Data Skills for Reproducible Science (PGT) PSYCH5077

  • Academic Session: 2019-20
  • School: School of Psychology
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

This course provides an overview of skills needed for reproducible research and open science using the statistical programming language R. Students will learn about data visualisation, data tidying and wrangling, archiving, iteration and functions, probability and data simulations, general linear models, and reproducible workflows.

Timetable

1 x 2 hour lecture per week for 10 weeks

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).

Assessment

Assessment consists of two components:

1. 7 of 8 in-class set exercises, and

2. a reproducible report demonstrating all class skills.

 

The lowest score of the 8 in-class set exercises will be dropped and the remaining 7 scores will be equally weighted. The specific content of the report will be independent of the specific content of the in-class set exercises.

 

The score for the higher of these two components will contribute 70% to the total grade, while the score for the lower of these two components will contribute 30%.

Course Aims

This course aims to teach students the basic principles of reproducible research and to provide practical training in data processing and analysis in the statistical programming language R.

Intended Learning Outcomes of Course

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

 

■ Draw on a range of specialised skills and techniques to formulate a research design appropriate to various kinds of questions in psychology and neuroscience;

■ Write scripts in R to organise and transform data sets using best accepted practices;

■ Explain basics of probability and its role in statistical inference;

■ Critically analyse data and report descriptive and inferential statistics in a reproducible manner.

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.