频数推理
结构方程建模
贝叶斯概率
先验概率
计量经济学
可靠性(半导体)
统计
马尔科夫蒙特卡洛
背景(考古学)
贝叶斯推理
拟合优度
蒙特卡罗方法
数学
贝叶斯统计
计算机科学
地理
物理
功率(物理)
考古
量子力学
作者
Timothy R. Konold,Elizabeth A. Sanders
标识
DOI:10.1080/10705511.2023.2220915
摘要
Within the frequentist structural equation modeling (SEM) framework, adjudicating model quality through measures of fit has been an active area of methodological research. Complicating this conversation is research revealing that a higher quality measurement portion of a SEM can result in poorer estimates of overall model fit than lower quality measurement models, given the same structural misspecifications. Through population analysis and Monte Carlo simulation, we extend the earlier research to recently developed Bayesian SEM measures of fit to evaluate whether these indices are susceptible to the same reliability paradox, in the context of using both uninformative and informative priors. Our results show that the reliability paradox occurs for RMSEA, and to some extent, gamma-hat and PPP (measures of absolute fit); but not CFI or TLI (measures of relative fit), across Bayesian (MCMC) and frequentist (maximum likelihood) SEM frameworks alike. Taken together, these findings indicate that the behavior of these newly adapted Bayesian fit indices map closely to their frequentist analogs. Implications for their utility in identifying incorrectly specified models are discussed.
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