17.806: Quantitative Research Methods IV

Spring 2013

Instructors: Danny Hidalgo & Teppei Yamamoto TA: Chad Hazlett Department of Political Science MIT

1 Contact Information Danny Teppei Chad Oﬃce: E53–402 E53–401 E40–4446 Phone: 617–253–8078 617–253–6959 412–760–7544 Email: [email protected] [email protected] [email protected] URL: http://web.mit.edu/teppei/www

2 Logistics

• Lectures: Mondays and Wednesdays 3:30 – 5:00pm, E53–438

• Recitations: TBA

• Danny’s oﬃce hours: Make an appointment.

• Teppei’s oﬃce hours: Make an appointment

• Chad’s oﬃce hours: Mondays 1:30 – 3:00, E40-446

3 Course Description

This course is the fourth and ﬁnal course in the quantitative methods sequence at the MIT political science department. The course covers various advanced topics in applied statistics, including those that have only recently been developed in the methodological literature and are yet to be widely applied in political science. The topics for this year are organized into three broad areas: (1) Advanced causal inference, where we build on the basic materials covered earlier in the sequence (17.802) and study more advanced topics; (2) statistical learning, where we provide an overview of machine learning, one of the most active subﬁelds in applied statistics in the past decade; and (3) Bayesian inference and statistical computing, where we extend the model-based inference techniques covered in the previous course of the sequence (17.804) and study more technically sophisticated materials as well as applications in political science.

1 4 Prerequisites

There are three prerequisites for this course:

1. Mathematics: basic calculus and linear algebra.

2. Probability and statistics covered in 17.800, 17.802, 17.804.

3. Statistical computing: familiarity with at least one statistical software.

For 1 and 3, refer to this year’s math camp materials to see the minimum you need to know; see

https://stellar.mit.edu/S/project/mathprefresher/

5 Course Requirements

The ﬁnal grades are based on the following items:

• Problem sets (30%): A total of four problem sets will be given throughout the semester. Problem sets will contain analytical, computational, and data analysis questions. Each problem set will be counted equally toward the calculation of the ﬁnal grade. The following instructions will apply to all problem sets unless otherwise noted.

– Neither late submission nor electronic submission will be accepted, except for special circumstances which must be discussed with us prior to the deadlines. – Working in groups is encouraged, but each student must submit their own writeup of the solutions. In particular, you should not copy someone else’s answers or computer code. We also ask you to write down the names of the other students with whom you solved the problems together on the ﬁrst sheet of your solutions. – For analytical questions, you should include your intermediate steps, as well as comments on those steps when appropriate. For data analysis questions, include annotated code as part of your answers. All results should be presented so that they can be easily understood.

• Final project (50%): The ﬁnal project will be a short research paper which typically applies a method learned in this course to an empirical problem of your substantive interest. We strongly encourage you to co-author a paper with another student in the class. By co-authoring you will (1) learn how to eﬀectively collaborate with someone else on your research, which is very important in political science where most cutting-edge research is collaborative (see any recent issue of APSR or AJPS!) and (2) more likely have a good, potentially publishable paper (multiple brains are usually better than one). Unless you already have a concrete research project suitable for this course (e.g., from your dissertation project), we recommend that you start with replicating the results in a published article and then improve the original analysis using the methods learned in this course (or elsewhere), methodologically or substantively. Oftentimes, gathering an original dataset is too time-consuming and not suitable for a course project. Students are expected to adhere to the following deadlines:

2 – March 11: Turn in a one-page description of your proposed project. By this date you need to have found your coauthor, acquired the data you plan to use, and completed a descriptive analysis of the data (e.g. simple summary statistics, crosstabs and plots). After submission, schedule a meeting with us to discuss your proposal. – April 17 and 22: Students will present interim reports on their projects in front of the class. Each presentation should last about 10 minutes and will be followed by a short Q&A session. Students should prepare electronic slides to accompany their presentation. Performance on this presentation will be counted towards your total final grade (see below). – May 15: Paper due. Please hand in one printed copy of your paper by 5pm, and also email electronic copies to us by then. Your final paper should have all the proper format of an academic journal article (except extensive literature review and theory sections), including a title, abstract, introductory and concluding sections, tables and/or figures with appropriate captions, and references with a coherent citation style. You will be penalized if any of these elements is missing from your submitted manuscript. You should use this project as an exercise to write a good scientific paper. We recommend that you closely follow the advice given in this article: King, Gary. 2006. “Publication, Publication.” PS: Political Science and Politics, 39(1): 119–125.

