Abstract

Researchers in psychology, education, and organizational behavior often encounter multilevel data with hierarchical structures. Bayesian approach is usually more advantageous than traditional frequentist-based approach in small sample sizes, but it is also more susceptible to the subjective specification of priors. To investigate the potentially detrimental effects of inaccurate prior information on Bayesian approach and compare its performance with that of traditional method, a series of simulations was conducted under a multilevel model framework with different settings. The results reveal the devastating impacts of inaccurate prior information on Bayesian estimation, especially in the cases of larger intraclass correlation coefficient, smaller level 2 sample size, and smaller prior variance. When the dependent variable is non-normal or binary, these negative effects are more noticeable. The present study investigated the impacts of inaccurate prior information and provides advice on the specification of priors.

摘要

心理学、教育和组织行为学的研究人员经常遇到具有层次结构的多层次数据。贝叶斯方法通常比传统的基于频率的方法在小样本量下更有优势，但它也更容易受到先验主观规范的影响。为了研究不准确的先验信息对贝叶斯方法的潜在不利影响，并将其性能与传统方法进行比较，在具有不同设置的多级模型框架下进行了一系列模拟。结果揭示了不准确的先验信息对贝叶斯估计的破坏性影响，尤其是在类内相关系数较大、第 2 级样本量较小和先验方差较小的情况下。当因变量为非正态或二元时，这些负面影响更加明显。本研究调查了不准确先验信息的影响，并就先验规范提供了建议。