MRes Experimental Psychology with Data Science

For entry in the academic year beginning September 2027, the tuition fees are as follows:

• MRes (full-time)
  UK students (per annum): £15,400
  International, including EU, students (per annum): £35,700

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

International student CAS deposit

Self-funded international applicants are required to pay a deposit of £2500 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. Some applicants will be required to pay a higher deposit. More information on tuition fee deposits .

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

The unit will consist of 12 weeks of asynchronous content coupled with synchronous workshops.  

To further develop students’ understanding of statistics as applied to non-normal outcome variables and dependent data. To provide students with experience and understanding of how to implement different methods for modelling these data using R. To provide students with a clear understanding of hierarchical/multilevel/mixed-effects models. To provide students with a clear understanding of the generalised linear models framework. To provide students with understanding and experience of how these two methods can be combined to form generalised linear mixed-effects models. 

Demonstrate an understanding of the theory behind the generalised linear model framework

• Apply this framework to different types of outcome variable using R
• Demonstrate an understanding of the theory behind dependent data and its impact on inference
• Demonstrate an understanding of different approaches to modelling dependent data, including the traditional repeated measures ANOVA, generalised least squares (GLS) and multilevel/mixed-effects models
• Apply these approaches to different forms of dependent data using R
• Combined these approach to analyse non-normal dependent data using generalised linear mixed-effects models in R 

This unit will be taught exclusively using a “flipped classroom” approach. Each week, students will be provided with detailed online asynchronous materials that discuss the theory behind each week’s topic, alongside snippets of R code to help aid understanding. These materials are to be studied for 1 week before the associated synchronous workshop. During these workshops, students can engage in discussions of the materials before completing workshop activities designed to consolidate and enhance the asynchronous materials. 

Recommended reading will be provided  as part of the online asynchronous materials