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PSY-40107 - Module Specification
School of Psychology
Faculty of Natural Sciences

For academic year: 2023/24 Last Updated: 13 February 2024

PSY-40107 - Enhancing Reproducibility in Research
Coordinator: Darren Rhodes
Lecture Time: See Timetable...
Level: Level 7
Credits: 15
Study Hours: 150
School Office: 01782 733736

Programme/Approved Electives for 2023/24

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2023/24

A 2016 survey of over 1500 researchers by the prominent journal Nature found that 90% believed there is a 'reproducibility crisis'; in research. The factors leading to a lack of reproducibility are complex and many, ranging from the behaviour of individual researchers through to the incentive structures within academic publishing and funding. This module will provide an in-depth exploration of the main threats to reproducible research together with concrete solutions to counter these. The module will also provide hands-on experience of coding with an open-source statistical programming language and how to create a fully reproducible report of quantitative data analysis. (Note that no prior programming experience is required; we will work at a pace suitable for code-phobic individuals!). The module will leave you well-positioned to enhance the trustworthiness and quality of the research you conduct.

Aims
This module will introduce students to various issues related to enhancing reproducibility in research. The module will be a combination of content-based material (outlining current issues around reproducibility, delivered via synchronous and asynchronous material) together with hands-on experience with tools to aid reproducibility (such as analysis in R and reproducible report-writing using markdown).

Intended Learning Outcomes

describe and explain a range of threats to reproducibility in research and a range of proposed solutions to these threats: 1
identify and evaluate the key issues relating to reproducibility relevant to the students' primary area of research: 1
conduct data processing and simple analyses / visualisations using an open-source statistical programming language: 1
use open-source software tools to produce a reproducible report of data analysis: 1

Study hours

- 24 hours of scheduled teaching (interactive, discussion-based etc.)
- 24 hours of guided asynchronous learning (roughly 1 hour per week for content preparation, and 1 hour per week on learning and practicing with R and R markdown)
- 102 hours preparing the Reproducible analysis report

School Rules

None

Description of Module Assessment

1: Exercise weighted 100%
Reproducible analysis report (Approx 3-4,000 words)
Students will individually work through questions addressing aspects of both the conceptual content (i.e., coverage of threats to reproducibility and their proposed solutions) and practical content (i.e., reproducible data analysis skills) of the module. The tasks and questions will require students to demonstrate both the skills and the conceptual knowledge they have developed during the course of the module. The final report will be a fully reproducible document which contains a combination of written answers from the student, example code snippets showing the analysis code used to complete the task, and output associated with the code snippet (e.g., tidied data, output of statistical analysis, plots). Marking will assess the degree to which the student has sufficiently detailed their steps to allow full reproducibility, together with the accuracy and depth of the responses to the tasks and/or questions.

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/student-terms-and-conditions/

Last Updated: 13 February 2024