Bayesian Statistics for Social Scientists

Description:

led by Dr Rowland Seymour, University of Birmingham

This one-day module introduces social scientists to the principles and practice of Bayesian inference. Participants will learn how Bayesian methods allow researchers to update beliefs with data, incorporate prior knowledge, and quantify uncertainty in intuitive ways. The session blends theory and application: we will cover the core building blocks of Bayesian thinking, explore hierarchical models common in social research, and introduce modern computational approaches for estimation and simulation. Hands-on exercises in R will give participants practical experience fitting models, interpreting results, and communicating findings clearly. By the end of the day, participants will be able to recognise when Bayesian approaches are appropriate, implement simple models, and situate Bayesian reasoning within the wider toolkit of social science research.

Attendees will need something to write with (pen and paper, or tablet) and a laptop with R and R Studio downloaded. Attendees can find out how to do this and work through some short pre-course material to get familiar with R on the module website https://rowlandseymour.github.io/BS4SS/. 

Before registering, please read our Event Engagement Statement. 

Accessibility information for the venue can be found here: https://www.accessable.co.uk/the-university-of-edinburgh/central-area/access-guides/edinburgh-futures-institute-efi. If you have any additional accessibility needs, please indicate these in the registration form.

Cost:

Free

Website and registration:

Region:

Scotland

Keywords:

Quantitative Data Handling and Data Analysis, Bayesian statistics

Related publications and presentations from our eprints archive:

Quantitative Data Handling and Data Analysis

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