Workshops on Bayesian Analysis

Bayesian data analysis is rapidly supplanting traditional statistical methods because it provides richer inferences from empirical observations, without having to resort to ill-defined probability values in hypothesis tests. The Bayesian paradigm is particularly useful for the type of data that social scientists encounter, given its recognition of the mobility of population parameters, its ability to incorporate information from prior research, and its ability to update estimates as new data are observed.

In 2015, the ICPSR Summer Program is offering four workshops on Bayesian analysis.

Three- to Five-Day Workshops

July 7-10, 2015
Doing Bayesian Data Analysis: An Introduction
LOCATION: Ann Arbor, MI
INSTRUCTOR: John Kruschke, Indiana University
DESCRIPTION: This workshop introduces participants to modern Bayesian methods. We will begin with the basic ideas of probability and Bayes' rule. After that, we move on to cover probability distributions, grid approximation, Markov chain Monte Carlo methods, and Bayesian approaches to some specific statistical models (e.g., the multiple linear regression model, ANOVA, contingency table analysis, hierarchical models). Along the way, we will consider additional topics, including null hypothesis significance testing, Bayesian model comparison, Bayesian assessment of null values, and statistical power. Upon completion of this workshop, participants should be able to incorporate Bayesian tools into their own research projects and data analyses.
FEE: ICPSR Members, $1400; Non-members, $2800

August 3-5, 2015
An Applied Introduction to Bayesian Methods
LOCATION: Chapel Hill, NC
INSTRUCTOR: Jeffrey Harden, University of Colorado at Boulder
DESCRIPTION: This course will provide an introductory overview of Bayesian methods as they are applied to social science research. We will focus on the two complementary goals of learning the theory behind Bayesian inference as well as practical implementation of several common models in R.
FEE: ICPSR Members, $1300; Non-members, $2600


Four-Week Workshops

Session I: June 22-July 17, 2015
Introduction to Applied Bayesian Modeling for the Social Sciences
LOCATION: Ann Arbor, MI
INSTRUCTORS: Ryan Bakker, University of Georgia, and Johannes Karreth, University at Albany, State University of New York
DESCRIPTION: This course introduces the basic theoretical and applied principles of Bayesian statistical analysis in a manner geared toward students and researchers in the social sciences. The course begins with a discussion of the strengths of the Bayesian approach for social science data and the philosophical differences between Bayesian and frequentist analyses. Next, the course covers the theoretical underpinnings of Bayesian modeling and provides a brief introduction to the primary estimation algorithms. The bulk of the course focuses on estimating and interpreting Bayesian models from an applied perspective.
FEE: ICPSR Members, $2300 (before May 1); Non-members, $4600 (before May 1)

Session II: July 20-August 14, 2015
Advanced Bayesian Models for the Social Sciences
LOCATION: Ann Arbor, MI
INSTRUCTORS: Jeffrey Harden, University of Colorado at Boulder, and Daniel Stegmueller, University of Essex
DESCRIPTION: This course covers the theoretical and applied foundations of Bayesian statistical analysis at a level that goes beyond the introductory course. Topics include: Bayesian stochastic simulation (Markov chain Monte Carlo); model checking, model assessment, and model comparison, with an emphasis on computational approaches; Bayesian variants of "workhorse" political science models, such as linear models, models for binary and count outcomes, discrete choice models, and seemingly unrelated regression; and advanced Bayesian models, such as hierarchical/multilevel models, models for panel and time-series cross-section data, latent factor and item response theory (IRT) models, as well as instrumental variable models.
FEE: ICPSR Members, $2300 (before May 1); Non-members, $4600 (before May 1)