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.

Statistics & Research Design (PGT) PSYCH5020

  • Academic Session: 2022-23
  • School: School of Psychology and Neuroscience
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

This course is designed to provide a detailed understanding of the use of multilevel regression modeling for data analysis, as well as to provide a basic familiarity with non-parametric approaches and Bayesian modeling. Concepts and techniques are demonstrated using the statistical platform R, which is open source (weblink and runs under most operating systems. Learning is reinforced through weekly assignments that involve working with different types of data.


20 hours, 2 hours per week

Requirements of Entry

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

For the MSc Brain Sciences, at least a second class (2:2) honours degree in neuroscience, physiology, psychology or acceptable equivalent(s).


Coursework based assessment (100%)

Course Aims


■ To introduce students to basic techniques involved in organizing and processing complex datasets.

■ To provide a non-technical introduction to nonparametric and robust techniques to improve on parametric statistics and detect outliers.

■ To provide a basic understanding of the regression framework, including how to express study design through regression.

■ To provide an understanding of multilevel regression models and their use in experimental research.

■ To provide a basic familiarity with Bayesian approaches to modelling data;

■ To train students to use the statistical programming language R.

Intended Learning Outcomes of Course

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

■ use R to organize, restructure, and visualise complex datasets;

■ explain the basic ideas behind resampling and robust statistics and their relation to classic parametric techniques;

■ make predictions from a multiple regression equation and explain the interpretation of parameter estimates;

■ express various study designs within a multilevel regression framework;

■ compute basic quantities within a Bayesian framework.

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.