Raykov Tenko, DiStefano Christine, Calvocoressi Lisa
Michigan State University, East Lansing, USA.
University of South Carolina, Columbia, USA.
Educ Psychol Meas. 2024 Apr;84(2):271-288. doi: 10.1177/00131644231166348. Epub 2023 Apr 21.
This note demonstrates that the widely used Bayesian Information Criterion (BIC) need not be generally viewed as a routinely dependable index for model selection when the bifactor and second-order factor models are examined as rival means for data description and explanation. To this end, we use an empirically relevant setting with multidimensional measuring instrument components, where the bifactor model is found consistently inferior to the second-order model in terms of the BIC even though the data on a large number of replications at different sample sizes were generated following the bifactor model. We therefore caution researchers that routine reliance on the BIC for the purpose of discriminating between these two widely used models may not always lead to correct decisions with respect to model choice.
本笔记表明，当将双因素模型和二阶因素模型作为数据描述与解释的竞争手段进行考察时，广泛使用的贝叶斯信息准则（BIC）通常不应被视为模型选择的常规可靠指标。为此，我们使用了一个具有多维测量工具组件的实证相关设置，在该设置中，尽管不同样本量下大量重复的数据是按照双因素模型生成的，但在BIC方面，双因素模型始终被发现劣于二阶模型。因此，我们提醒研究人员，为区分这两种广泛使用的模型而常规依赖BIC，在模型选择方面可能并不总是能做出正确决策。
