BIC and Alternative Bayesian Information Criteria in the Selection of Structural Equation Models
BIC and Alternative Bayesian Information Criteria in the Selection of Structural Equation Models
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DOI：
10.1080/10705511.2014.856691
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发表时间：
2014-01-02
影响因子：
6
通讯作者：
Zavisca, Jane
中科院分区：
文献类型：
--
作者：
Bollen, Kenneth A.;Harden, Jeffrey J.;Zavisca, Jane
在相互竞争的结构方程模型之间进行选择是一个常见的问题。通常基于卡方检验统计量或其他适合度指数进行选择。在统计研究的其他领域，通常使用贝叶斯信息标准，但与其他拟合指数相比，在结构方程模型中使用贝叶斯信息标准的频率较低。本文考察了几种近似贝叶斯因子的新旧信息准则(IC)。在包含真模型和假模型的模拟中，我们将这些IC度量与常见的拟合指数进行了比较。在中到大样本中，IC度量的表现优于拟合指数。在第二个模拟中，我们只考虑IC度量，而不包括真实模型。在中等到较大的样本中，IC度量偏爱与真实模型仅有不同之处的近似模型，因为它们有额外的参数。总体而言，SPBIC是一种新的IC指标，相对于其他IC指标表现良好。
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.