Using Multilevel Regression Mixture Models to Identify Level-1 Heterogeneity in Level-2 Effects.
Using Multilevel Regression Mixture Models to Identify Level-1 Heterogeneity in Level-2 Effects.
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DOI：
10.1080/10705511.2015.1035437
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Jaki T
中科院分区：
文献类型：
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作者：
Van Horn ML;Feng Y;Kim M;Lamont A;Feaster D;Jaki T
本文提出了一种新的探索性方法来评估二级预测因子的影响在一级单位之间的差异。多水平回归混合模型用于识别1级潜在类别，这些类别在一个或多个2级预测因子的影响上存在差异。蒙特卡罗模拟用于演示不同样本量的方法，并演示将随机效应中的1约束为零的结果。应用该方法来评估课堂实践对学生的影响的异质性，以显示可以用该方法回答的研究问题的类型，以及在估计多水平回归混合时面临的问题。
This paper proposes a novel exploratory approach for assessing how the effects of level-2 predictors differ across level-1 units. Multilevel regression mixture models are used to identify latent classes at level-1 that differ in the effect of one or more level-2 predictors. Monte Carlo simulations are used to demonstrate the approach with different sample sizes and to demonstrate the consequences of constraining 1 of the random effects to zero. An application of the method to evaluate heterogeneity in the effects of classroom practices on students is used to show the types of research questions which can be answered with this method and the issues faced when estimating multilevel regression mixtures.