Department of Psychology, McGill University, 2001 McGill College, 7th Floor; Montreal, Quebec, H3A 1G1, Canada.
Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
Behav Res Methods. 2024 Feb;56(2):750-764. doi: 10.3758/s13428-023-02079-4. Epub 2023 Feb 22.
Mediation analysis in repeated measures studies can shed light on the mechanisms through which experimental manipulations change the outcome variable. However, the literature on interval estimation for the indirect effect in the 1-1-1 single mediator model is sparse. Most simulation studies to date evaluating mediation analysis in multilevel data considered scenarios that do not match the expected numbers of level 1 and level 2 units typically encountered in experimental studies, and no study to date has compared resampling and Bayesian methods for constructing intervals for the indirect effect in this context. We conducted a simulation study to compare statistical properties of interval estimates of the indirect effect obtained using four bootstrap and two Bayesian methods in the 1-1-1 mediation model with and without random effects. Bayesian credibility intervals had coverage closest to the nominal value and no instances of excessive Type I error rates, but lower power than resampling methods. Findings indicated that the pattern of performance for resampling methods often depended on the presence of random effects. We provide suggestions for selecting an interval estimator for the indirect effect depending on the most important statistical property for a given study, as well as code in R for implementing all methods evaluated in the simulation study. Findings and code from this project will hopefully support the use of mediation analysis in experimental research with repeated measures.
中介分析在重复测量研究中可以揭示实验操作改变因变量的机制。然而，在 1-1-1 单中介模型中，间接效应的区间估计的文献很少。迄今为止，大多数评估多层次数据中介分析的模拟研究考虑的情景与实验研究中通常遇到的 1 级和 2 级单位的预期数量不匹配，而且迄今为止，没有研究比较过在这种情况下构建间接效应区间的重采样和贝叶斯方法。我们进行了一项模拟研究，比较了在有和没有随机效应的情况下，使用四种自举法和两种贝叶斯法在 1-1-1 中介模型中获得的间接效应区间估计的统计性质。贝叶斯可信区间的覆盖率最接近标称值，没有过度的Ⅰ型错误率，但比重采样方法的功效低。研究结果表明，重采样方法的性能模式通常取决于随机效应的存在。我们根据特定研究中最重要的统计性质，为间接效应的区间估计提供了选择建议，以及在模拟研究中实施所有评估方法的 R 代码。本项目的研究结果和代码有望支持在具有重复测量的实验研究中使用中介分析。
