Advanced Research Methods - PSY00025H
Module will run
Occurrence | Teaching period |
---|---|
A | Semester 1 2025-26 |
Module aims
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. Former students have gone on to varied and interesting careers in analytical roles, including working for the Government Statistical Service and the Joseph Rowntree Foundation.
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 some data analysis techniques in R (a software environment used for statistics)
Module content
- Introduction to R
- Meta-analysis and systematic reviews
- Mixed effects models
- Stochastic methods (bootstrapping)
- Nonlinear curve fitting and optimization
- Structural equation modelling
- Multivariate Pattern Analysis
- Fourier Analysis
- Bayesian statistics
Indicative assessment
Task | % of module mark |
---|---|
Online Exam -less than 24hrs (Centrally scheduled) | 100 |
Special assessment rules
None
Indicative reassessment
Task | % of module mark |
---|---|
Online Exam -less than 24hrs (Centrally scheduled) | 100 |
Module feedback
Marks will be available through e:vision.
Indicative reading
Baker, D.H. (2022). Research Methods Using R: Advanced Data Analysis in the Behavioural and Biological Sciences. Oxford University Press, ISBN: 9780192896599