RMS1

Home / RMS1

RMS1

Overview of Structure

Students are expected to attend 3 hours per week of taught sessions: 

  • 2x 1hour lecture
  • 1x 1hour lab 

In addition students will be given:

  • a guided practical tutorial (SWIRL; independent study),
  • a weekly homework quiz (part of the assessment),
  • a problem set (independent study) and
  • assigned reading.

In total, it is anticipated that students spend approximately 7.5 hours a week on RMS work (inclusive of 3 compulsory hours). More information about how to structure your time can be found in the course Study Guide on LEARN.

Content overview

The course provides a thorough grounding in the basics of probability and statistical data analysis for psychologists. Importantly, the course focuses on statistical methods as a tool to answering research questions. We will cover the basics of probability and probability distributions; fundamentals of statistical hypothesis testing; and core statistical tools including chi-square, t-tests, correlation and simple linear regression models.

In RMS, we make use of the R statistical programming environment for all labs. Practically, this will develop not only students’ ability to conduct statistical analyses, but also provide some basic programming skills. Thus, students will develop a suite of highly transferable knowledge and skills.

How to approach your research methods training

Work hard. Work across the year. Ask questions. You will do well.

The research methods and statistics courses have been structured to encourage continual engagement, based on the fact that these types of skills are best acquired through regular practice. We strongly encourage students to work together week by week in labs, problem sets, and working through lecture material, and to make use of the office hours provided. These are your chance to come and clarify any points of misunderstanding with the teaching team while it is all still fresh in your mind. Working with friends, sharing ideas, explaining concepts, and discussing with the teaching team are all highly effective tools for learning. We hope we can build an atmosphere that will encourage these things.