Essex Summer School in
Social Science Data Analysis

Richard Morey

Richard D. Morey obtained his PhD in Psychology and Masters in Statistics from the University of Missouri in 2008. Since then, he has held an appointment as an Assistant Professor at the University of Groningen in the Netherlands. His main research interests are Bayesian hierarchical models in the cognitive sciences (including working memory, recognition memory, and subliminal priming) and statistical model comparison methods.

3G Bayesian Analysis for the Social Sciences

Richard Morey, Oxford Brookes University
6 - 17 August (two week course / 35 hrs)

Detailed Course Outline [PDF]

Course Content

In recent decades, there has been an explosion of interest in Bayesian methodologies in the sciences. There are several reasons for this recent interest: first, Bayesian methods often yield easier-to-interpret answers to statistical questions than classical methods; and second, Bayesian methods are applicable in situations where classical methods are difficult or impossible to implement. In this course, you will learn the basics of practical Bayesian data analysis.

Course Objectives

The course will begin with the theory behind Bayesian data analysis, and move toward simple, common models in the social sciences, like t tests, ANOVA, and regression. From there, we will learn about more complicated models and how these may be fit to the data. Special attention will be given to Markov Chain Monte Carlo (MCMC) methods, which give Bayesian methods their immense flexibility and power. Using software, the power of MCMC methods are available to researchers who are not specialists in Bayesian methods. This class will give you the tools to fit a wide variety of models easily, though the use of the WinBUGS software.

Course Prerequisites

A working knowledge of probability theory is assumed for this class. In addition, knowledge of common statistical models used in the social sciences is necessary , including t tests, ANOVA, and regression. A familiarity with more complicated models such as logistic regression will also prove helpful. Finally, a basic knowledge of the R statistical environment, which will be extensively used in the course, will be very helpful. For many methods, we will use WinBUGS or JAGS to fit models.

BackgroundReading

Gelman, Carlin, Rubin, and Stern's classic Bayesian Data Analysis

Kruschke. Doing Bayesian data analysis

Lee. Introductory Bayesian Statistics

Required Reading

Jackman, S. 2009. Bayesian Analysis for the Social Sciences. Wiley: Chichester.

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