What's All the Buzz about Bayes? An Overview of Bayesian Inference for the Social and Behavioral Sciences, Part 2

David Kaplan, Ph.D.Patricia Busk Professor of Quantitative Methods, University of Wisconsin-Madison

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Description

Spring 2015 Nebraska Methodology Workshop

To receive the password to view this workshop please send a message to mapacademy@unl.edu.

The Nebraska Academy for Methodology, Analytics and Psychometrics invites you to view a workshop guided by David Kaplan, the Patricia Busk Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin–Madison. 

Workshop Description

Bayesian statistics has long been overlooked in the quantitative methods training for education. 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 short course is to introduce practicing education 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. 

Topics to be covered in this short course include:
  1. Major differences between Bayesian and frequentist paradigms of statistics, with particular focus on how uncertainty is characterized
  2. Bayes’ theorem
  3. Bayesian model building and model evaluation
  4. Bayesian computation
  5. An example
  6. Wrap-up: Relative advantages of the Bayesian perspective