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Doing Bayesian data analysis in psychology and the social sciences - Workshop 2

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Course Information

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This workshop aims to provide a solid theoretical and practical foundation for real-world BDA in psychology and social sciences.

Booking OptionFee
Full Workshop Fee £20.00
Full Workshop Fee - Postgraduate student rate £10.00

 

Course Code

ESRC Workshop 2

Course Date

7th April 2017

Places Available

Course Leader

Mark Andrews

Course Fee

£20.00
Course Description

Time: 9 am - 5 pm

Location: Room 424, Chaucer building, Nottingham Trent University, NG1 5LT

Prerequisites

The prerequisites for this workshop are fulfilled by Workshop 1. They include a general understanding of the core concepts of Bayesian statistical inference, and the general distinction between classical and Bayesian statistical methods.

Content

This workshop aims to provide a solid theoretical and practical foundation for real-world BDA in psychology and social sciences. It will focus primarily on the linear statistical models and so-called conjugate prior distributions. The reason for this focus is twofold. First, linear models – which include t tests, ANOVA, and linear regression models – are the core of the standard repertoire of statistical models. Studying the Bayesian counterparts of these approaches will therefore be a natural transition. Second, Bayesian inference in linear models with conjugate priors is analytically tractable, and this entails, amongst other things, that we can use relatively simple formulae to calculate the posterior distribution over the parameters and to make predictive inferences. This allows us to illustrate the general nature of Bayesian inference quickly and easily. In practical terms, this workshop will involve the use of the R statistical computing environment both to calculate posterior distributions in linear models and to graphically illustrate them.

Learning outcomes

On completion of this workshop, we expect attendees to be able to confidently perform and understand the Bayesian counterparts to many of the models with which they would be already familiar. They will also become familiar with new concepts – conjugate priors, posterior predictive distributions, marginalized likelihood functions – that arise only in the context of Bayesian data analysis.

Indicative reading

Lee, P. M. (2004). Bayesian Statistics: An Introduction. London, UK: Hodder Arnold.

Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2003). Bayesian data analysis (2nd ed.). Chapman & Hall.