Paul Gronke, Political Science

Initial Publication Date: October 25, 2013

Paul Gronke is a professor of Political Science at Reed College, a private 4-year liberal arts institution. Information for this case study was obtained from an interview conducted on August 22nd, 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

I have taught the course Introduction to Political Behavior for about 11 years. A mixture of students from across the disciplines take this introductory course. The class is generally about half students who either are, or think they want to be, social science majors and the other half are students from around the college who need to get an intro course out of the way. First- or second-year students comprise more than fifty percent of the students, and there are often a few seniors taking the course as well. We have two sections with 24 students in each section.

Syllabus for Introduction to Political Behavior (Acrobat (PDF) 222kB Sep18 13)

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

How Quantitative Reasoning (QR) and Literacy are Approached

In the past I was involved in an internal curriculum evaluation regarding including a quantitative reasoning requirement at our school. We ended up settling on the term, "numeracy", which is an umbrella term encompassing both quantitative literacy and reasoning.

I teach in a field where quantitative skills are important if not essential to comprehending much of the material. But I think that if possible, quantitative reasoning should be--like writing--integrated across the disciplines. A stellar writing program example is Writing Across the Disciplines. We need a similar kind of program to incorporate quantitative reasoning across the disciplines. It should be part of substantive courses.

Design and Implementation of QR Goals

Motivation to integrate QR

I find teaching quantitative reasoning to be interesting and enjoyable. By and large, students also like this material and appreciate the skills they learn.

In addition, I strongly believe that having material like this in liberal arts colleges is absolutely vital. In my opinion, students should not be able to earn a liberal arts college degree without some exposure to quantitative reasoning. Certainly I don't think anyone should earn a political science degree without having exposure to statistics and quantitative material.

Another motivation is that I believe if everyone knew about concepts such as standard error and the basic theory of random sampling, people would make better decisions and the world would be a better place. People frequently make major policy decisions both in the academy and in the world based on incorrect understandings. An informed citizen or policy maker needs to know more than just how to add, do algebra, and understand distributions. They need to know about statistics such as the standard error.

QR goals

One goal is for students to develop an analytical approach to understanding politics based on quantitative data. I want students to understand what it means to be an empirical scientist compared to qualitative or normative approaches or textual analysis. Along these lines, I want everyone to come out of the course with a basic sense of how statistics works--summary statistics, standard errors, and dispersion.

When students are reading articles in their upper division courses and they get to the tables, charts or figures, I want them to do what most professional political scientists I know do, which is spend a lot of time staring at the tables and figures and possibly less time on the text.

Pedagogic approaches used

For the quantitative reasoning components, I tend to rely on modular exercises that illustrate ideas or theories students encounter in their readings. I try to design these in order to foster or to build upon previously learned skills

When I need to simply convey material to students, a lecture is often the best way. I have found it helpful to be explicit about the amount of lecture early on in the course so that students know what to expect.

On other days we have conference-style classes where we read a professional political science paper that illustrates important quantitative points. We'll usually spend an entire day going through a paper--looking at the charts and tables and deciphering what they mean.

I tend to create assignments that include active engaged learning on the part of the students such as asking students to go to websites and download data on their own. One example would be asking them to figure how many democracies there were in the world in a certain year and how many there are today using online data that they are able to find and download.

Knowing the course is successful

When I assess the quality of the final papers, I am always heartened by the number of students who do very well. This is true even for some who tell me early on that they're lost at sea and drowning. Then they go on to write a wonderful final paper.

My other colleagues in the social sciences offer positive assessments about my course. I have been told that they can tell when students have had my class because they have a comfort level with quantitative materials and have clearly had more exposure to these ideas compared to other students. I also see the same thing in my own upper division courses when the students come back and take my courses.

Key QR Assignment of the Course

The major QR assignment of the course is a seven to ten page final paper dealing with the question of voter participation and turnout. This quantitative analysis paper is a way for students to use the theoretical approaches that they have been learning throughout the course.

While the whole course leads up to this final paper, there are five specific modules (which I call data confrontation exercises) that enable students to be able to write their final paper. All of these data confrontation exercises build upon each other, so that students learn in steps what they need to know to complete the final assignment.

For some classes, we meet in a computer lab and work through computer exercises. Tutors help students learn to use the software needed to complete their final paper. For instance, they learn basic Excel skills such as reformatting a spreadsheet and doing basic computations.

For the final papers, an example of what students could write about would be turnout among blacks and whites in relation to higher and lower education levels. Some of the requirements are that they are able to use and properly cite the National Election Survey. They must describe how they took a concept about different levels of empowerment in society and measured that in relation to level of education. In addition, they need to have both bi-variate and multi-variate analyses. The data confrontation exercises and other things I teach them throughout the course help them to build up their skills in order to successfully write this term paper.


  • Time involved--grading, providing feedback, tutoring, and preparing for class. Teaching this way is a significant time investment.
    • There is a steady flow of grading. We hire a grader to help with the portions of the exercises for which there is only one right answer, but I must do a significant portion of the grading. I strive to provide frequent, meaningful feedback. I look forward to the day when we have more online assessment, which will simplify the grading process.
    • The hands-on aspects of teaching students to use Excel and statistical analysis requires one-on-one help within a group setting, which is where student tutors are critical.
    • The reward down the road is that once you invest the time to create quantitative reasoning modules, they can simply be renewed, but there is an initial time investment. It is a little more time-consuming to set up because when you're presenting quantitative material on the board, everything needs to add up and when you're doing a lab, it needs to work.
  • Student push-back. While students sometimes complain during the course because they are being pushed out of their comfort zone with numbers, as time goes by they generally come to appreciate the value of what they learned. Some students have an expectation about the way courses will be taught that influences their acceptance for quantitative components. If the college has a quantitative reasoning requirement, it helps because students realize they must learn this because the institution has made a statement that it is valuable.


  • Look for exercises and activities online. Look for online modules that have been created by others. For example, I make my modules accessible to people, and I hope more people will be encouraged to do that, such as through ICPSR, SSDAN, or
  • Teaching with data can be rewarding. I think it is a lot of fun. I think it can also ease your teaching load in the long run because once you've established the modules or the exercises, they're set. They basically run themselves because the schedule is determined, everything is in place, and you know the exercises are going to work.
  • Incorporate hands-on work. Hands-on work is absolutely critical. With assistance from your institution, run lab sessions where the process can move slowly. In addition, if your class is anything over about 15 or 16 students, it is also important to get help in running these. You can't both be trying to run something for a whole class and helping individual students who are having basic problems such as signing on to the Internet.
  • Gather support. Don't underestimate how valuable it would be to have graders, tutors, and support from the college. It would be particularly helpful to have a quantitative reasoning center similar to a writing center. This way students could find the right person to help them navigate through the materials.


Syllabus for Introduction to Political Behavior (Acrobat (PDF) 222kB Sep18 13)

Paul Gronke's Research Toolbox

SSDAN Logo ICPSR logo Teaching with Data Logo Teaching with Data Logo