Bollen Kenneth A, Harden Jeffrey J, Ray Surajit, Zavisca Jane
H.R. Immerwahr Distinguished Professor, Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.
Assistant Professor, Department of Political Science, University of Colorado Boulder, Boulder, CO 80309.
Struct Equ Modeling. 2014;21(1):1-19. doi: 10.1080/10705511.2014.856691. Epub 2014 Jan 31.
Selecting between competing Structural Equation Models (SEMs) 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 SEMs 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.
在相互竞争的结构方程模型（SEM）之间进行选择是一个常见问题。通常选择是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，贝叶斯信息准则被广泛使用，但与其他拟合指数相比，它们在结构方程模型中的使用频率较低。本文研究了几种近似贝叶斯因子的新旧信息准则（IC）。我们在一个包括真实模型和虚假模型的模拟中，将这些IC度量与常见的拟合指数进行比较。在中等至大样本中，IC度量优于拟合指数。在第二个模拟中，我们只考虑IC度量，不包括真实模型。在中等至大样本中，IC度量倾向于那些仅因具有额外参数而与真实模型不同的近似模型。总体而言，一种新的IC度量——SPBIC，相对于其他IC度量表现良好。
