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| 강건한 매개 분석× | 매개 분석× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열≠ | Latent structure | Hypothesis test |
| 기원 연도≠ | 2008–2014 | 1986 |
| 창시자≠ | Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008) | Baron & Kenny |
| 유형≠ | Causal inference / indirect effects | Indirect effects / path test |
| 원전≠ | Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20. DOI ↗ | Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182. link ↗ |
| 별칭≠ | robust indirect effects, outlier-resistant mediation, robust causal mediation | indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS) |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Mediation analysis is a statistical procedure that tests whether the effect of an independent variable X on an outcome Y operates wholly or partly through a third variable M, called the mediator. Formalised by Baron and Kenny in 1986, it decomposes the total effect of X on Y into a direct path (c′) and an indirect path (a × b), quantifying how much of the relationship is carried by the mediating mechanism. |
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