Selecting between competing structural equation models is a common problem. Often selection is based on the chi-square test statistic or other fit indices. In other areas of statistical research Bayesian information criteria are commonly used, but they are less frequently used with structural equation models compared to other fit indices. This article examines several new and old information criteria (IC) that approximate Bayes factors. We compare these IC measures to common fit indices in a simulation that includes the true and false models. In moderate to large samples, the IC measures outperform the fit indices. In a second simulation we only consider the IC measures and do not include the true model. In moderate to large samples the IC measures favor approximate models that only differ from the true model by having extra parameters. Overall, SPBIC, a new IC measure, performs well relative to the other IC measures.

中文翻译：

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结构方程模型选择中的 BIC 和替代贝叶斯信息准则

在竞争的结构方程模型之间进行选择是一个常见问题。通常选择基于卡方检验统计量或其他拟合指数。在统计研究的其他领域中，贝叶斯信息标准是常用的，但与其他拟合指数相比，它们较少用于结构方程模型。本文研究了几个近似贝叶斯因子的新旧信息标准 (IC)。我们将这些 IC 度量与包含真假模型的模拟中的常见拟合指数进行比较。在中到大的样本中，IC 度量优于拟合指数。在第二个模拟中，我们只考虑 IC 测量，不包括真实模型。在中到大的样本中，IC 测量倾向于近似模型，这些模型仅通过具有额外参数而与真实模型不同。全面的，