Methods of Political Analysis
Professor Burt Monroe
This seminar is about research design. In contrast to 502-504, which focus on the analysis of data you have, this seminar focuses on the prior concern of how to collect data worth analyzing. In 2008, Don Rubin coined a now ubiquitous phrase: “design trumps analysis.” In this course, we’re going to think about what this means, why this is, and what you can do to design your research to provide compelling support for your arguments. Topics include design in experimental and observational settings, sampling and selection, concepts and measurement, challenges of small-N to large-N to massive-N designs, and approaches to inference.
Tuesdays, 1:30 p.m. – 4:30 p.m.
112 Walker Building
Statistical Methods for Political Research
Professor Bruce Desmarais
This course provides an introduction to the principles of probability and mathematical statistics. Here you will learn the foundational principles of statistics that will be important for any type of quantitative analysis you will do in the future. This includes topics such as probability, distributions, estimation, hypothesis testing, cross-tabulations, and bivariate regression. The material taught in this class will be important for understanding later classes in the methods sequence on regression and other topics.
Thursdays, 9:00 a.m. – 12:00 p.m.
350 HHD Building
Topics in Political Methodology
Professor Christopher Zorn
This is an elective course in statistical methods designed to meet the particular needs of students in the political science Ph. D. curriculum. PL SC 504 is tailored to focus on the specific issues that arise in the types of data found in political science applications. Students are expected to have completed the three required foundational courses in political methodology or their equivalents. This course examines a range of regression-like models widely used in empirical political science. Its core focus is on maximum likelihood estimation of models for various kinds of limited-dependent and qualitative response variables. Specific models covered are widely used in political science today, including binary logit and probit, multinomial logit and probit, ordered logit and probit, and Poisson regression models. Additional topics include models for time-to-event (survival) data, panel data and time-series cross-sectional analysis, item response theory, multi-level models, and methods for causal inference using observational data. Students will apply these models in a series of homework assignments and a replication project. Empirical political scientists must have familiarity with these models; these techniques represent a minimal level of statistical competence necessary for those seeking to do advanced quantitative analysis in the political science. The material in this course is technical, but students will be given an intuitive rationale for each model. Weekly homework assignments will be based on data from published research in political science.
Thursdays, 9:00 a.m. – 12:00 p.m.
232 HHD Building
Writing and Professional Development in Political Science
Professor Peter Hatemi
Professional development focusing on the publishing research, writing dissertations, and professional issues of advanced graduate students. This course is designed to help advanced graduate students surmount the challenges they face as they turn to writing a dissertation and prepare to become junior faculty. The course is designed to give practical advice on many of the issues faced by these students. Primary among these is learning to turn to initial papers into research publishable in high quality peer reviewed journals. The course also focuses on practical advise on finishing comprehensive exams, starting a dissertation and early preparation for the job market.
Note: You should enroll in this course if you are entering your second year in the program. This course is the second of two required, 1.5 credit professional development courses in the graduate program in political science.
Mondays, 1:00 p.m. – 2:30 p.m.
205N Millennium Science Complex
Survey Methods II: Analysis of Survey Data
Professor Eric Plutzer
Cross-listed with Sociology
Data collected by surveys have a combination of qualities that represent challenges to valid inference. These include cluster and stratified sampling, under-representation of some groups due to differential response rates, missing data due to item non-response, and coarse measurement (3-4 categories to capture rich concepts such as religious faith or economic status). We often use surveys to test theories that the original survey designer did not intend to address, raising issues of validity and reliability of measurement. At the same time, surveys offer a number of opportunities and, when combined with other surveys (pooled cross sections) or merged with contextual data, can address a wide range of theoretical puzzles in the social sciences. This course provides an introduction to techniques in applied statistics that have been developed specifically to address the special features of survey data: use of design weights, post-stratification weights, accounting for clustering and other features of the research design in analysis, merging surveys with other surveys or auxiliary data, and missing data imputation. The class will emphasize the intuition of the theory underlying the statistical models rather than focusing on proofs and estimation. This will provide a foundation for frequent hands-on applications in this seminar and for subsequent enrollment in more advanced courses offered by the Statistics department and the various social science departments.
Mondays & Wednesdays, 6:00 p.m. – 7:30 p.m.
158 Willard Building
American Political Proseminar: American Government and Politics
Professor Michael Nelson
This course introduces graduate students to the core concepts and controversies in the study of American politics. We will discuss the evolution of research on American political institutions and behavior through discussions of both current and classic readings. We will consider both how these readings contribute to our knowledge of politics in the United States and how researchers designed and executed their studies.