• Participation in Applied Paper Sessions (10%): Throughout the semester, we will have four applied paper sessions, in which we discuss journal articles and working papers which apply the methods covered in the lectures to empirical problems in political science and related ﬁelds. For each paper (or set of papers in some cases) marked as “required,” one student will deliver a 15-minute oral report which will walk the rest of the class through its content and provide comments on its merits and weaknesses, followed by a class discussion. The other students must read the required papers in advance and submit short written comments on each of the papers by the previous day. These comments should be no longer than a few sentences for each paper, optionally followed by a list of questions for class discussion. Although the written comments will not be graded, your participation in the class discussion will count towards the participation grade.

• Midterm Project Presentation (10%)

In addition, there will be required readings for each lecture which students must complete in advance in order to enhance their understanding. The lectures and applied paper sessions will also have optional readings, which are listed in the course schedule below.

6 Course Website

You can ﬁnd the Stellar website for this course at:

http://stellar.mit.edu/S/course/17/sp13/17.806/

We will distribute course materials, including readings, lecture slides and problem sets, on this website.

3 7 Questions about Course Materials

In this course, we will utilize an online discussion board called Piazza. Below is an official blurb from the Piazza team: Piazza is a question-and-answer platform specifically designed to get you answers fast. They support LaTeX, code formatting, embedding of images, and attaching of files. The quicker you begin asking questions on Piazza (rather than via individual emails to a classmate or one of us), the quicker you’ll benefit from the collective knowledge of your classmates and instructors. We encourage you to ask questions when you’re struggling to understand a concept ... See this New York Times article to learn more about their founder’s story: http://www.nytimes.com/2011/07/04/technology/04piazza.html In addition to recitation sessions and office hours, please use the Piazza Q & A board when asking questions about lectures, problem sets, and other course materials. You can access the Piazza course page either directly from the below address or the link posted on the Stellar course website: https://piazza.com/mit/spring2013/17806 Using Piazza will allow students to see other students’ questions and learn from them. Both the TA and the instructor will regularly check the board and answer questions posted, although everyone else is also encouraged to contribute to the discussion. A student’s respectful and constructive participation on the forum will count toward his/her class participation grade. Do not email your questions directly to the instructors or TAs (unless they are of personal nature) — we will not answer them!

8 Recitation Sessions

Recitation sections will be held during the two weeks each problem set is available. Time and location will be announced after the ﬁrst week of class. The purpose of these sessions will be to clarify theoretical material and assist with computing issues, particularly as needed to complete each problem set. Attendance is strongly encouraged.

9 Notes on Computing

In this course we use R, an open-source statistical computing environment that is very widely used in statistics and political science. (If you are already well versed in another statistical software, you are free to use it, but you will be on your own.) Problem sets will contain computing and/or data analysis exercises which can be solved with R but often require going beyond canned functions and writing your own program. In addition to the materials from the department’s math prefresher (see above), there are many resources for R targeted at both introductory and advanced levels, including: • Fox, John and Sanford Weisberg. 2010. An R Companion to Applied Regression. Sage Publications. (focused on regression analysis) • Venables, W. N. and B. D. Ripley. 2002. Modern Applied Statistics with S, 4th ed. Springer. (general statistics) • For speciﬁc questions about R, searching the CRAN website with appropriate keywords will often yield satisfactory results.

4 10 Books

The course has no required or recommended textbooks. All the reading materials are listed in the course schedule below and will be made available electronically.

11 Course Schedule

Part I: Advanced Causal Inference 1. Complex Experiments (2/11) Required:

• Imai, Kosuke, Gary King, and Clayton Nall. 2009. “The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation.” Statistical Science 24(1): 2953. • Small, Dylan S, Thomas R Ten Have, and Paul R Rosenbaum. 2008. “Randomization Inference in a Group-Randomized Trial of Treatments for Depression.” Journal of the American Statistical Association 103(481): 27179.

