MRes Psychology / Course details

Year of entry: 2020

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

Unit code BIOL63101
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
Offered by School of Biological Sciences
Available as a free choice unit? No

Overview

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.

• An introduction to statistical analysis using R and RStudio

• Writing reports using R Markdown

• Data 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

• Principles and practice of signal detection analysis. 

Aims

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 and other appropriate software.

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

    • 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

Assessment methods

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

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). (1000 words equivalent).

 

Signal Detection Analysis: the form of assessment varies from year to year. Part A - You will be asked to analyse and interpret a real data set (300 words). Part B - You will be asked to describe a paper of your choice that uses signal detection analysis and critically evaluate their use of the technique (700 words). 

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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