Home / RMS1 / Labs



1x 1 hour lab each week.

Due to the number of students enrolled in this course, there are 4 timetabled lab sessions. Please sign up to one of these and attend this same session each week. If you have not done so already, please sign up to a lab session via LEARN.

Lab sessions will provide the primary practical skill sets for statistical analysis in psychology. Statistics and computer software require practice, so please make sure you attend all labs, and do all related exercises. The lab sessions will use the R statistical environment.

In addition to the one hour lab session, students will also be asked to complete a problem set and a R tutorial (SWIRL) in their own study time. Both the problem sets and the SWIRL lab sessions should be completed before arriving at the taught lab sessions. See the course Study Guide on LEARN for suggestions on time planning.

Problem Sets

The weekly problem sets act as a bridge from the more theory based lecture content, to the more practical labs. Dependent on the topic for the week, problem sets will contain a mix of introductory R material, hand-calculated problems (like those done on the board in lectures and like those which will appear in your exam), results interpretation and guidance on writing up results. Understanding and conducting statistical analyses takes practice, and the problem sets form a core part of guided practice on this course.

SWIRL Tutorials

SWIRL is a package which is run through R and provides interactive tutorials. In this course we will make use of SWIRL to introduce students to the concepts they will work with in the labs. The SWIRL sessions are designed to be completed before  the lab sessions they are assigned to. SWIRL has a number of advantages to simply setting reading to prepare.

  • Firstly, it is interactive and so you will get to actually practice what is required, rather than simply read about it.
  • Second, the SWIRL tutorials provide a step by step guide which you can return to. If you forget how to do something in R, then go back and re-do the SWIRL tutorial to remind yourself.
  • Third, they are interactive, and so you can not be a passive learner.

You will be introduced to SWIRL and how to use it in the first couple of lab sessions. If you would like to read ahead on how to use SWIRL, you can do so here.


Problem Set and Lab topics will follow the content of lectures. See the Lectures section in this handbook and the Course Details page on LEARN.