Understanding interaction models: Improving empirical analyses
Understanding interaction models: Improving empirical analyses
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DOI：
10.1093/pan/mpi014
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发表时间：
2006-12-01
影响因子：
5.4
通讯作者：
Golder, M
中科院分区：
文献类型：
--
作者：
Brambor, T;Clark, WR;Golder, M
乘法交互作用模型在定量政治学文献中很常见。这是有原因的。制度的论点往往意味着政治投入和结果之间的关系取决于制度背景。战略互动模型通常也会产生条件假设。尽管条件假设在政治学中无处不在，乘法交互模型也被发现能很好地捕捉到它们的直觉，但对1998年至2002年三大政治学期刊的调查表明，这些模型的执行往往存在缺陷，推理错误也很常见。我们相信，如果学者们遵循这篇文章中提出的使用乘法互动模型的简单清单，那么我们对政治世界的理解就会有相当大的进步。在我们的调查中，只有10%的文章遵循了清单。
Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although conditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10% of the articles in our survey followed the checklist.