MRes Cognitive Neuroscience and Neuropsychology

Year of entry: 2024

Course unit details:
Advanced Data Skills, Open Science and Reproducibility

Course unit fact file
Unit code PCHN63101
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Available as a free choice unit? No


  • Course Unit Overview

The topics covered will include:

• The principles of Open Science in the context of replication and reproducibility

• Principles and practice of conducting power analyses.

• Data wrangling and visualisation in R

• Simple linear and multiple regression under the General Linear Model (GLM) in R

• ANOVA under the General Linear Model (GLM) in R

• Writing reproducible reports using R Markdown

• Writing reproducible presentations using xaringan

• Capturing your computational environment using Binder and GitHub


To familiarise students with a range of advanced, quantitative analytical techniques at a level not normally encountered in undergraduate study.

To equip students with the confidence and skills necessary to apply the methods to datasets using R.

To provide sufficient understanding for sophisticated statistical decision-making and interpretation of results.

To contextualise statistical analysis in the context of the principles of reproducibility and Open Science. 

Learning outcomes

  • Learning Outcomes

    Having attended the unit, students will be able to:

    • select the analytic technique(s) appropriate for a range of research questions

    • demonstrate their understanding of advanced psychological statistics and ability to apply the techniques to datasets using R.

    • demonstrate their ability to understand and interpret the results of a range of advanced psychological statistics based on the General Linear Model

    • demonstrate their ability to generate reproducible analysis.

Teaching and learning methods

Teaching and Learning Methods

In Semester 1 there will be weekly 2-hour seminars providing an introduction and explanation of each technique and practical training. Each technique will be demonstrated using R or other appropriate software. 

Assessment methods

Continuous assessment. Two assignments. Each topic will be formally assessed by a written assignment, worth 50% of the marks for this module.

Data Wrangling and Data Visualisation: the form of assessment varies from year to year but will involve using the tidyverse packages to tidy and visualise data using R. You will need wrangle/tidy a data set that will be provided to you and then create visualisations of the dataset in R. Your report will be written in R Markdown.  (1000 words equivalent).

ANOVA: the form of assessment varies from year to year. You will analyse and discuss data sets provided to you. You will be asked to carry out the appropriate analyses and for each dataset, write a results section (where you only report on descriptive and inferential statistics) and a brief discussion section (where you interpret the results, based on the analyses you carried out). Your report will be written in R Markdown.  (1000 words equivalent).

Feedback methods

No information available.

Recommended reading

Recommended Reading

Appropriate online R-based resources will be made available alongside each lecture.

Study hours

Scheduled activity hours
Lectures 24
Independent study hours
Independent study 126

Teaching staff

Staff member Role
Andrew Stewart Unit coordinator

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