PL SC 501
Methods of Political Analysis
Professor Allison Harris
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.
Mondays & Wednesdays, 5:00 p.m. – 6:30 p.m.
301 Boucke Building
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PL SC 502
Statistical Methods for Political Research
Professor Betul Demirkaya
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.
Tuesdays & Thursdays, 1:00 p.m. – 2:30 p.m.
236 Pond Lab
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PL SC 504
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, a replication project, and a final exam. 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.
Tuesdays & Thursdays, 9:30 a.m. – 11:00 a.m.
305 Boucke Building
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PL SC 513
Writing and Professional Development in Political Science
Professor Michael Nelson
This course is designed to help graduate students make the transition from coursework to writing a master’s essay and dissertation, and engaging in the many activities that constitute life as a professional political scientist. To this end, we will discuss strategies for undertaking research for and writing a master’s essay; preparing manuscripts for publication and responding to reviews; writing manuscript reviews; writing a dissertation prospectus and dissertation; and writing grant proposals. We will also focus on making effective presentations of your research; identifying strategies for locating sources of funds for your projects; and preparing for and navigating the job market. Students will devote several weeks of the semester to working on their master’s essays with the aim of making considerable progress on the essay by the end of the fall semester. All members of the seminar will read and provide constructive comments on one another’s work. Students are expected to attend each and every session, participate in seminar discussions, and complete weekly assignments. Grading for the course will be pass/fail.
Note: You should enroll in this course if you are entering your second year in the program. This is a required, 1.5 credit course.
Tuesdays, 1:35 p.m. – 2:50 p.m.
217 Thomas Building
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PL SC 519
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.
Tuesdays & Thursdays, 8:00 a.m. – 9:15 a.m.
158 Willard Building
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PL SC 541
American Political Institutions: State Politics
Professor Michael Nelson
The fifty state governments are often referred to as “laboratories of democracy.” On the one hand, this label refers to the role that states play in the policymaking process by experimenting with policies across time and space. In this course, we will examine how policies are developed and implemented, how they diffuse across state lines, and how the federal government encourages (and discourages) this process of policy experimentation. But states are also laboratories for scholars. As we review the literature on state political institutions and behavior, we will pay particular attention to how the states can be used by scholars to test general questions about political institutions, mass behavior, and representation.
Tuesdays, 3:20 p.m. – 6:20 p.m.
236 Pond Lab
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PL SC 550
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, civil war, 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.
Mondays, 1:00 p.m. – 4:00 p.m.
236 Pond Lab
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PL SC 560
International Relations: Theory and Methodology
Professor Zaryab Iqbal
This course is the field seminar in international relations, aimed at providing an introduction to major theories of international relations and exposing students to contemporary research in the field. In this seminar, you will learn to understand and evaluate critically academic literature in international relations, as well as become familiar with major themes in international relations research. We will discuss important theoretical approaches used in the study of international politics and explore the manner in which social scientific research is conducted. The broad overview of theories and research topics in this course should enable you to identify areas of interest that you can further pursue in subsequent graduate courses and in independent research. This course is designed for graduate students who are planning to pursue careers in international relations or political science research; we will not focus on current events or issues in particular world regions.”
Wednesdays, 1:00 p.m. – 4:00 p.m.
236 Pond Lab
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PL SC 597.001
War and Domestic Politics
Professor Glenn Palmer
This class looks at the reciprocal relationship between a state’s domestic politics and its involvement in interstate war. Analyzing international relations from the perspective of this two-way relationship is consistent with more recent scholarship in the field of foreign policy. Research that incorporates the impacts of domestic into analyses of international behavior is most advanced, I think, in the focus on international conflict. I expect that over time we will develop more systematic and rigorous knowledge of the domestic politics-foreign policy nexus. Currently, however, our understanding of how war affects domestic politics and vice versa shows that approaches – such as realism – to foreign policy that ignore this interaction are greatly underspecified. Domestic politics matter for foreign policy.
We will investigate, among other things, how popular support or opposition to war involvement evolves and how it affects subsequent actions taken in the conduct of the war; how war strategy is affected by anticipated political support; how war involvement is financed; and how war involvement affects the political fortunes of leaders.
Thursdays, 3:00 p.m. – 6:00 p.m.
236 Pond Lab
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PL SC 597.002
Government Corruption in Comparative Perspective
Professor Elizabeth Carlson
“Corruption” is a popular, and often unchallenged, explanation for everything from economic stagnation to bureaucratic delays to unpopular policies. But the concept is actually quite slippery: what acts count as corruption, what acts count as legitimate means of influencing politics, and what acts are simply incompetent governance? How do we identify and measure exchanges that all participants are intentionally keeping secret? Why have some countries virtually eliminated corruption while it is endemic in others? What harm does corruption actually cause, to whom, and how? How can corruption be curtailed — or can’t it? The course will provide a nuanced view of the causes and effects of corruption as well as an introduction to the particular methodological challenges inherent in studying it.
Wednesdays, 9:05 a.m. – 12:05 p.m.
236 Pond Lab
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PL SC 597.003
Social & Political Network Analysis
Professor Bruce Desmarais
A network is a set of relationships among units. The study of networks in political science, the social sciences, and beyond has grown rapidly in recent years. This course is a comprehensive introduction to methods for analyzing network data. We will cover network data collection and management, the formulation and expression of network theory, network visualization and description; and methods for statistical inference with networks. The course will make extensive use of real-world applications—primarily in political science—and students will gain a thorough background in the use of network analytic software. Though most of the applications will be drawn from political science, and we will prioritize methods that are common in political networks research, this course will be relevant to anyone interested in the study of network data.
Thursdays, 11:30 a.m. – 2:30 p.m.
110 Walker Building
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SODA 502
Approaches and Issues in Social Data Analytics
Professor Bruce Desmarais
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, 11:30 a.m. – 2:30 p.m.
TBA