Value of sample size for computation of the Bayesian information criterion (BIC) in multilevel modeling
Value of sample size for computation of the Bayesian information criterion (BIC) in multilevel modeling
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DOI：
10.3758/s13428-018-1188-3
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发表时间：
2019-02-01
影响因子：
5.4
通讯作者：
Womack, Andrew
中科院分区：
文献类型：
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作者：
Lorah, Julie;Womack, Andrew
贝叶斯信息准则（BIC）可用于多水平建模研究中的模型选择。然而，BIC的公式需要样本量的值，这在多水平模型中是不清楚的，因为至少在两个水平上观察到样本量。在本研究中，我们使用模拟数据来评估假阳性率和功效时，水平1样本量，有效样本量，水平2样本量作为样本量值，在不同水平的样本量和组内相关系数值。结果表明，样本量的适当值取决于所进行的模型和测试。基于所研究的情景，我们建议使用BIC，该BIC基于每个水平的固定效应数和基于水平的样本量，对模型的每个水平具有不同的惩罚项。
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.