方法对比
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| 稳健中介分析× | 结构方程模型× | |
|---|---|---|
| 领域≠ | 统计学 | 研究统计学 |
| 方法族≠ | Latent structure | Process / pipeline |
| 起源年份≠ | 2008–2014 | 1921 |
| 提出者≠ | Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008) | Sewall Wright |
| 类型≠ | Causal inference / indirect effects | Method |
| 开创性文献≠ | Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20. DOI ↗ | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ |
| 别名≠ | robust indirect effects, outlier-resistant mediation, robust causal mediation | SEM, path analysis, latent variable modeling, causal modeling |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. |
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