Different Roles of Prior Distributions in the Single Mediator Model with Latent Variables.
Different Roles of Prior Distributions in the Single Mediator Model with Latent Variables.
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DOI：
10.1080/00273171.2019.1709405
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发表时间：
2021-01
影响因子：
3.8
通讯作者：
MacKinnon DP
中科院分区：
文献类型：
--
作者：
Miočević M;Levy R;MacKinnon DP
在显式变量模型中，贝叶斯中介分析方法比常用的频率论方法具有更好的统计特性。然而，与潜变量，贝叶斯中介分析与扩散先验可以产生更差的统计特性比频率论的方法，迄今为止，没有研究已经评估了信息先验的统计特性的点和区间摘要的中介效果的影响。本文介绍了第一次检查使用完全共轭和信息（准确和不准确）先验贝叶斯中介分析与潜变量。结果表明，具有相同相对先验样本量的完全共轭先验和信息先验在N = 200和400时的效果明显不同于N = 50和100时的效果。少量的不准确的先验载荷的后果可以减轻，使以前的信息量较少，而同样的不总是正确的先验结构路径。最后，使用信息先验的后果取决于分析的推理目标：不准确的先验更不利于准确估计介导效应，而不是评估介导效应是否为非零。建议何时有益地采用贝叶斯中介分析与潜变量。
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 N = 200 and 400, than at N = 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.
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