文章基本信息

标题：Prior Specification for More Stable Bayesian Estimation of Multilevel Latent Variable Models in Small Samples: A Comparative Investigation of Two Different Approaches
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作者：Zitzmann, Steffen ; Helm, Christoph ; Hecht, Martin 等
期刊名称：Frontiers in Psychology
电子版ISSN：1664-1078
出版年度：2021
卷号：11
页码：4027
DOI：10.3389/fpsyg.2020.611267
出版社：Frontiers Media
摘要：Bayesian approaches for estimating multilevel latent variable models can be beneficial in small samples. Prior distributions can be used to overcome small sample problems, for example, when priors that increase the accuracy of estimation are chosen. This article discusses two different but not mutually exclusive approaches for specifying priors. Both approaches aim at stabilizing estimators in such a way that the Mean Squared Error (MSE) of the estimator of the between-group slope will be small. In the first approach, the MSE is decreased by specifying a slightly informative prior for the group-level variance of the predictor variable, whereas in the second approach, the decrease is achieved directly by using a slightly informative prior for the slope. Mathematical and graphical inspections suggest that both approaches can be effective for reducing the MSE in small samples, thus rendering them attractive in these situations. The article also discusses how these approaches can be implemented in Mplus.
关键词：Bayesian estimation; Markov chain Monte Carlo; multilevel modeling; Structural Equation Modeling; Small sample