Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Formal Models and Quantitative Methods for Psychology (PGT) PSYCH5025

  • Academic Session: 2021-22
  • School: School of Psychology
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

The course typically introduces students to the R/RStudio modelling environments through hands-on sessions providing a mixture of lectures, class exercises and oral presentations


Preparatory meetings and residential seminar/workshop/hackathon/presentation

Requirements of Entry

Typically a 2:1 honours degree in Psychology or related discipline.

Excluded Courses



There is one piece of coursework worth 100% of the overall mark. The coursework can be chosen from the following list:

1. A critical review on a topic relevant to the course (2,500 words).

2. An original computer program that performs a specific task relevant to the experimental techniques described in the course. 

3. A detailed description of how to analyse data from a specific neuroimaging experiment (e.g. fMRI, EEG).

Course Aims

To introduce students to formal models in the psychological sciences. This entails the application of parametric estimation and inference, prediction and testing of models, and quantitative methods in general.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ use specialised tools for quantitative methods (e.g. packages in R)

■ critically evaluate challenges of formal modelling, and parametric testing (e.g. sampling, simulation)

■ engage in cutting-edge applications of data analyses (e.g. statistical learning)

■ develop interactive apps (e.g. shiny apps in R/RStudio)

■ present and communicate functionality of IT solutions  

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.