GVPT601. Research Design for Political Analytics (3 credits).
This course will introduce students to the empirical research techniques used in political science. Students will explore the core questions that motivate political science research and the approaches used to answer those questions. Students will understand when and how to implement research designs that utilize experiments, surveys, case studies, historical data, and administrative data.
GVPT620. The Logic and Practice of Measurement (3 credits).
This course will introduce students to core concepts necessary to measure political behavior. Students will learn to take ideas from the concept stage to measurement of the concepts as part of a research design to answer theoretically motivated questions about political behavior and other political activity.
GVPT621. Coding in Statistical Software (3 credits).
This course will introduce students to different statistical software packages I used in empirical political research and which they will use in later substantive courses. Students will receive instruction in beginning programming in these packages, which will ST AT A and R.
GVPT624. National Security and International Relations (3 credits).
This course will introduce students to key areas of research in national security and international relations. Students will learn the major approaches to empirical research on national and international security and work with datasets focused on terrorist attacks and civil conflict.
GVPT635. Public Opinion (3 credits).
This course will investigate how citizens in a democracy think about politics, form attitudes, and how public opinion shapes and is shaped by the political environment. While being exposed to core debates in public opinion and the study of public opinion, students will use a number of surveys that have been central to advancing our knowledge of public opinion.
GVPT685. Voting, Campaigns, and Elections (3 credits).
This course will introduce students to the theoretical and empirical research on political participation, campaigns, and elections. By gaining an understanding of the literature and working with a variety of data sets, including surveys and voter history files, students will be equipped to carry out their own research on these topics.
SURV615. Statistical Methods I (3 credits).
The purpose of this class is to learn basic statistical methods through the use of linear model theory and regression. Particular topics covered include one- and two-sample t-tests, multiple linear regression, analysis of variance, regression diagnostics, model-building techniques, random effects models, and mixed models. The emphasis will be to understand and apply the methods presented, and develop a feel for how problems in data analysis can be viewed in several different ways. In all cases the emphasis will be on understanding the techniques, rather than deriving their theoretical properties. The student will be expected to apply the techniques on weekly homework assignments, a midterm project, and a final project.
SURV616. Statistical Methods II (3 credits).
Builds on the introduction to linear models and data analysis provided in Statistical Methods I. Topics include: Multivariate analysis techniques (Hotelling's T-square, Principal Components, Factor Analysis, Profile Analysis, MANOV A); Categorical Data Analysis ( contingency tables, measures of association, log-linear models for counts, logistic and polytomous regression, GEE) and Lifetime Data Analysis (Kaplan-Meier plots, logrank tests, Cox regression).
SURV621: Fundamentals of Data Collection I (3 credits).
This course is the first semester of a two-semester sequence that provides a broad overview of the processes that generate data for use in social science research. Students will gain an understanding of different types of data and how they are created, as well as their relative strengths and weaknesses. A key distinction is drawn between data that are designed, primarily survey data, and those that are found, such as administrative records, remnants of online transactions, and social media content. The course combines lectures, supplemented with assigned readings, and practical exercises. In the first semester, the focus will be on the error that is inherent in data, specifically errors of representation and errors of measurement, whether the data are designed or found. The psychological origins of survey responses are examined as a way to understand the measurement error that is inherent in answers. The effects of the mode of data collection (e.g., mobile web versus telephone interview) on survey responses also are examined.
SURV630. Questionnaire Design and Evaluation (3 credits).
This course focuses on the development of the survey instrument, the questionnaire. Topics include wording of questions (strategies for factual and non-factual questions), cognitive aspects, order of response alternatives, open versus closed questions, handling sensitive topics, combining individual questions into a meaningful questionnaire, issues related to question order and context, and aspects of a questionnaire other than questions. Questionnaire design is shown as a function of the mode of data collection such as face-to-face interviewing, telephone interviewing, mail surveys, diary surveys, and computer-assisted interviewing.
SURV727. Fundamentals of Computing and Data Display (3 credits).
Empirical social scientists are often confronted with a variety of data sources and formats that extend beyond structured and handle able survey data. With the emergence of Big Data, especially data from web sources play an increasingly important role in scientific research. However, the potential of new data sources comes with the need for comprehensive computational skills in order to deal with loads of potentially unstructured information. Against this background, the first part of this course provides an introduction to web scraping and APis for gathering data from the web and then discusses how to store and manage (big) data from diverse sources efficiently. The second part of the course demonstrates techniques for exploring and finding patterns in (non-standard) data, with a focus on data visualization. Tools for reproducible research will be introduced to facilitate transparent and collaborative programming. The course focuses on Ras the primary computing environment, with excursus into SQL and Big Data processing tools.
SURV740. Fundamentals of Inference (3 credits).
This course is one of the fundamental 3 courses required by all students in the Master's Program in Survey Methodology, and focuses on the fundamentals of statistical inference in the finite population setting. The course is design to overview and review fundamental ideas of making inferences about populations. It will emphasize the basic principles of probability sampling; focus on differences between making predictions and making inferences; explore the differences between randomized study designs and observational studies; consider model-based vs. design-based analytic approaches; review techniques designed to improve efficiency using auxiliary information; and consider non-probability sampling and related inferential techniques.