EH217-7-SL-CO:
Bayesian Analysis for the Social and Behavioural Sciences
2023/24
Essex Summer School in Social Science Data Analysis
Colchester Campus
Summer & Long Vacation
Postgraduate: Level 7
Current
Monday 22 April 2024
Wednesday 02 October 2024
15
03 February 2023
Requisites for this module
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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.
No information available.
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 JAGS and stan 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 JAGS or stan to fit models.
Background reading
Gelman, Carlin, Rubin, and Stern’s classic Bayesian Data Analysis,
Jackman, S. Bayesian Analysis for the Social Sciences (Wiley, 2009)
Lee. Introductory Bayesian Statistics.
Required text – this text will be provided by ESS:
McElreath, R. Statistical Rethinking, 2nd Edition (CRC Press, 2020)
Module information will be made available at https://essexsummerschool.com/.
Please contact essexsummerschoolssda@essex.ac.uk and govpgquery@essex.ac.uk with any queries.
No information available.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
No
No
No
Available via Moodle
No lecture recording information available for this module.
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