For Fellow Teachers: Revised Primers on Linear Regression and Causality

If you teach in a policy school or in a political science department, chances are some of your students are not quite conversant in the quantitative methods used in the social sciences.

Many of the students who sign up for my fall seminar on the Microeconomics of International Development Policy or my spring seminar on Law, Economics and Organization, for example, are incredibly bright, but they are not familiar with regression analysis, and so they don’t know how to read a regression table. This makes it difficult to assign empirical papers in World Development for in-class discussion, let alone papers in the Journal of Development Economics.

While I do not have the time to teach basic econometrics to students in those seminars, I have prepared two handouts for them to read in preparation for reading papers containing empirical results, which I thought I should make available to anyone who would rather not spend precious class time teaching the basics of quantitative methods. I have used both these handouts in my development seminar last fall, and my students said that they had learned quite a bit from reading them.

Given that many of us are spending these days revising our syllabus for the fall semester, I have revised my empirical handouts for the new academic year, and I am happy to make them available to whoever wants to use them. If you use them, I simply request that you do not modify them and that you let me know about how I can improve them for next year.

The first handout is a primer on linear regression, which shows analytically and graphically (and hopefully painlessly) what a regression does, and why it is such a useful tool in the social sciences. Perhaps more importantly, this handout also explains how to read a regression table.

The second handout primer on the identification of causal relationships in the social sciences, which discusses the distinction between correlation and causation and explains two ways in which social scientists go about making causal statements (i.e., randomized controlled trials and instrumental variables), with a few examples. I suggest supplementing this handout with a reading of Jim Manzi’s “What Social Science Does–and Doesn’t–Know” in City Journal as well as with Esther Duflo’s TED talk.

Of course, neither handout is a substitute for a course in econometrics or on research design, respectively, but these handouts are intended primarily for undergraduates or Masters students with little to no quantitative background.

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