Optional:

• Kalton, Graham, J. Michael Brick, and Thahn Le. 2005. “Estimating Components of Design Eﬀects for Use in Sample Design.” In Household Sample Surveys in Developing and Transition Countries, New York: United Nations. • Bloom, H. 2008. “The Core Analytics of Randomized Experiments for Social Research.” In The SAGE Handbook of Social Research Methods, eds. Pertti Alasuutar, Leonard Bickman, and Julia Brannen. London: SAGE. • Bruhn, Miriam, and David McKenzie. 2009. “In Pursuit of Balance: Randomization in Practice in Development Field Experiments.” American Economic Journal: Applied Economics 1(4): 200232.

2. Causal Inference with Interference between Units (2/13) Required:

• Bowers, Jake, Mark M Fredrickson, and Costas Panagopoulos. Forthcoming. “Reason- ing About Interference Between Units: a General Framework.” Political Analysis. • Hudgens, Michael G, and M Elizabeth Halloran. 2008. “Toward Causal Inference with Interference.” Journal of the American Statistical Association 103(482): 83242.

Optional:

• Tchetgen, Eric, and Tyler VanderWeele. 2012. “On Causal Inference in the Presence of Interference.” Statistical Methods in Medical Research 21(1): 5575. • Aronow, Peter, and Cyrus Samii. 2012. “Estimating Average Causal Eﬀects Under General Interference.” Working Paper. • Hong, Guanglei, and Stephen W Raudenbush. 2006. “Evaluating Kindergarten Re- tention Policy: A Case Study of Causal Inference for Multilevel Observational Data”, Journal of the American Statistical Association 101(475): 90110.

5 3. Multiple Comparisons (2/19) Required:

• Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society. Series B (Methodological) 57(1): 289300.

Optional:

• Romano, Joseph P, and Michael Wolf. 2005. “Stepwise Multiple Testing as Formalized Data Snooping.” Econometrica 73(4): 123782. • Schochet, Peter Z. 2008. “Guidelines for Multiple Testing in Impact Evaluations”. National Center for Education Evaluation and Regional Assistance. • Dudoit, Sandrine, Mark J Van Der Laan, and Katherine Pollard. 2004. “Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates.” Statistical Applications in Genetics and Molecular Biology 3(1). • Caughey, Devin, Allan Dafoe, and Jason Seawright. 2012. “Testing Elaborate Theories in Political Science: Nonparametric Combination of Dependent Tests”. Working Paper. • Humphreys, Macartan, Raul Sanchez de la Sierra, and Peter van der Windt. 2013. “Fishing, Commitment, and Communication: a Proposal for Comprehensive Nonbinding Research Registration.” Political Analysis 21(1): 120.

4. Applied paper session (2/20) Required:

• Sinclair, Betsy, Margaret McConnell, and Donald Green. 2012. “Detecting Spillover Eﬀects: Design and Analysis of Multilevel Experiments.” American Journal of Political Science 56(4): 105569. • Casey, Katherine, Rachel Glennerster, and Edward Miguel. 2012. “Reshaping Institu- tions: Evidence on Aid Impacts Using a Preanalysis Plan.” The Quarterly Journal of Economics 127(4): 17551812. • (Optional) Centola, Damon. 2010. “The Spread of Behavior in an Online Social Network Experiment.” Science 329(5996): 119497.

5. Causal Diagrams (2/25) Required:

• Pearl, Judea. 2010. “The Foundations of Causal Inference.”Sociological Methodology, 40(1): 75–149.

Optional:

• Pearl, Judea. 1995. “Causal Diagrams for Empirical Research” (with discussions). Biometrika, 82(4):669–710. • Pearl, Judea. 2009. Causality, 2nd ed. Cambridge University Press.

