Using Shrinkage in Multilevel Models to Understand Intersectionality A Simulation Study and a Guide for Best Practice
Using Shrinkage in Multilevel Models to Understand Intersectionality A Simulation Study and a Guide for Best Practice
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DOI：
10.1027/1614-2241/a000167
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发表时间：
2019-04-01
影响因子：
3.1
通讯作者：
Jones, Kelvyn
中科院分区：
文献类型：
--
作者：
Bell, Andrew;Holman, Daniel;Jones, Kelvyn
多层次模型最近被用来实证研究的想法，社会特征是交叉的，如年龄，性别，种族和社会经济地位相互作用，以推动结果。有些人认为这种方法解决了标准虚拟变量（固定效应）回归中的多重检验问题，因为交叉效应会自动向均值收缩。希望在固定效应回归中偶然出现的具有统计学意义的交叉点在多水平模型中不会出现。然而，这需要一些可能被打破的假设。我们使用模拟来显示打破这些假设的效果：当存在真正的主效应/相互作用时，在模型的固定部分未建模。我们发现，虽然多层次的方法优于固定效应的方法，收缩小于预期的，一些交叉效应可能会出现错误的统计显着的机会。最后，我们建议使这种有前途的方法工作稳健。
Multilevel models have recently been used to empirically investigate the idea that social characteristics are intersectional such as age, sex, ethnicity, and socioeconomic position interact with each other to drive outcomes. Some argue this approach solves the multiple-testing problem found in standard dummy-variable (fixed-effects) regression, because intersectional effects are automatically shrunk toward their mean. The hope is intersections appearing statistically significant by chance in a fixed-effects regression will not appear so in a multilevel model. However, this requires assumptions that are likely to be broken. We use simulations to show the effect of breaking these assumptions: when there are true main effects/interactions, unmodeled in the fixed part of the model. We show, while the multilevel approach outperforms the fixed-effects approach, shrinkage is less than is desired, and some intersectional effects are likely to appear erroneously statistically significant by chance. We conclude with advice to make this promising method work robustly.