Daniel Posner, Political Science

Initial Publication Date: November 6, 2013

Daniel Posner is a professor of Political Science at UCLA, a public 4-year research institution. Information for this case study was obtained from an interview conducted on August 20, 2013. This page is part of a collection of profiles about a variety of techniques for integrating Quantitative Reasoning (QR) across the curriculum.

Jump down to Design and Implementation of QR Goals | Key QR Assignment of the Course | Challenges | Advice | Documents

Overview and Context

About the Course

Introduction to Comparative Politics is an introductory political science course. The research version of the course is offered in tandem with a statistical methods course taught by my colleague, John Zaller. Students must enroll in both courses and take them concurrently. Students attend each course two days per week. The courses also share a common set of discussion sections on Fridays which are led by teaching assistants (TA's). The TA's are familiar with the material from both courses and help integrate the two. During the common discussion section, students work on joint assignments that knit together substantive material from comparative politics with quantitative methods from the statistics course.

We have taught the course this way four times over a six-year period, and hope to continue in the future.

The class size of Introduction to Comparative Politics is normally between 200 to 300 students. However, when taught as the research version, the class size is only about 100 students due to limits on computer lab space. This course is one of a set of 4 to 5 introductory courses, of which political science majors must take at least 3. The audience is mainly first- and second-year students, but there are also some juniors and seniors.

Syllabus for Introduction to Comparative Politics (Microsoft Word 46kB Sep6 13)

Key QR Assignment Description (links to section in this page)

How Quantitative Reasoning (QR) and Literacy are Approached

It is fundamental to any social science discipline that students are able to use quantitative analysis skills and scientific reasoning to evaluate evidence and determine whether or not claims are true. I want students to learn to do comparative politics in a way that is as close as possible to the way it is actually done in the profession.

Design and Implementation of QR Goals

Motivation to integrate QR

Incorporating quantitative reasoning allows me to teach comparative politics in a way that I think it should be taught. It helps students look for patterns of political behavior and associations among important variables in the world. The goal of this orientation is to find generalizations and test whether or not they hold under different circumstances. For example, in addition to telling students, "Countries that are wealthy tend to be democratic," I am also able to illustrate the concept by putting up a scatter plot of per capita income and democracy. I draw a regression line through the scatter, and students who are taking the combined courses are able to understand that this line describes a general trend. Teaching this way is very enjoyable and I think it is a better way to teach comparative politics.

The department was motivated for us to teach these courses concurrently because they were convinced that this method was, from a pedagogical standpoint, a vastly superior way of teaching Introduction to Comparative Politics.

QR goals

I see the quantitative reasoning goals and my course goals as complementary. There are substantive issues with which I want students to become acquainted. There are theoretical ideas and regularities that they will need in upper-division comparative politics courses. But in my mind teaching the substance is secondary to introducing students to a way of reasoning; a way of viewing the world through that a causal-inference lens.

The course goals come from the goals we set in designing the course. We wanted to go beyond story-telling about specific countries or events, and focus on the process of political science research. Research starts with a hypothesis or an intuition about something that might be true, moves from there to a set of clearly stated implications about that hypothesis or intuition, and then leads to research design. Research involves the systematic collection of evidence or data and analysis to see whether or not the implications are born out.

Pedagogic approaches used

I primarily lecture during the course, but within the lecture I use real world puzzles to motivate students. I present the material in such a way that the questions are so compelling that students really want know the answer. For example, "Do left-leaning cabinets really spend more on social programs? What might be the cause of that? Why does it matter?" Students are strongly motivated to understand why it is that some countries have higher levels of social spending than others and what might be the implications for real people.

The discussion sections are taught by TA's who go over materials from both courses and try to knit them together. Specifically, the TA's go over problem sets which were explicitly designed to blend together the substance and the methods. We also collaborate a good deal as a team. Every week John, myself and the TA's meet to discuss everything that they will be teaching in the sections for both sides of the course and we talk in depth about how to interweave them together.

Knowing the course is successful

John and I had the sense that teaching the courses this way made a huge difference—that it was an incredibly successful undertaking. However, we would need to do more systematic assessment in order to prove that learning gains occurred compared to the other versions of these courses.