6. Partial Identiﬁcation (2/27) Required:

6 • Chapter 7 in Manski, Charles F. 2007. Identification for Prediction and Decision, Har- vard University Press. Optional: • The rest of Manski, 2007. • Balke, Alexander and Judea Pearl. 1997. “Bounds on Treatment Effects from Studies with Imperfect Compliance.” Journal of the Americal Statistical Association, 92(439): 1171–1176. • Imai, Kosuke. 2008. “Sharp Bounds on the Causal Effects in Randomized Experiments with ‘Truncation-by-death.” Statistics and Probability Letters, 78, 144–149. • Imai, Kosuke and Teppei Yamamoto. 2010. “Causal Inference with Differential Measure- ment Error: Nonparametric Identification and Sensitivity Analysis.” American Journal of Political Science, 54(2): 765–789. 7. Causal Mediation (3/4) Required: • Imai, Kosuke, Luke Keele, Dustin Tingley and Teppei Yamamoto. 2011. “Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies.”American Political Science Review, 105(4):765–789. Optional: • Imai, Kosuke, Luke Keele and Teppei Yamamoto. 2010. “Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects.” Statistical Science, 25(1): 51–71. • Pearl, Judea. 2001. “Direct and Indirect Effects.” In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (J. S. Breese and D. Koller, eds.), 411–420. • Robins, James M. and Sander Greenland. 1992. “Identifiability and Exchangeability for Direct and Indirect Effects.” Epidemiology, 3: 143–155. 8. Causal Attribution (3/6) Required: • Yamamoto, Teppei. 2012. “Understanding the Past: Statistical Analysis of Causal Attribution.” American Journal of Political Science, 56(1): 237–256. Optional: • Tian, Jin and Judea Pearl. 2000. “Probabilities of Causation: Bounds and Identifica- tion.” Annals of Mathematics and Artificial Intelligence. 28(1–4): 287–313. 9. Experimental Approaches for Measurement (3/11) Required • Payne, B. K., Cheng, C. M., Govorun, O., & Stewart, B. D. (2005). “An Inkblot for Attitudes: Affect Misattribution as Implicit Measurement”. Journal of Personality and Social Psychology, 89(3), 277. • Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). “Measuring Individual Differences in Implicit Cognition: the Implicit Association Test.” Journal of Personality and Social Psychology, 74(6), 1464.

7 • Blair, G., Imai, K., & Lyall, J. (2012). “Comparing and Combining List and Endorse- ment Experiments: Evidence from Afghanistan.” Working Paper.

Optional:

• Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). “The Implicit Association Test at Age 7: A Methodological and Conceptual Review.” Automatic processes in social thinking and behavior, 265-292. • Greenwald, A. G., Smith, C. T., Sriram, N., Bar-Anan, Y., & Nosek, B. A. (2009). “Implicit Race Attitudes Predicted Vote in the 2008 US Presidential Election.” Analyses of Social Issues and Public Policy, 9(1), 241-253.

10. Applied paper session (3/13) Required: Controversy on Suicide Terrorism

• Pape, Robert A. 2003. “The Strategic Logic of Suicide Terrorism.” American Political Science Review. • Ashworth, Scott, Joshua D. Clinton, Adam Meirowitz, and Kristopher W. Ramsay. 2008. “Design, Inference, and the Strategic Logic of Suicide Terrorism.” American Political Science Review, 102(2): 269–273. • Pape, Robert A. 2008. “Methods and Findings in the Study of Suicide Terrorism.” American Political Science Review, 102(2): 275–277.

Required: Voter Registration and Turnout

• Hanmer, Michael J. 2007. “An Alternative Approach to Estimating Who is Most Likely to Respond to Changes in Registration Laws.” Political Behavior, 29: 1–30. • Glynn, Adam N. and Kevin M. Quinn. 2011. “Why Process Matters for Causal Infer- ence.” Political Analysis, 19: 273–286.

Optional: Ecological Inference

• Duncan, O. and B. Davis. 1953. “An Alternative to Ecological Correlation.” American Sociological Review, 18: 665–666. • Cho, Wendy K. Tam and Charles F. Manski. 2008.“Cross-Level/Ecological Inference.” In Oxford Handbook of Political Methodology, Ch. 22. • Cross, Philip J. and Charles F. Manski. 2002. “Regressions, Short and Long.” Econo- metrica, 70(1): 357–368. (Technical; see the working paper version for an empirical application)

Optional: Causal Mediation Analysis Applications

• Becher, Michael and Michael Donnelly. 2012. “Economic Performance, Individual Eval- uations and the Vote: Investigating the Causal Mechanism.” Working Paper. Princeton University. • Tingley, Dustin and Michael Tomz. 2012. “How Does the UN Security Council Inﬂuence Public Opinion?” Working Paper. Harvard University.

