Advanced Political Research Design and Analysis

University of Washington, Winter 2017

Class Meetings

Class Tu 4:30–7:20 pm SAV 162
Lab Fri 1:30–3:20 pm SAV 121
(The CSSCR Small Lab)

Office Hours

Nora Monday 10:30–12:30 pm SMI 37
Jeff Thursday 10:00 am–12:00 pm SMI 221B

Instructions for Jeff’s office hours:

  1. Sign up for a 30-minute slot using the Google Calendar appointments link which I emailed.

  2. Email me 24 hours prior to your appointment with what you would like to discuss.

These instructions will ensure that our time is efficiently spent and I can be of the most help to you.


For questions about the course that would be of general interest to all students in the course, email the course mailing list, multi_pols501a_wi17@uw.edu, rather than the individual instructors. Please reserve emails to individual instructors for individual concerns, such as your data analysis project or personal matters.


The course is suitable for students with a large range of prior exposure to statistics and mathematics. No prior statistical, mathematical, or programming experience is necessary beyond arithmetic, algebra, and elementary calculus.

In the UW political science Ph.D. student methods sequence, POLS 501 is the first quantitative methods course. It focuses on data analysis, programming, and provides an introduction to probability and statistical inference. POLS 503 is the next course in the sequence, focuses on linear models for causal inference.



Data analysis project paper

Every student in this class will execute their own statistical data analysis of a research question. The results of this analysis will be presented as a paper due at the end of the course. See the schedule for the due date.

The purpose of this paper is for you to apply the quantitative methods used in this course to the real-world research problems that you will encounter in their research careers. The objective is to get you working with data on a research project as quickly as possible, even if it is a flawed project. So, due to the limited time in this course, it is not necessary for this paper to address an important research problem or a novel contribution to the literature. While those will not be criteria for the evaluation of this paper, you are encouraged to pursue those, as those ideas are what lead to publications. The paper will be evaluated on the appropriateness of the statistical methods applied to the data and question, and not the novelty or contribution of the question itself.

If you developed a research design for POLS 500, you may be able to use it in 501. However you need to confirm that you will be able to assemble a dataset to test a specific research hypothesis within the time constraints of this course, because you will be using it throughout the course. If that seems unlikely, you will need to choose a different project.


There will be approximately weekly assignments. Except when otherwise indicated, assignments will be announced by the start of class on Tuesday, and will be due prior to the start of class the next week. Assignments can consist of data analysis, substantive questions.

No late homework will accepted except in the case of a documented emergency.


Students will be evaluated on the whole of their work in this course with an emphasis on the final paper.

For this course, grades on the 4.0 scale have the following interpretation:
4.0 Exceptional
3.9 Very good
3.8 Meeting expectations
3.7 Somewhat below average
3.6 Not up to expectations
≤ 3.5 Way below expectations



  • Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science. O’Reilly Media.
  • Imai, Kosuke. 2017. A First Course in Quantitative Social Science. Princeton University Press. Forthcoming. Draft available on Canvas.
  • Bailey, Michael A. 2015. Real Stats: Using Econometrics for Political Science and Public Policy. Oxford University Press.
  • Diez, David M., Christopher D. Barr, and Mine Çetinkaya-Rundel. 2015. OpenIntro Statistics. 3rd ed.

R for Data Science will be relied upon for teaching R, while QSS will be relied upon for concepts in data analysis and statistics, and political science examples. OpenIntro Statistics provides supplementary statistical information. Real Stats will be provide supplementary information on regression and causal inference towards the end of the course, but it will be primarily used in the following course, POLS 503.

The primary text for R, R for Data Science, uses tidyverse, a set of R packages not included in R by default. I have written solutions to the exercises and some additional notes for R4DS. These are available at jrnold/e4qf.

However, the primary statistical text, QSS, relies upon the base R functionality, and uses different functions than those in R4DS. So, I have written (am writing) equivalent tidyverse R code to replace the code examples

Additionally, readings may be supplemented by my course notes and lecture slides.

Software and Computing

This course will use R, which is a free and open-source programming language primarily used for statistics and data analysis. We will also use RStudio, which is an easy-to-use interface to R. Instructions to install or upgrade R are here.

Students should have a laptop that they can bring to both class and lab as we will integrate computing with learning data analysis and statistics throughout the course.


Beyond what the teaching team can providing, there are several resources on campus that you can go for assistance with data, computing, and statistical problems:

  • Center for Social Science Computing and Research (CSSCR) has a drop-in statistical consulting center in Savery 119. They provide consulting on statistical software, e.g. R. Go there for software or data related questions.
  • CSSS Statistical Consulting provides general statistical consulting. Go there for questions about statistical methods.
  • eScience Data Science Office Hours


Science should be open, and this course builds up other open licensed material, so unless otherwise noted, all materials for this class are licensed under a Creative Commons Attribution 4.0 International License.

The source for the materials of this course is on GitHub at UW-POLS501/pols_501_wi17


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