | Latent structure | Latent structure |
| 提唱年≠ | 2007–2013 | 2007 |
| 提唱者≠ | Hayes, A. F.; building on Preacher, Rucker & Hayes (2007) for moderated mediation and robust bootstrap inference | Hayes & Cai; Wilcox |
| 種類≠ | Conditional indirect effect model with robust inference | Robust regression-based interaction test |
| 原典≠ | Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). Guilford Press. ISBN: 978-1462549030 | 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. DOI ↗ |
| 別名 | robust conditional process analysis, robust mediated moderation, robust moderated indirect effects, robust conditional indirect effects | robust interaction analysis, robust moderated regression, HC-corrected moderation, outlier-resistant interaction testing |
| 関連 | 5 | 5 |
| 概要≠ | 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 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. |
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