A Brief Introduction to Bayesian Statistics for Social Survey Analysis

November 8, 2017, 1pm

GESIS, Mannheim, B2,8

David Kaplan

Abstract

Bayesian statistics has long been overlooked in the quantitative methods training for social scientists.  Typically, the only introduction that a student might have to Bayesian ideas is a brief overview of Bayes' theorem while studying probability in an introductory statistics class.  This is not surprising.  First, until recently, it was not feasible to conduct statistical modeling from a Bayesian perspective because of its complexity and lack of available software.  Second, Bayesian statistics represents a powerful alternative to frequentist (classical) statistics, and is therefore, controversial. Recently, however, there has been great interest in the application of Bayesian statistical methods, mostly due to the availability of powerful (and free) statistical software tools that now make it possible to estimate simple or complex models from a Bayesian perspective.  The orientation of this talk is to introduce social scientists to elements of Bayesian statistics and to show why the Bayesian perspective provides a powerful alternative to the frequentist perspective.

About the speaker

David Kaplan is the Patricia Busk Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin – Madison.  Dr. Kaplan holds affiliate appointments in the University of Wisconsin’s Department of Population Health Sciences and the Center for Demography and Ecology, and is also an Honorary Research Fellow in the Department of Education at the University of Oxford. Dr. Kaplan is an elected member of the National Academy of Education, a recipient of the Humboldt Research Award, a fellow of the American Psychological Association (Division 5), a fellow of the German Institute for International Educational Research, and was a Jeanne Griffith Fellow at the National Center for Education Statistics.  Dr. Kaplan’s program of research focuses on the development Bayesian statistical methods for education research. His work on these topics is directed toward applications to large-scale cross-sectional and longitudinal survey designs. Dr. Kaplan received his Ph.D. in education from UCLA in 1987.