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Advanced Methods in Behavioural Research - PSY00120M

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  • Department: Psychology
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2026-27
    • See module specification for other years: 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 2026-27

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

  • Explain the purpose and use of a range of data analysis methods, including their underlying theoretical and statistical assumptions
  • Compare analysis techniques and select an appropriate method for a given research question, dataset or experimental design
  • Implement a range of data analysis techniques usingR, making effective use of specialist packages
  • Critically evaluate and interpret analytical results

Module content

Indicative methods include:

  • Meta-analysis
  • Mixed effects models
  • Quantile regression
  • Structural equation modelling
  • Item response theory
  • Mixture models
  • Network modelling
  • Machine learning
  • Stochastic methods
  • Missing data
  • Bayesian statistics

Indicative assessment

Task % of module mark
Essay/coursework 100.0

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 100.0

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.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.