Some pieces of evidence were that many of the papers written at the end of my course by first- and second-year undergraduates were as good as some graduate students. These papers were quite sophisticated. In addition, while it could be a coincidence or self-selection, I think it is partly attributable to how these courses were taught that a number of our students caught the political science bug and decided to go on to do graduate training in political science. In addition, a disproportionate number of the students who took the research version of the course decided to write an honors thesis in their senior year.

Key QR Assignment of the Course

The key assignment is a midterm paper, The Politics of Inequality in Rich Democracies, which involves students reading pertinent literature, analyzing a dataset, and writing up the results.

Assignment Description: The Politics of Economic Inequality in Rich Democracies (Microsoft Word 47kB Sep6 13)
(Note: In this assignment description, PS 50 refers to Introduction to Comparative Politics, and PS 6 refers to Introduction to Statistics.)

The key assignment, The Politics of Inequality in Rich Democracies, involves a literature review, data analyses, and quantitative writing. The purpose of the assignment is to combine substantive knowledge with application of statistical tools in order to write an engaging, convincing paper. It is one in a series of problem sets that my course has in common with the statistical methods course that my colleague, John Zaller, teaches concurrently. This assignment illustrates how we knit the two courses together. In order to understand the topic students need to have the information I teach them in comparative politics. In order to do the data analyses, students need information from the methods course. And in order to interpret and explain the results, students need both.

In the assignment the following questions are posed:

"Can politics explain why some countries have much more economic inequality than others? Left parties commonly offer policies that they claim will lessen inequality, but does the election of Left parties to office really have any effect on economic inequality? Or is economic equality (or lack thereof) independent of politics?"

For their papers, students are asked to first describe relevant research and theory pertaining to the relationship between politics and inequality. Next, they were asked to analyze a given data set that included several variables. Two variables are looked at first: a measure of political orientation--the percentage of cabinet members that came from left-leaning political parties between 1960 and 1990; and, a measure of inequality--the share of national income earned by citizens in the bottom one-tenth of the income distribution in 1990.

Students examine the relationship between these two variables using a scatter plot and a linear regression. Based on their findings, they discuss their assessment of the relationship. Students also consider a third variable in order to determine if it might also affect either of the first two variables, which would lower confidence in the significance of the relationship. Students are presented with a number of possible variables, and select this third variable based on their prior readings. Students create two more scatterplots to examine the relationship between their third variable and the first two variables (political orientation and inequality.)

The paper concludes with a statement about what additional data or analysis would contribute to being able to reach stronger conclusions and summaries of the findings. The assignment is set up in such a way that students discover an important political science regularity which is the relationship between politics and income inequality.

This assignment helps students tie together comparative political science concepts and statistics. From a statistical standpoint, students use a simple regression procedure, but the only way to really make sense of the results is to understand the substantive material. For example, in Introduction to Comparative Politics, students learn about what a cabinet is, what it means to be a leftist or a rightist party, what it means to have the majority in the cabinet, what roles a cabinet plays in terms of policies, what kinds of policies matter for people, and how those policies get played out in measurable terms.

Challenges

  • Department buy-in. One difficulty was the logistical issue of getting the department to agree to commit the resources to allow me teach a smaller course. Normally, this course has 200 to 300 students when I teach it alone. However, since John's methods course required the use of computers, and only 50 students fit into a computer lab, the course had to be capped at a much lower number. John agreed to teach 2 sections; thus, we were able to include 100 students in the combined course.
  • Time and energy. There is some extra effort involved in teaching this way. But this is how I have always wanted to teach the course. So I don't feel like it is a costly investment. I am very pleased to be able to teach the course the way that I felt it should be taught.

Advice

  • Collaboration can be very rewarding. Find a good person to collaborate with and do it. John Zaller is fantastic. He was really the driver behind this joint course because the key to knitting together the two courses were the assignments. He was the one who spent most of the time creating the assignments--putting together the data sets--so that there were the patterns that could and couldn't be found and then holding the students' hands in terms of finding those patterns. My job is much easier. I motivate students to be interested in doing the exercises and then impress upon them the importance of being able to use these quantitative methods to try find the answers.

Documents