ABSTRACT

New model fit and evaluation tools have been introduced into the Bayesian structural equation modeling framework, including Bayesian versions of classic approximate fit measures (RMSEA, CFI, and TLI), as well as a new adjustment of the posterior predictive p-value to properly account for missing data. We examine the performance of these indices for model fit and selection through a simulation study. This study was designed to assess the performance of several indices in the context of model misspecification and missing data across different sample sizes. Findings suggest that Bayesian approximate fit indices may be better suited for model selection than they are as direct measures of model fit. Implications for applied researchers are discussed.

摘要

贝叶斯结构方程建模框架中引入了新的模型拟合和评估工具，包括经典近似拟合度量的贝叶斯版本（RMSEA、CFI 和 TLI），以及对后验预测p值的新调整以正确考虑对于丢失的数据。我们通过模拟研究检查了这些指标在模型拟合和选择方面的表现。本研究旨在评估多个指数在模型错误指定和不同样本量数据缺失的情况下的表现。研究结果表明，贝叶斯近似拟合指数可能比作为模型拟合的直接度量更适合模型选择。讨论了对应用研究人员的影响。