This course has three central aims: to help students find feasible research questions that they can investigate throughout their graduate careers, to begin to prepare students for the field examination in American politics, and to ready students for more advanced seminars in American political institutions and behavior.
Tuesdays, 9:00 a.m. – 12:00 p.m.
107 Wartik Lab
Introduction to Comparative Politics
Professor Matthew Golder
This course is the core seminar for the field of comparative politics in the political science Ph.D. program. It provides an introduction to the dominant questions, theories, and empirical research in comparative politics. The substantive topics covered in the class include democracy and dictatorship, democratic performance, political institutions, culture and identity issues, elections and political parties, representation and accountability, and political economy. The course has two goals: (i) to prepare students for a research career in comparative politics by providing a general survey of the field and (ii) to help prepare graduate students for the comprehensive examination in comparative politics.
Wednesdays, 9:00 a.m. – 12:00 p.m.
105 HHD Building
Text as Data
Professor Burt Monroe
This course investigates the use of digitized texts — news articles, speeches, laws, treaties, press releases, party manifestos, campaign ads, interviews, transcripts, open-ended surveys, Tweets, Reddit posts, Youtube comments, Yelp reviews … — as sources of data for social science research. Topics will include gathering text data (e.g., web scraping, pdf scraping), preprocessing text and related NLP tasks (e.g., stemming, tokenizing), representing text as data (e.g., bag-of-words, word embeddings, measures of association), technical practicalities in using text data (e.g., encodings, sparsity), ethical and legal practicalities in using text data (e.g., robots.txt, terms of service, copyright), linguistic and substantive issues in text data (e.g., hand coding of training data, short vs. long texts, transcribed vs. thumb-typed vs. copy-edited texts, multilingual texts), inferential approaches to text as data (e.g., latent measurement modeling, decomposition, supervised learning, deep learning), measurement tasks with text (e.g., classification, scaling, topic modeling, sentiment analysis), and the social scientific application of modern neural / deep learning approaches to NLP. Across topics there will be a heavy emphasis on social scientific objectives like measurement, validation, and inference. (The course will assume students have a base facility with Python, R, or similar, and some graduate level work in statistical inference, quantitative social science methodology, or machine learning.)
Thursdays, 1:30 p.m. – 4:30 p.m.
205N Millennium Science Complex
Social Media and Politics
Professor Kevin Munger
This seminar covers recent and classic empirical research on the relationship between “the media” (broadly understood) and politics. The modern study of mass media influence originated in the 1940s and spans several social science disciplines. As we will see, the paradigms developed in the early years of that research program continue to influence scholars today–as well as to be debated and critiqued. Some of the canonical questions we will explore include the power of media messages to persuade; the extent to which media outlets are ideologically slanted, and how to objectively evaluate claims of bias; how censorship and propaganda work; and the role of new information technologies and social media on societal pathologies such as mass polarization. It would be impossible to adequately cover all aspects of media research even in a comprehensive survey course. As such, this seminar will focus on relatively recent work that is quantitative in nature (although not exclusively so), but we will also strive to remain grounded in foundational works.
Wednesdays, 2:00 p.m. – 5:00 p.m.
101 Osmond Building
Professor Cyanne Loyle
States and their governments protect our human rights and paradoxically are the main source of the violation of those rights. This course will review and advance the existing literature on the complex and often violent relationship between state power and challenges to that power with a specific focus on when and how states choose to violate individual human rights. We will focus on the theoretical and methodological study of state repression. Topics will include non-coercive forms of repression, protest policing, civilian targeting in insurgency and other forms of rebellion, and genocide.
Mondays, 9:00 a.m. – 12:00 p.m.
117 Thomas Building
Approaches and Issues in Social Data Analytics
Professor Charles Seguin
Addresses the interdisciplinary integration of computational, informational, statistical, visual analytic, and social scientific approaches to learning from data that are both “social” (about, or arising from, human interactions) and “big” (of sufficient scale, variety, or complexity to strain the informational, computational, or cognitive limits of conventional social scientific approaches to data collection or analysis). Includes alternative scientific models for learning from data (Bayesian inference, causal inference, statistical / machine learning, visual analytics, measurement modeling), analytics issues with big data (variable selection, parallel computing, algorithmic scaling, ensemble modeling, validation), analytics issues with particular structures and channels of social data (network data, geospatial data, intensive longitudinal data, text data), and issues of scientific responsibility and ethics in analysis of big social data.
Tuesdays, 2:30 p.m. – 5:30 p.m.
115 Osmond Building