综合前人研究成果获取贝叶斯结构方程模型先验值的方法比较

Psych Pub Date : 2024-01-03 DOI:10.3390/psych6010004
Holmes W. Finch
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引用次数: 0

摘要

与更常用的最大似然法相比,贝叶斯估计潜变量模型为处理小样本和复杂模型的研究人员提供了一些独特的优势。贝叶斯建模的一个关键方面是选择相关参数的先验分布。先前的研究表明,使用默认先验(通常为非信息先验)可能会产生有偏差和低效率的估计值。因此,建议数据分析师尽可能从先前的研究中获取有用的、信息丰富的先验值。当前模拟研究的目标是比较几种方法,这些方法旨在结合先前研究的结果,为结构方程模型中的回归系数提供信息先验。这些方法包括非信息先验法、贝叶斯综合法、集合分析法、聚合先验法、标准元分析法、幂先验法和元分析预测法。结果表明,幂先验和元分析预测先验与贝叶斯估计法结合使用,可能会产生最准确的潜在结构系数估计值。讨论了对实践的启示和对未来研究的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparison of Methods for Synthesizing Results from Previous Research to Obtain Priors for Bayesian Structural Equation Modeling
Bayesian estimation of latent variable models provides some unique advantages to researchers working with small samples and complex models when compared with the more commonly used maximum likelihood approach. A key aspect of Bayesian modeling involves the selection of prior distributions for the parameters of interest. Prior research has demonstrated that using default priors, which are typically noninformative, may yield biased and inefficient estimates. Therefore, it is recommended that data analysts obtain useful, informative priors from prior research whenever possible. The goal of the current simulation study was to compare several methods designed to combine results from prior studies that will yield informative priors for regression coefficients in structural equation models. These methods include noninformative priors, Bayesian synthesis, pooled analysis, aggregated priors, standard meta-analysis, power priors, and the meta-analytic predictive methods. Results demonstrated that power priors and meta-analytic predictive priors, used in conjunction with Bayesian estimation, may yield the most accurate estimates of the latent structure coefficients. Implications for practice and suggestions for future research are discussed.
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