中介效应的三类区间估计方法

由于中介效应ab的估计量通常不是正态分布, 因此需用不对称置信区间进行中介效应分析。详述了三类获得不对称置信区间的方法, 包括乘积分布法(M法和经验M法)、Bootstrap方法(偏差校正和未校正的非参数百分位Bootstrap方法、偏差校正和未校正的参数百分位残差Bootstrap方法)和马尔科夫链蒙特卡罗(MCMC)方法。比较了三类方法在单层(简单和多重)和多层中介效应分析中的表现, 发现三类方法的表现相近, 与乘积分布法相比, 偏差校正的百分位Bootstrap方法表现较好, 但有先验信息的MCMC方法能更有效降低均方误。最后对中介效应不对称置信区间研究的拓展方向做了展望。

Estimating Confidence Intervals of Mediating Effects by Using the Distribution of the Product, Bootstrap and Markov chain Monte Carlo Methods

Because the estimators of mediating effects are generally not normally distributed, it would be better use asymmetric confidence intervals to analyze mediating effects. There are three approaches to obtain the asymmetric confidence intervals of mediating effects: 1) Based on the distribution of the product (including M method and Empirical-M method); 2) Bootstrap methods (nonparametric percentile Bootstrap method, Bias-corrected nonparametric percentile Bootstrap method, parametric percentile residual Boots...