POLS/CS&SS 503: Advanced Quantitative Political Methodology

University of Washington, Spring 2015

Teaching Team

Professor Jeffrey Arnold jrnold@uw.edu
TAs Sergio García-Rios sigarcia@uw.edu
Carolina Johnson csjohns@uw.edu

Class Meetings

Class Tues 4:30-7:20 pm SAV 158
Lab Fri 1:30-3:30 pm SAV 117

Office Hours

Jeffrey Arnold Th 3:00–5:00 pm Smith 221B
Carolina Johnson Tu 3:30–4:30 pm SAV 119
Sergio García-Rios F 12:30–1:30 pm SAV 119

Overview and Class Goals

This course continues the graduate sequence in quantitative political methodology, focused particularly on fitting, interpreting, and refining the linear regression model.

Our agenda includes gaining familiarity with statistical programming via the popular R environment, developing clear and informative graphical representations of regression results, and understanding regression models in matrix form.

Prerequisites

It is desirable for students to have taken the introductory course in the sequence (Political Science 501), but any prior course on basic social statistics and linear regression should suffice.

Assessment and Evaluation

Problem Sets

Problem sets assigned weekly or bi-weekly. These problem sets will include programming problems with an emphasis on writing understandable, reproducible code.

The assignments and due dates will be distributed during the quarter.

Assignments will be both submitted digitally through Canvas at the due date and a paper copy to the TA at the next lab section.

Final Paper

A 15–20 page original report on an original quantitative analysis or replication-and-extension of a published article. The quantitative analysis should be conducted in R and reproducible. Students may work in pairs on the final paper with instructor permission.

The final paper is due on June 9, 2015 15:00 PDT.

For details, see the Guidelines for the Final Papers.

Email & Canvas:

The teaching team will send announcements regularly by email.

Any non-personal questions related to the material in the course should be posted as a Canvas discussion. Reserve email for personal or administrative matters. Before posting, check that the question has not been asked and answered already.

It is often more efficient to answer questions in person, so try to ask them attend office hours.

Resources

There are a couple places on campus that you can go to get additional statistical conulting

  • CSSCR has a drop-in statistical consulting center in Savery 119. They provide consulting on statistical software, e.g. R. http://csscr.washington.edu/consulting.html
  • CSSS Statistical Consulting provides general statistical consulting (questions about your research project). You can find their hours and locations on thier site.

Texts

Required

  • Fox, John. 2008. Applied Regression Analysis and Generalized Linear Models. 2nd edition. Los Angeles: SAGE Publications, Inc.
  • Gelman, Andrew, and Jennifer Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. 1st edition. Cambridge ; New York: Cambridge University Press.
  • Angrist, Joshua D., and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. 1st edition. Princeton: Princeton University Press. (UW library eBook available)

Optional

  • Zuur, Alain, Elena N. Ieno, and Erik Meesters. 2009. A Beginner’s Guide to R. Springer. (UW library eBook available)

Schedule and Topics

Warning: The uncertainty on this schedule is high. Given that this is my first time teaching this course, I expect many adjustments to be made.

Week 1: Introduction to 503 and R, Math Review

Tuesday, March 31

Friday, April 3

Week 2: Assumptions & Properties of the Linear Regression Model, Part I

Tuesday, April 7

  • Deck 2
  • Readings:
    • Matrix algebra readings. Read any of the following
      • Moore, Will H., and David A. Siegel. 2013. A Mathematics Course for Political and Social Research. 1st edition. Princeton, NJ: Princeton University Press, Chapter 12 (on Canvas).
      • Kevin Quinn’s matrix algebra handout
      • CSSS Math Camp Lectures Section 4
    • Fox, Ch. 5, 9.1–9.2

Friday, April 10

Week 3: Assumptions & Properties of the Linear Regression Model, Part II

Tuesday, April 14

Friday, April 17

Problem Set 1 Due

Week 4: Statistical Inference / Interpretation of the Linear Model

Tuesday, April 21

  • Deck 4
  • Using R with the Duncan occupational prestige data .Rmd, .html

  • Readings:

    • Fox, Ch. 6, 9.3

Friday, April 24

Week 5: Model Fitting and Data Transformation

Tuesday, Apr 28

Problem Set 2 Due

Friday, May 1

Week 6: Measurement Error, Transformations

Tuesday, May 5

We covered substantive vs. statistical influence; transformations; and measurement error. You will need to cover collinearity, unusual and influential data, and robust regression on your own.

Friday, May 8

Lab notes: .html, .Rmd

Week 7: Interpretation, Model Selection

Tuesday, May 12

Problem Set 3 Due

  • Deck on Model Specification and Fit
  • Life Expectancy Example: .html, .Rmd

  • Interpretation of regression

    • King, Gary, Michael Tomz, and Jason Wittenberg. 2000. ``Making the Most of Statistical Analyses: Improving Interpretation and Presentation." American Journal of Political Science 44(2): 347–61. http://www.jstor.org/stable/2669316.

  • Bootstrapping

    • Fox, Ch 21

  • Model Selection and Cross-Validation

    • Fox, Ch 22

Friday, May 15

Lab notes: .html, .Rmd

Week 8: Causal Inference I

Tuesday, May 19

  • Unusual and Influential Data and Robust Regression

  • Missing Data

    • Slides on Missing Data pdf
    • Worked Example Rmd, html

    • Readings:

      • King, Gary, James Honaker, Anne Joseph, and Kenneth Scheve. 2001. “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political Science Review 95: 49–69. Copy at http://j.mp/1zTTZUT

  • Miscellaneous Thoughts on the state of quantitative analysis in political science. We did not talk about this, but these are good readings

Friday, May 22

Lab document: html, Rmd

Week 9: Causal Inference II

Tuesday, May 26

Problem Set 4 Due

  • Limited Dependent Variables

    • Slides on the Linear Probability Model and Logit pdf

  • Potential outcomes framework, regression, matching

    • Slides on Casual Inference pdf

    • Readings

      • Gelman and Hill, Ch 9–10.
      • Angrist and Pischke, Ch 1–3.
      • Angrist, Joshua D., and Jorn-Steffen Pischke. 2010. ``The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics.’’ Journal of Economic Perspectives 24(2): 3–30. http://www.aeaweb.org/articles.php?doi=10.1257/jep.24.2.3 For background reading

Friday, May 29

Lab document: Rmd, html

Week 10: Panel Data

Tuesday, June 2

  • Slides on Panel data: pdf
  • Example with R code: html, Rmd

  • Readings

    • Angrist and Pischke, Ch. 5, 8.2
    • Peter Kennedy, A Guide to Econometrics, 6th edition, Chapter 18, “Panel Data”. On Canvas.
    • Wooldridge, Econometric Analysis of Cross Section and Panel Data, Chapter 10, “Basic Linear Unobserved Effects Panel Data Models”. On Canvas.
    • Beck, N., & Katz, J. N. (2011). Modeling Dynamics in Time-Series–Cross-Section Political Economy Data. Annual Review of Political Science, 14(1), 331–352. http://doi.org/10.1146/annurev-polisci-071510-103222

Friday, June 5

Open office hours to answer questions about your papers

Syllabus derived from Christopher Adolph. (Spring 2014). POLS/CSSS 503: Advanced Quantitative Political Methodology [Syllabus]. University of Washington. http://faculty.washington.edu/cadolph/503/503.pdf CC-BY-SA.