Michael S. Lynch

POLS 7014: Intermediate Political Methodology
Spring 2019

Class Meeting: Wednesday 3:35-6:35, Candler Hall 214
Office Hours: Monday 1:30-3:30 or by appointment, Baldwin Hall 409

Course Syllabus

  • Syllabus.

    Homework Assignments

  • Homework #1 (due Jan. 16).
  • Homework #2 (due Jan. 23). (R-code and Graph of #6).
  • Homework #3 (due Jan. 30).
  • Homework #4 (due Feb. 6)
  • Homework #5 (due Feb. 13) (#2 key and #3 key).
  • Homework #6 (no due date - for exam practice only) (#2 Key)
  • Homework #7 (due March 20) (Key)
  • Homework #8 (Due March 27)
  • Homework #9 (April 10) (Key)
  • Homework #10 (April 17)
  • Homework #11 (no due date)

    Lecture Resources

  • January 16. Here is the R code that we went over in class on Jan. 16.
  • January 30. Here is the R code that we went over in class. Here are the articles we will discuss in class. Please read prior to class.
    King's On Political Methodology
    Data Visualization Article
    Tables to Graphs Article
    Tables to Graphs -- Palin Example
  • February 6 and 13. Here is the R code that we went over in class.
    Also, here is a complete mathematical derivation of least squares, for those of you that are interested.
  • March 6. Here are the interactive variable output tables we have been using in class. Here is the R code that we went over. All materials adapted from Kam and Franzese 2007. Matt Golder (Penn State) has a great website that discusses interactive variables. Both papers we went over in class are linked to his site.
  • March 20. Here is the transformations R code we used in class. We also looked at some Monte Carlo simulation R code.
  • March 27. Here is the DIY Effects R code and the Marginal Effects R code we used in class. I would encourage you to give the Berry et al. article on interactive variables another look. I would also recommend looking at some marginal effects plot information online. The R interplot library has code to make marginal effects plots. I also like Miles William's RPub on plotting marginal effects.
  • April 3. Here is the R code we used in class.
  • April 10. Here is the non-normal and non-constant errors R code we used in class.

    Software Resources

  • This course will make heavy use of statistical software. While you may use any software you would like, I recommend that you use R. You can download the latest version of R and find a lot of great R resources at the R Project for Statistical Computing.
  • A lot of students seem to like Quick R. This website gives generally easy to follow advice on a variety of R-related topics.
  • UGA's own Prof. Jamie Monogan has prepared an excellent short course on using R that should be a great resource for this class. Prof. Monogan's new book, Political Analysis Using R, is accessible for free via the UGA library. This link should get you started.
  • There is lots of information on the web about R. Rseek allows you to perform a Google-based search of websites dedicated to R.

    Additional Resources

  • We will use this article about drug testing to starting thinking about inference.
  • The Simulations and Demonstration section of the Rice Virtual Lab in Statistics is a great way to wrap your head around sampling distributions and the central limit theorem.
  • This list gives some good examples of published work that utilizes the simple stats we will learn at the beginning of the semester.