How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice
How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice
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DOI：
10.1017/pan.2018.46
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发表时间：
2019-04-01
影响因子：
5.4
通讯作者：
Xu, Yiqing
中科院分区：
文献类型：
--
作者：
Hainmueller, Jens;Mummolo, Jonathan;Xu, Yiqing
乘法交互模型在社会科学中被广泛用于检验结果与自变量之间的关系是否随调节变量而变化。目前的实证实践往往忽视了两个重要问题。首先，这些模型假设线性相互作用效应随调节因子以恒定速率变化。其次，如果缺乏调节者的共同支持，对自变量条件效应的估计可能会产生误导。我们复制了5个顶级政治学期刊最近发表的22篇文章中的46个相互作用效应，发现这些核心假设在实践中往往失败，这表明基于相互作用模型的所有政治学子领域的大部分发现都是脆弱的，并且依赖于模型。我们提出了一个简单的诊断清单来评估这些假设的有效性，并提供灵活的估计策略，允许非线性相互作用的影响和防止过度的外推。这些统计例程在R和STATA中都可用。
Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two important problems. First, these models assume a linear interaction effect that changes at a constant rate with the moderator. Second, estimates of the conditional effects of the independent variable can be misleading if there is a lack of common support of the moderator. Replicating 46 interaction effects from 22 recent publications in five top political science journals, we find that these core assumptions often fail in practice, suggesting that a large portion of findings across all political science subfields based on interaction models are fragile and model dependent. We propose a checklist of simple diagnostics to assess the validity of these assumptions and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation. These statistical routines are available in both R and STATA.