8 Part II: Statistical Learning 1. Introduction to Learning and Regularization (3/18) Required:

• Chapter 2 and 3 in Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer.

Optional:

• Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. Springer. • Berk, Richard A. 2008. Statistical Learning From a Regression Perspective. Springer.

2. Model assessment and selection, Classiﬁcation Trees (3/20)

• Chapter 7 in Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Ele- ments of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer. • Pg. 305 - 313 in Elements of Statistical Learning.

Optional:

• King, Gary, and Langche Zeng. 2001. “Improving Forecasts of State Failure.” World Politics 53(4): 62358. • Ulfelder, Jay. 2012. “Forecasting Political Instability: Results From a Tournament of Methods”. SSRN Working Paper.

3. Learning Algorithms (4/1) Required:

• Hainmueller, Jens, and Chad Hazlett. “Kernel Regularized Least Squares: Moving Beyond Linearity and Additivity Without Sacriﬁcing Interpretability”. Under Review. • Tommi Jaakkola. “Lecture 7”. Course materials for 6.867 Machine Learning, Fall 2006. MIT OpenCourseWare(http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded July 2012.

Optional:

• Sections 6.4-6.4.2 in Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. Springer. • Yaser Abu-Mostafa, “Lecture 14: Support Vector Machines”. May 2012 lecture for Learning From Data at Caltech. Available at http://www.youtube.com/watch?v=eHsErlPJWUU. • Yaser Abu-Mostafa, “Lecture 15: Kernel Methods”. May 2012 lecture for Learning From Data at Caltech. Available at http://www.youtube.com/watch?v=XUj5JbQihlU. • Two tougher but very good background pieces on SVM, kernels, etc. – Burges, C. J. (1998). “A Tutorial on Support Vector Machines for Pattern Recog- nition”. Data Mining and Knowledge Discovery, 2(2), 121-167. – Jakel, F., Scholkopf, B., & Wichmann, F. A. (2007). “A Tutorial on Kernel Methods for Categorization”. Journal of Mathematical Psychology, 51(6), 343-358.

9 4. Ensemble Learning (4/3) Required:

• Hillard, Dustin, Stephen Purpura, and John Wilkerson. 2008. “Computer-Assisted Topic Classiﬁcation for Mixed-Methods Social Science Research.” Journal of Informa- tion Technology & Politics 4(4): 3146. • Polley, Eric C, and Mark J Van Der Laan. 2010. “Super Learner in Prediction”. Working Paper. University of California, Berkeley.

Optional:

• Montgomery, J., F. Hollenbach, and M. Ward. 2012. “Improving Predictions Using Ensemble Bayesian Model Averaging.” Political Analysis 20(3): 27191. • Van Der Laan, Mark J, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. 1st ed. Springer.

5. Applied paper session (4/8) Required:

• Diermeier, Daniel, Jean-Franois Godbout, Bei Yu, and Stefan Kaufmann. 2011. “Lan- guage and Ideology in Congress.” British Journal of Political Science 42(01): 3155. • Stewart, Brandon M, and Yuri M Zhukov. 2009. “Use of Force and Civil-Military Relations in Russia: an Automated Content Analysis.” Small Wars & Insurgencies 20(2): 31943. • Blair, Robert, Christopher Blattman, and Alexandra Hartman. 2012. “Predicting Local-Level Violence”. Working Paper.

6. Unsupervised Learning: Guest lecture by Luke Miratrix (4/10)

Midterm Project Presentations (4/17, 4/22) Part III: Bayesian Inference and Statistical Computing 1. Advanced Simulation Algorithms (4/24) Required:

• Jackman, Simon. 2000. “Estimation and Inference via Bayesian Simulation: An Intro- duction to Markov Chain Monte Carlo.” American Journal of Political Science, 44(2): 375–404. • Chapter 12.3 in Gelman, Andrew, John B. Carlin, Hal S. Stern and Donald B. Rubin. 2004. Bayesian Data Analysis, 2nd ed. Chapman & Hall/CRC.

