Making Dummies Work: Comparing the Effects of Dummy Variables in Linear and Non-linear Causal Models

There is an important debate within political science surrounding the use of ‘best-practise’ statistical techniques. One group of scholars argues that the data should determine which techniques are used. Another group argues that we should use the techniques that best allow us to answer our questions.

Even a cursory glance at the major political-science journals reveals a contemporary obsession with voting behaviour. In most voting behaviour articles, political scientists attempt to explain why individuals or groups vote the way they do. Because most vote choices involve a Yes-No (1-0) decision, we should, according to the ‘data-driven’ school of thought, employ non-linear probability techniques when investigating voting decisions. However, the ‘question-driven’ scholars argue that this approach is ineffective because non-linear techniques do not allow us to investigate those questions that most interest us. This article examines the efficacy of both schools of thought in relation to a specific but not uncommon voting behaviour problem. That is, should we use linear or non-linear techniques when employing a multi-stage, recursive causal model where the dependent variable is dichotomous and many of the independent variables are dummies?