Bayesian methods have the potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This article compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N ≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N ≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results.

中文翻译：

---

小样本研究中贝叶斯中介分析的威力


贝叶斯方法具有增强中介分析能力的潜力（Koopman、Howe、Hollenbeck 和 Sin，2015；Yuan 和 MacKinnon，2009）。本文将中介效应的贝叶斯可信区间的功效与正态理论、乘积分布、百分位数和 N ≤ 200 时偏差校正引导置信区间的功效进行了比较。具有扩散先验的贝叶斯方法的功效与分布相当乘积法和引导法，以及具有信息先验的贝叶斯方法最有效。还检查了先验分布的不同精确度。仅当 N ≥ 100 且影响较小、N < 60 且影响较大、N < 200 且影响中等时，精度的提高才会带来更大功效。心理学的一个实证例子说明了从先前选择到解释结果的单一中介模型的贝叶斯分析。