Quantitative Analysis in Political Science II

Political Science 582, Friday 1-3, Location: Seigle 205.

Fall 2015

  • Course Description: This course extends what you did in previous methods courses by focusing on nonlinear model forms for the outcome variable. These are typically called "generalized linear models," although for historical reasons people in political science call them "maximum likelihood models." The principle we will care about is how to adapt the standard linear model that you know so that a broader class of outcome variables can be accommodated. These include: counts, dichotomous outcomes, bounded variables, and more. There is a strong theoretical basis for the models that we will use. Also, the bulk of the learning in the course will take place outside of the classroom by reading, practicing using statistical software, replicating the work of others, and doing problem sets. Keep in mind that the skills attained in this course are those that the discipline of political science expects of any self-declared data-oriented researcher.

    The second aspect of the course is focused on the statistical package R which is completely free for downloading for Mac, Unix, Linux and that other platform at CRAN, the Comprehensive R Archive Network. R is an implementation of the S language, which is the default computational tool for research statisticians. Quite simply R is the most powerful, extensively featured, and capable statistical computing tool that has ever existed on this planet. And as mentioned, its free. We will not use Stata; don't ask.
     
  • Prerequisite Details: The only official prerequisite for this course is QPA I. However, each student should be familiar with: basic probability theory, statistical inference, hypothesis testing, and least squares estimation. The course will also assume a working knowledge of calculus and linear algebra at the level of Essential Mathematics for Political and Social Research. Jeff Gill, 2006, Cambridge University Press. Knowledge of R is assumed.
     
  • Course Grade: The final grade will be based on three components: problem sets (40%), a replication assignment (30%), and an exam (30%) on MLE theory and basic models. The exam covers material from the first 7 weeks of the course plus the assigned readings (Faraway and articles). Consequently, we will discuss the readings in as much detail as the class desires. The problem sets will be a combination of analytical and computational assignments and given in each meeting. See Alicia Uribe's tips on success with the problem sets. For the replication assignment, find a published work in your field of interest, obtain the data, and exactly replicate the author's model results. It is usually easier to find an article that uses the readily available datasets in the discipline (COW, ANES, GSS, etc.), but some authors are forthcoming about distributing their data if asked. The relevant model should be one of the nonlinear forms studied in this course. Gary King has some useful tips and links to his PS paper on the subject here, and a recent success story (publication) is described by two of his students here. All submitted work must be from LaTeX source.
     
  • Office Hours: Friday 9-10.
     
  • Incompletes: None given
     
  • Teaching Assistant: Jonathan Homola, homola@wustl.edu. Office hours: TBD in Seigle 277.
     
  • Homework: assigned each day and due the following week at classtime. No late homework accepted. All homework must be LaTeX'd.
     
  • Required Text:
    Title: Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models.
    Author: Faraway.
    Publisher: Chapman & Hall/CRC.
    Edition: First.
    ISBN: 158488424.
    Also: Practical Regression and Anova using R
    by Faraway (from PS581) , and All of Statistics: A Concise Course in statistical Inference by Larry Wasserman. Springer, 2004, ISBN: 978-0387402727.
     
  • Optional Texts (these are for background; see me before making any purchases):

    Title: A Guide to Econometrics.
    Author: Kennedy.
    Publisher: MIT Press, 2003.
    Edition: Fifth or Sixth.
    ISBN: 0-262-61183-X.

    Title: Generalized Linear Models: A Unified Approach.
    Author: Gill.
    Publisher: Sage, 2001.
    Edition: First.
    ISBN: 0761920552.

    Title: Modern Applied Statistics with S.
    Author: Venables and Ripley
    Publisher: Springer-Verlag, 2003.
    Edition: Fourth.
    ISBN: 0387954570.

    Title: An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics.
    Author: R Development Core Team
    Available (free) online here
    Version 1.1, June 15, 2000

    Title: Linear Models with R.
    Author: Faraway.
    Chapman & Hall/CRC
    Edition: First.
    ISBN: 1-58488-425-8.

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