Bayesian Methods for the Social and Behavioral Sciences

October 4, 2013
10:00 AM – 1:00 PM
Nebraska Room (#335) of the Iowa Memorial Union

Bayesian statistics has long been overlooked in the quantitative methods training for social and behavioral 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 PPC, in collaboration with the Depts of Psychology, Sociology, Political science, and Statistics and the Injury Prevention Research Center, is co-sponsoring a short course on Bayesian Methods for the Social and Behavioral Sciences by David Kaplan, a Bayesian statistician at the University of Wisconsin, Madison in the Ed Psych Department.

The workshop is Friday, October 4, from 9:00-12:00 in the Nebraska Room (#335) of the Iowa Memorial Union.

The orientation of this short course is to introduce practicing social and behavioral scientists to the basic elements of Bayesian statistics and to show why the Bayesian perspective provides a powerful alternative to the frequentist perspective.  It is assumed that students of the short course will have a background in basic statistical methods up to, and including, regression analysis. 

During the first half of the course we will explore the major differences between the Bayesian and frequentist paradigms of statistics, with particular focus on how uncertainty is characterized.  The implications of the Bayesian perspective for hypothesis testing will be highlighted.  Next, we will explore the basics of model building and model evaluation.  During the second half of the course we will discuss how Bayesian models are estimated and show an example based on simple regression analysis.   The workshop will close with a discussion of the relative advantages of the Bayesian perspective.

There is no cost to participate, but space is limited, so all participants must register at the following link: https://publicpolicycenter.wufoo.com/forms/bayesian-methods-for-social-and-behavioral-science/

Individuals with disabilities are encouraged to attend all University of Iowa-sponsored events. If you are a person with a disability who requires an accommodation in order to participate in this program, please contact Dragana Petic at dragana-petic@uiowa.edu.