ABSTRACT

Objective: There is a growing interest in the potential benefits of applying Bayesian estimation for multilevel models of SCED data. Methodological studies have shown that Bayesian estimation resolves convergence issues, can be adequate for the small sample, and can improve the accuracy of the variance components. Despite the potential benefits, the lack of accessibility to software codes makes it difficult for applied researchers to implement Bayesian estimation in their studies. The purpose of this article is to illustrate a feasible way to implement Bayesian estimation using OpenBUGS software to analyze a complex SCED model where within-participants variability and autocorrelation may differ across cases. Method: By using extracted data from a published study, step-by-step guidance in analyzing the data using OpenBUGS software is provided, including (1) model specification, (2) prior distributions, (3) data entering, (4) model estimation, (5) convergence criteria, and (6) posterior inferences and interpretations. Result: Full codes for the analysis are provided.

摘要

目的：人们越来越关注将贝叶斯估计应用于SCED数据的多级模型的潜在好处。方法学研究表明，贝叶斯估计解决了收敛问题，可以满足小样本的需要，并且可以提高方差成分的准确性。尽管有潜在的好处，但是缺乏对软件代码的可访问性使得应用研究人员难以在他们的研究中实施贝叶斯估计。本文的目的是说明使用OpenBUGS软件执行贝叶斯估计以分析复杂的SCED模型的可行方法，其中参与者内部的可变性和自相关可能在不同情况下有所不同。方法：通过使用从公开发表的研究中提取的数据，提供了使用OpenBUGS软件分析数据的逐步指导，包括（1）模型规格，（2）先验分布，（3）数据输入，（4）模型估计， （5）收敛准则，以及（6）后验推论和解释。结果：提供了完整的分析代码。