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Instructor: Alexander Wenz

The fields of survey methodology and data science draws on theories and practices developed in several academic disciplines – mathematics, statistics, psychology, sociology, computer science, and economics. To become an accomplished professional in these fields requires a mastery of research literatures as well as experience designing, conducting, and analyzing surveys and data from other sources, such as administrative records, social media, or transactions.

This course introduces the student to a set of principles of survey design and data science that are the basis of standard practices in these fields. The course exposes the student to research literatures that use both observational and experimental methods to test key hypotheses about the nature of human behavior and other factors that affect the quality of data. It will also present important statistical concepts and techniques in sample design, execution, and estimation, as well as models of behavior describing errors in responding to survey questions. Thus, both social science and statistical concepts will be presented.

The course uses the concept of total error as a framework to discuss coverage properties of sampling frames and organic data, alternative sample designs and their impacts on standard errors of statistics, different modes of data collection and generation, the role of interviewers and respondents in surveys, impacts of nonresponse and missing data on statistics, measurement errors in data, data processing, and data/research ethics.

The course is intended as an introduction to the fields of survey methodology and data science, taught at a graduate level. Lectures and course readings assume that students understand basic statistical concepts (at the level of an undergraduate course) and have exposure to elements of social science perspectives on human behavior. For those lacking such a background, supplementary readings are recommended.