Department of Psychology, McGill University, Montreal, Canada.
T. Denny Sanford School of Social & Family Dynamics, Arizona State University, Tempe, AZ, USA.
Multivariate Behav Res. 2021 Jan-Feb;56(1):20-40. doi: 10.1080/00273171.2019.1709405. Epub 2020 Jan 31.
In manifest variable models, Bayesian methods for mediation analysis can have better statistical properties than commonly used frequentist methods. However, with latent variables, Bayesian mediation analysis with diffuse priors can yield worse statistical properties than frequentist methods, and no study to date has evaluated the impact of informative priors on statistical properties of point and interval summaries of the mediated effect. This article describes the first examination of using fully conjugate and informative (accurate and inaccurate) priors in Bayesian mediation analysis with latent variables. Results suggest that fully conjugate priors and informative priors with the same relative prior sample sizes have notably different effects at = 200 and 400, than at = 50 and 100. Consequences of a small amount of inaccuracy in priors for loadings can be alleviated by making the prior less informative, whereas the same is not always true of inaccuracy in priors for structural paths. Finally, the consequences of using informative priors depend on the inferential goals of the analysis: inaccurate priors are more detrimental for accurately estimating the mediated effect than for evaluating whether the mediated effect is nonzero. Recommendations are provided about when to gainfully employ Bayesian mediation analysis with latent variables.
在显变量模型中，贝叶斯中介分析方法的统计性质可能优于常用的频率派方法。然而，对于潜在变量，贝叶斯中介分析中具有弥散先验的方法可能会产生比频率派方法更差的统计性质，并且迄今为止尚无研究评估信息先验对中介效应的点估计和区间估计的统计性质的影响。本文首次考察了在具有潜在变量的贝叶斯中介分析中使用完全共轭和信息先验（准确和不准确）的情况。结果表明，在 = 200 和 400 时，完全共轭先验和具有相同相对先验样本大小的信息先验的影响明显不同于 = 50 和 100 时的影响。通过使先验的信息量减少，可以减轻对加载的先验少量不准确的影响，而对于结构路径的先验不准确则并非总是如此。最后，使用信息先验的后果取决于分析的推断目标：不准确的先验对于准确估计中介效应比评估中介效应是否为零更不利。本文提供了有关何时可以有益地使用具有潜在变量的贝叶斯中介分析的建议。
