Using the margins command to estimate and interpret adjusted predictions and marginal effects
Using the margins command to estimate and interpret adjusted predictions and marginal effects
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DOI：
10.1177/1536867x1201200209
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发表时间：
2012-01-01
期刊：
影响因子：
4.8
通讯作者：
Williams, Richard
中科院分区：
文献类型：
--
作者：
Williams, Richard
许多研究人员和期刊强烈强调效应的符号和统计意义--但往往很少强调这些发现的实质性和实际意义。正如Long和Freese(2006，分类因变量回归模型使用STATA[Stata Press])所显示的那样，通过计算假设或典型案例的预测值或期望值，结果往往可以变得更加有形。STATA 11引入了用于进行此类计算的新工具--因子变量和边距命令。这些命令可以完成以前由Stata自己的ADJUST和MFX命令完成的大部分工作，甚至更多。不幸的是，页边距语法的复杂性、描述它的令人望而生畏的50页参考手册条目以及对页边距相对于多年来广泛使用的旧命令所提供的内容的缺乏理解，可能会阻止一些研究人员研究边距命令如何为它们带来好处。因此，在本文中，我将解释什么是调整后的预测和边际效果，以及它们如何有助于解释结果。我将进一步解释为什么较旧的命令(如ADJUST和MFX)经常会产生不正确的结果，以及因子变量和边距命令如何避免这些错误。考虑了模型中变量设定代表值的不同方法(均值边际效应、平均边际效应和代表值边际效应)的相对优劣。我展示了在Stata 12中引入的边距图命令如何为显示和理解边距结果提供了一种图形化且通常容易得多的方法，并解释了为什么边距不会对交互术语产生边际影响。
Many researchers and journals place a strong emphasis on the sign and statistical significance of effects-but often there is very little emphasis on the substantive and practical significance of the findings. As Long and Freese (2006, Regression Models for Categorical Dependent Variables Using Stata [Stata Press]) show, results can often be made more tangible by computing predicted or expected values for hypothetical or prototypical cases. Stata 11 introduced new tools for making such calculations-factor variables and the margins command. These can do most of the things that were previously done by Stata's own adjust and mfx commands, and much more.Unfortunately, the complexity of the margins syntax, the daunting 50-page reference manual entry that describes it, and a lack of understanding about what margins offers over older commands that have been widely used for years may have dissuaded some researchers from examining how the margins command could benefit them.In this article, therefore, I explain what adjusted predictions and marginal effects are, and how they can contribute to the interpretation of results. I further explain why older commands, like adjust and mfx, can often produce incorrect results, and how factor variables and the margins command can avoid these errors. The relative merits of different methods for setting representative values for variables in the model (marginal effects at the means, average marginal effects, and marginal effects at representative values) are considered. I shows how the marginsplot command (introduced in Stata 12) provides a graphical and often much easier means for presenting and understanding the results from margins, and explain why margins does not present marginal effects for interaction terms.