Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
Health Serv Res. 2012 Feb;47(1 Pt 1):255-74. doi: 10.1111/j.1475-6773.2011.01314.x. Epub 2011 Aug 30.
To explain the use of interaction terms in nonlinear models.
We discuss the motivation for including interaction terms in multivariate analyses. We then explain how the straightforward interpretation of interaction terms in linear models changes in nonlinear models, using graphs and equations. We extend the basic results from logit and probit to difference-in-differences models, models with higher powers of explanatory variables, other nonlinear models (including log transformation and ordered models), and panel data models. EMPIRICAL APPLICATION: We show how to calculate and interpret interaction effects using a publicly available Stata data set with a binary outcome. Stata 11 has added several features which make those calculations easier. LIMDEP code also is provided.
It is important to understand why interaction terms are included in nonlinear models in order to be clear about their substantive interpretation.
解释非线性模型中交互项的用法。
我们讨论了在多元分析中包含交互项的动机。然后，我们使用图表和方程解释了在非线性模型中，交互项在线性模型中的直接解释是如何变化的。我们将基本结果从逻辑回归和概率回归扩展到了差分模型、具有更高解释变量幂次的模型、其他非线性模型（包括对数变换和有序模型）以及面板数据模型。
我们使用一个带有二元结果的公共可用 Stata 数据集，展示了如何计算和解释交互效应。Stata 11 增加了几个功能，使得这些计算更加容易。还提供了 LIMDEP 代码。
为了清楚地了解交互项的实质解释，理解为什么在非线性模型中包含交互项是很重要的。