Optional:

• Casella, George and Edward I. George, 1992, “Explaining the Gibbs Sampler,” The American Statistician, 46(3), 167–174. • Chib, Siddhartha and Edward Greenberg, 1995, “Understanding the Metropolis-Hastings Algorithm,” The American Statistician, 49(4), 327–335. • Tanner, Martin A. and W. H. Wong. 1987. “The Calculation of Posterior Distribu- tions by Data Augmentation” (with discussion). Journal of the American Statistical Association, 82: 528–550.

10 • Wei, Greg C. G. and Martin A. Tanner. 1990. “A Monte Carlo Implementation of the EM Algorithm and the Poor Man’s Data Augmentation Algorithms.” Journal of the American Statistical Association, 85(411): 699–704. • Liu, Jun S. 2004. Monte Carlo Strategies in Scientiﬁc Computing. Springer.

2. Discrete Choice Analysis (4/29) Required:

• Glasgow, Garrett. 2001. “Mixed Logit Models for Multiparty Elections.” Political Analysis, 9(1): 116–136. • Train, Kenneth E. 2001. “A Comparison of Hierarchical Bayes and Maximum Simulated Likelihood for Mixed Logit.” Working Paper. University of California, Berkeley.

Optional:

• Albert, J. and S. Chib. 1993. “Bayesian Analysis of Binary and Polychotomous Data.” Journal of the American Statistical Association, 88, 669–679. • Allenby, J. and Peter Rossi. 1999. “Marketing Models of Consumer Heterogeneity.” Journal of Econometrics, 89(1–2): 57–78. • Train, Kenneth E. 2009. Discrete Choice Methods with Simulation, 2nd ed. Cambridge University Press. • Yamamoto, Teppei. 2010. “A Multinomial Response Model for Varying Choice Sets, with Application to Partially Contested Multiparty Elections.” Working Paper. Mas- sachusetts Institute of Technology.

3. Bayesian Measurement Techniques (5/1) Required:

• Chapter 9 in Jackman, Simon. 2009. Bayesian Analysis for the Social Sciences. Wiley.

Optional:

• Bock, R. D. and M. Aitken. 1981. “Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm.” Psychometrika, 46: 443–459. • Bafumi, Joseph, Andrew Gelman, David K. Park and Noah Kaplan. 2005. “Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation.” Political Analysis, 13: 171–187.

4. Applied paper session (5/6) Required: EM Algorithm Application

• Slapin, Jonathan B. and Sven-Oliver Proksch. 2008. “A Scaling Model for Estimating Time-Series Party Positions from Texts.” American Journal of Political Science, 52(3): 705–722.

Required: Ideal Point Estimation

• Bonica, Adam. 2012. “Ideology and Interests in the Political Marketplace.” American Journal of Political Science, forthcoming.

Optional: Measuring Democracy

11 • Treier, Shawn and Simon Jackman. 2008. “Democracy as a Latent Variable.” American Journal of Political Science, 52(1): 201–217. • Pemstein, Daniel, Stephen A. Meserve and James Melton. 2010. “Democratic Compro- mise: A Latent Variable Analysis of Ten Measures of Regime Type.” Political Analysis, 18(4): 426–449.

Optional: More Ideal Point Estimation

• Martin, Andrew D. and Kevin M. Quinn. 2002. “Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999.” Political Analysis, 10: 134–153. • Clinton, Joshua, Simon Jackman and Douglas Rivers. 2004. “The Statistical Analysis of Roll Call Data.” American Political Science Review, 98(2): 355–370. • Lauderdale, Benjamin E. 2010. “Unpredictable Voters in Ideal Point Estimation.” Po- litical Analysis, 18: 151–171.

Optional: Bayesian Approaches to Ecological Inference

• Greiner, D. James and Kevin M. Quinn. 2009. “R × C Ecological Inference: Bounds, Correlations, Flexibility and Transparency of Assumptions.” Journal of the Royal Sta- tistical Society, Series A, 172(1): 76–81. • Imai, Kosuke, Ying Lu and Aaron Strauss. 2008. “Bayesian and Likelihood Inference for 2 × 2 Ecological Tables: An Incomplete-Data Approach.” Political Analysis, 16: 41–69.

5. Multi-level Regression and Post-Stratiﬁcation: Guest lecture by Chris Warshaw (5/8)

6. Missing Data: Guest lecture by James Honaker (5/13)

Bonus Sessions 1. Web Scraping (5/15)