The Master of Science Degree in Applied Political Analytics is offered jointly by the Department of Government & Politics and Joint Program in Survey Methodology. The program provides advanced training in the application of data science to the analysis of key issues in political science. APAN exists to train future employees of a multitude of political organizations seeking to stay on the cutting edge of political data analysis. We do this by providing students with a firm foundation of the principles and theories of political science alongside practical applications of sound research designs, coding, database management, and data visualization.
The course requirements for APAN consist of 18 credits of advanced political science and 18 credits of data science. The program is administered both to college graduates, as a standalone master’s, and to existing UMD undergraduates as a combined 4+1 pair of degrees (BA plus MS in APAN).
This is a non-thesis program, though students on both tracks will also complete a major project in one of their courses in their final year of the program.
Upon completion of this program, all students will possess:
- A secure and rigorous theoretical background in the principles and practices of political science.
- A firm grasp on the mathematical and statistical underpinnings of data analysis, combining lessons learned about everything from conceptual research designs to measurement, coding, database management, and visualization.
- An enhanced and advanced understanding of the potentials of political analysis, to include the use of professional analytical tools and execution of a multitude of estimation methods.
- The confidence and skills necessary to create, execute, analyze, and present a unique and technically appropriate research design of substantive political importance, from beginning to end.
Program Course Requirements
GVPT601 – RESEARCH DESIGN FOR POLITICAL ANALYSIS
Introduction to empirical research techniques used in political science. Exploring the core questions that motivate political science research and the approaches used to answer those questions in various fields of political science, including American Politics, Comparative Politics, and International Relations. 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
Introduction to the core concepts necessary to measure political behavior. Students will learn to take ideas from the concept stage to the measurement stage as part of a research design to answer theoretically motivated questions about political behavior and other political activity.
GVPT621 – CODING IN STATISTICAL SOFTWARE
Introduction to different statistical software packages used in empirical political research. Instruction in beginning programming in STATA and R.
GVPT624 – NATIONAL SECURITY AND INTERNATIONAL RELATIONS
Introduction 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
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 work with a number of surveys to advance their knowledge of public opinion.
GVPT685 – VOTING, CAMPAIGNS, AND ELECTIONS
Introduction 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 MODELING I
The first course in a two-term sequence in applied statistical methods covering topics such as regression, analysis of variance, categorical data, and survival analysis.
SURV616 – STATISTICAL MODELING II
Builds on the introduction to linear models and data analysis provided in Statistical Methods I. Topics include analysis of longitudinal data and time series, categorical data analysis and contingency tables, logistic regression, log-linear models for counts, statistical methods in epidemiology, and introductory life testing.
SURV621 – FUNDAMENTALS OF DATA COLLECTION I
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
The stages of questionnaire design; developmental interviewing, question writing, question evaluation, pretesting, and questionnaire ordering and formatting. Reviews of the literature on questionnaire construction, the experimental literature on question effects, and the psychological literature on information processing. Examination of the diverse challenges posed by self versus proxy reporting and special attention is paid to the relationship between mode of administration and questionnaire design.
SURV727 – FUNDAMENTALS OF COMPUTING AND DISPLAY
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. The course focuses on R as the primary computing environment, with excursus into SQL and Big Data processing tools.
SURV740 – FUNDAMENTALS OF INFERENCE
Focuses on the fundamentals of statistical inference in the finite population setting. Overview and review fundamental ideas of making inferences about populations. 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.
***All students will complete a major project in their final year of the program (apart from coursework) which combines lessons gained from the scope of courses taken in the program. An APAN faculty member will guide and assess this final project.