This study proposes a Bayesian approach to testing informative hypotheses in confirmatory factor analysis (CFA) models. The informative hypothesis, which is formulated by the constrained loadings, can directly represent researchers' theories or expectations about the tau equivalence in reliability analysis, item-level discriminant validity, and relative importance of indicators. Support for the informative hypothesis is quantified by the Bayes factor. We present the adjusted fractional Bayes factor of which the prior distribution is specified using a part of the data and adjusted according to the hypotheses under evaluation. This Bayes factor is derived and computed using the Markov chain Monte Carlo posterior samples of model parameters. Simulation studies investigate the performance of the proposed Bayes factor. A classic example of CFA models is used to illustrate the construction of the informative hypothesis, the specification of the prior distribution, and the computation and interpretation of the Bayes factor. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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使用贝叶斯因子测试因子分析模型中的信息假设。


本研究提出了一种贝叶斯方法来测试验证性因素分析 (CFA) 模型中的信息假设。由约束载荷制定的信息假设可以直接代表研究者对可靠性分析、项目级判别效度和指标相对重要性中的tau等价性的理论或期望。对信息性假设的支持通过贝叶斯因子进行量化。我们提出了调整后的分数贝叶斯因子，其先验分布是使用部分数据指定的，并根据评估的假设进行调整。该贝叶斯因子是使用模型参数的马尔可夫链蒙特卡罗后验样本导出和计算的。模拟研究调查了所提出的贝叶斯因子的性能。使用 CFA 模型的经典示例来说明信息假设的构建、先验分布的规范以及贝叶斯因子的计算和解释。 （PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。