POLS/CS&SS 503 Schedule
Week 1
Class:
Class:
The Real Stats readings should be mostly review. Use them and the two Nature pieces to refresh yourself on regression. Scan the PS symposium to get a sense of where political science research methods are and are goings. Angrist and Pischke and Dunning provide background to the "causal inference revolution". In particular, understand the difference between "design based" and "model based" methods of inference. The Freedmand and Berk articles discuss the limitations, or rather, the proper uses of regression, and stats, more generally.
Readings before class:
Real Stats, Ch 1: "The Quest for Causality"
Real Stats, Ch 3: "Bivariate OLS: The Foundation of Statistical Analysis"
Altman and Krzywinski. 2015. "Points of Significance: Association, Correlation, and Causation". Nature Methods
Altman and Krzywinski. 2015. "Points of Significance: Simple linear regression." Nature Methods.
Symposium: Big Data, Causal Inference, and Formal Theory: Contradictory Trends in Political Science?. This is effectively a commentary on where methodology goes from here. Scan the various articles to get the gist of the different viewpoints.
Agrist and Pischke. 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics". Journal of Economic Perspectives.
Dunning. 2010. "Design-Based Inference: Beyond the Pitfalls of Regression Analysis?" in Rethinking Social Inquiry: Diverse Tools, Shared Standards
Freedman. 1991. "Statistical Models and Shoe Leather". Sociological Methodology.
Berk. 2010. "What You Can and Can’t Properly Do with Regression". Journal of Quantitative Criminology.
Optional readings after class:
King. 1991. "On Political Methodology" Political Analysis. A dated history of statistics in political science.
Sigelman. 2006. "The Coevolution of American Political Science and the American Political Science Review". APSR Becoming dated, but it touches on changes in methodology throughout the history of political science. This, along with King (1991) is to give you a big picture overview of the history of methodology in the discipline as some of the other papers provide a recent view of developments.
Lab:
Readings before class:
Software Carpentry. Automated Version Control and Using Git from RStudio.
Optional readings after class:
Bryan. 2017. Happy Git and GitHub for the useR
Software Carpentry. Version Control with Git. This is a good intro, but uses the command line
gitprogram rather than thegitinterface built into RStudio.Jones. 2013. "Git/GitHub, Transparency, and Legitimacy in Quantitative Research." Political Methodologist
Ram. 2013. "Git can facilitate greater reproducibility and increased transparency in science
Week 2
Class:
Readings before class:
Real Stats, Ch 4, "Hypothesis Testing and Interval Estimation: Answering Research Questions"
Optional readings after class:
Class:
Readings before class:
Acharya, Blackwell, and Sen. 2016. "The Political Legacy of American Slavery." Journal of Politics
Real Stats, "Ch 5: Multivariate OLS: Where the Action Is"
Real Stats, Ch 14, "Advanced OLS"
Lab:
Week 3
Class:
Readings before class:
Real Stats, Ch 6, "Dummy Variables: Smarter Than You Think"
Real Stats, Ch 7, "Transforming Variables, Comparing Variables"
Class:
Class:
Readings before class:
Nunn and Wantchekon. 2011. "The Slave Trade and the Origins of Mistrust in Africa" American Economic Review
Week 4
Class:
Class:
Lab:
Week 5
Class:
Readings before class:
Gross, J. H. 2014. ``Testing what matters (if you must test at all): a context-driven approach to substantive and statistical significance'' American Journal of Political Science
McCaskey, K. & Rainey, C. 2015. ``Substantive Importance and the Veil of Statistical Significance'' Statistics, Politics and Policy
Class:
Readings before class:
Class:
Week 6
Class:
Readings before class:
MM, Ch 1, "Randomized Trials"
Keele. 2015. "The Statistics of Causal Inference: A View from Political Methodology," Political Analysis.
Gelman and Imbens. "Why ask Why? Forward Causal Inference and Reverse Causal Questions," NBER
MM, Ch 1, "Randomized Trials"
Class:
Readings before class:
MM, Ch 2, "Regression"
MM, Ch 6.1, "Schooling, Experience, and Earnings"
MM, Ch 6.2, "Twins Double the Fun"
Optional readings after class:
King, Lucas, and Nielsen. (2016) "The Balance-Sample Size Frontier in Matching Methods for Causal Inference." American Journal of Political Science.
Imbens. (2015) "Matching Methods in Practice." Journal of Human Resources.
Sekhon. (2009) "Opiates for the Matches: Matching Methods for Causal Inference"
Class:
Week 7
Class:
Readings before class:
MM, Ch 3, "Instrumental Variables"
MM, Ch 6.3, "Econometricians are known by their ... Instruments"
A Readers’ Guide](https://dx.doi.org/10.1111/j.1540-5907.2010.00477.x)," AJPS.
EGAP. "10 Things to Know About the Local Average Treatment Effect"
Class:
Class:
Week 8
Class:
Class:
Class:
Week 9
Class:
Readings before class:
Real Stats, Ch 11
MM, Ch 4, "Regression Discontinuity Designs"
MM, Ch 6.4, "Rustling Sheepskin in the Lonestar State"
Optional readings after class:
Skovron and Rocio. 2015. A Practical Guide to Regression Discontinuity Designs in Political Science
Lee and Lemieux. 2010. "Regression Discontinuity Designs in Economics." Journal of Economic Literature
Class:
Class:
Week 10
Class:
Readings before class:
Real Stats, Ch 8.
MM, Ch 5, "Difference-in-Differences"
Real Stats, Ch 13
Real Stats, Ch 15
Optional readings after class:
Bertrand, Duflo, Mullainathan. 2004. "How Much Should We Trust Differences-In-Differences Estimates?" QJE
Cameron and Miller. "A Practitioner's Guide to Cluster-Robust Inference".
Class:
POLS/CS&SS 503: Advanced Quantitative Political Methodology, University of Washington, Spring 2017, by Jeffrey Arnold is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.