Advanced Methods in Behavioural Research - PSY00120M
- Department: Psychology
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2025-26
Module summary
Numerical and analytic skills are highly prized by employers, and provide graduates with a competitive edge in the job market, or when applying for future courses of study. This module will introduce a range of modern analysis techniques used in both academic and commercial domains. Concepts will be taught with reference to real examples and controversies, and practical sessions will give students hands-on experience of implementing the techniques using R.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 2 2025-26 |
Module aims
The aim of this module is to introduce the theory behind a range of advanced techniques for analysing complex datasets. The selected methods reflect statistical techniques used in both academia and in industry, and you will learn how each one can be used to tackle questions about real-world behavioural data. You will learn to evaluate each method for its benefits and limitations, and compare different statistical solutions to research questions. You will also gain hands-on experience of implementing these methods in R.
Module learning outcomes
- Give an overview of each data analysis method, including the key underlying theoretical assumptions
- Compare analysis techniques and select an appropriate method for a given data set or experimental design
- Design studies to take advantage of advanced analysis techniques
- Implement a range of data analysis techniques in R
Module content
- Mixed effects modelling
- Meta-analysis and systematic reviews
- Stochastic methods
- Power analysis
- Nonlinear curve fitting and parameter optimization
- Structural equation modelling
- Mixture models
- Machine Learning
- Item Response Theory
- Bayesian statistics
- Robust and reproducible science
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 100 |
Module feedback
Marks will be available on e:vision.
Indicative reading
Baker, D.H. (2022). Research Methods Using R: Advanced Data Analysis in the Behavioural and Biological Sciences. Oxford University Press.