Undergraduate Course: Bayesian Statistics for Social Scientists (SSPS10016)
Course Outline
School | School of Social and Political Science |
College | College of Humanities and Social Science |
Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
SCQF Credits | 20 |
ECTS Credits | 10 |
Summary | This course will cover the basics of Bayesian analysis, drawing on a range of examples from across the social sciences. It will cover the philosophical reasons for employing this approach and a discussion of its limitations. Attention will then turn to the use of MCMC methods to apply Bayesian thinking to analysis common within the social sciences. Practical issues related to the estimation of different types of models will then be considered, each rooted in specific social science examples. Models to be covered will include, comparisons of means (t-tests and ANOVA), single level regression models, measurement models (confirmatory factor analysis) and a brief introduction to multilevel modelling.
Bayesian methods are implemented in a range of software packages. This course will likely employ Stat-JR (Centre of Multilevel Modelling, University of Bristol) as a tool to allow students to learn BUGS based syntax. The interpretation (and model evaluation) taught on the course would be applicable to other software packages, such as MLWiN and MPlus.
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Course description |
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Entry Requirements (not applicable to Visiting Students)
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Other requirements | None |
Information for Visiting Students
Pre-requisites | None |
Course Delivery Information
Not being delivered |
Learning Outcomes
Students will:
- understand the principles and philosophical approach of Bayesian analysis;
- understand the situations within social science where the use of Bayesian methods maybe appropriate, and those where they are not;
- be able to create models in an appropriate software package;
- be able to report and interpret the output of Bayesian models. Covering both substantive interpretation and issues of model fit, appropriateness and robustness.
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Additional Information
Graduate Attributes and Skills |
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Keywords | Not entered |
Contacts
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