Department of Counseling and Educational Psychology, Indiana University School of Education, Bloomington, IN, USA.
Department of Statistics, Indiana University, Bloomington, IN, USA.
Behav Res Methods. 2019 Feb;51(1):440-450. doi: 10.3758/s13428-018-1188-3.
The Bayesian information criterion (BIC) can be useful for model selection within multilevel-modeling studies. However, the formula for the BIC requires a value for sample size, which is unclear in multilevel models, since sample size is observed for at least two levels. In the present study, we used simulated data to evaluate the rate of false positives and the power when the level 1 sample size, the effective sample size, and the level 2 sample size were used as the sample size value, under various levels of sample size and intraclass correlation coefficient values. The results indicated that the appropriate value for sample size depends on the model and test being conducted. On the basis of the scenarios investigated, we recommend using a BIC that has different penalty terms for each level of the model, based on the number of fixed effects at each level and the level-based sample sizes.
贝叶斯信息准则（BIC）可用于多层次模型研究中的模型选择。然而，BIC 的公式需要样本量的值，而在多层次模型中，由于样本量至少在两个层次上被观察到，因此样本量的值并不明确。在本研究中，我们使用模拟数据评估了当将一级样本量、有效样本量和二级样本量用作样本量值时，在不同样本量和组内相关系数值水平下的假阳性率和功效。结果表明，样本量的适当值取决于所进行的模型和检验。基于所调查的情况，我们建议根据模型中每个级别的固定效应数量和基于级别的样本量，为模型的每个级别使用具有不同惩罚项的 BIC。
