University of Washington, Spring 2015
|Class||Tues||4:30-7:20 pm||SAV 158|
|Lab||Fri||1:30-3:30 pm||SAV 117|
|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.
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 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.
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.
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.
- 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)
- 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
- Deck 1
- In-class R Markdown example analysis: Rmd source, html output
- Gelman and Hill, Ch 2
- Fox, Ch 2, 3 (review)
Friday, April 3
- Lab document: .Rmd, html
- Data: gapminder.csv
- Data Camp “Introduction to R”, https://www.datacamp.com/courses/free-introduction-to-r
- Wickham, Hadley. 2010. ``A Layered Grammar of Graphics.’’ Journal of Computational and Graphical Statistics 19(1): 3–28. http://dx.doi.org/10.1198/jcgs.2009.07098
Week 2: Assumptions & Properties of the Linear Regression Model, Part I
Tuesday, April 7
- Deck 2
- Matrix algebra readings. Read any of the following
- Fox, Ch. 5, 9.1–9.2
Friday, April 10
- Hadley Wickham, Introduction to dplyr
Week 3: Assumptions & Properties of the Linear Regression Model, Part II
Tuesday, April 14
- Deck 3
- Sampling Distribution of linear regression example
- Multiple regression coefficient anatomy
- Fox, Ch. 6, 9.3
Friday, April 17
Problem Set 1 Due
- Lab document: .Rmd, html
Week 4: Statistical Inference / Interpretation of the Linear Model
Tuesday, April 21
Friday, April 24
Week 5: Model Fitting and Data Transformation
Tuesday, Apr 28
Problem Set 2 Due
More on p-values and significance testing
- Nuzzo, Regina. 2014. ``Scientific Method: Statistical Errors.’’ Nature 506(7487): 150–52. http://www.nature.com/doifinder/10.1038/506150a
Omitted Variable Bias
- Omitted Variable Bias handout
- Fox, Ch. 6.3
- Agrist and Pischke, Ch. 3.2.2
Heteroskedasticity and misspecification
Mispecification, Transforming Covariates
- See Christopher Adolph’s slides from 503 last year http://faculty.washington.edu/cadolph/503/topic5.pw.pdf
- Fox, Ch. 4, Ch 17.1–17.3
- Gelman and Hill, Ch. 4.1–4.3, 4.5-4.6
- Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14(1): 63–82. http://pan.oxfordjournals.org/content/14/1/63
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.
- The perils of stargazing:
- Christopher Adolph, “Inference and Interpretation of Linear Regression”, POLS 503, Spring 2014. http://faculty.washington.edu/cadolph/503/topic4.pw.pdf#page=77, pg. 77–.
Transformations of Variables
- Lecture deck: .pdf
- Lecture deck: .pdf
- Fox. Ch. 6.4
- Fox Ch 13 (13.1, skim the rest)
Friday, May 8
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.
- Fox, Ch 21
Model Selection and Cross-Validation
- Fox, Ch 22
Friday, May 15
Week 8: Causal Inference I
Tuesday, May 19
Unusual and Influential Data and Robust Regression
- Worked Example Rmd, html
- Christopher Adolph, “Outliers and Robust Regression Techniques”, POLS 503, Spring 2014. http://faculty.washington.edu/cadolph/503/topic6.pw.pdf
- Fox, Ch 11, 19
Miscellaneous Thoughts on the state of quantitative analysis in political science. We did not talk about this, but these are good readings
- Schrodt, Philip A. 2014. ``Seven Deadly Sins of Contemporary Quantitative Political Analysis.’’ Journal of Peace Research 51(2): 287–300. http://jpr.sagepub.com/content/51/2/287
- Achen, Christopher H. 2002. ``Toward a New Political Methodology: Microfoundations and ART.’’ Annual Review of Political Science 5(1): 423–50. http://dx.doi.org/10.1146/annurev.polisci.5.112801.080943. Read the part on a “Rule of Three”; skim other parts.
- Achen, Christopher H. 2005. ``Let’s Put Garbage-Can Regressions and Garbage-Can Probits Where They Belong.’’ Conflict Management and Peace Science 22(4): 327–39. http://cmp.sagepub.com/content/22/4/327 skim
Friday, May 22
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
- 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
Week 10: Panel Data
Tuesday, June 2
- Slides on Panel data: pdf
- Example with R code: html, Rmd
- 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.