Research Methods and Statistics 2 (RMS2) is a third year course in statistical modelling. It will empower you with tools to analyse richer data and answer a broader set of research questions of interest to you.
Students are expected to have taken a first course in statistics and data analysis, such as RMS1, or any other course introducing the basics of plotting data, summarising data, and hypothesis testing (t-tests).
The course focuses on statistical methods as a tool to answering research questions. We will cover the linear model (regression) in detail, with a focus on demonstrating the equivalence of regression and ANOVA. We will also deal with some broader topics and practical issues in data analysis.
In RMS2, 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.
Students are expected to:
- Watch the video-lectures
- Study the corresponding slides
- Complete the linked activities
- Complete the practical exercises
- Attend drop-ins to discuss questions, or post their question on the discussion forum
In addition students will be given:
- a weekly homework quiz (part of the assessment)
- 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.
The assessment is 100% based on coursework. The 10 weeks of teaching will be split into:
- 1 practice quiz
- 9 graded quizzes (25% of final grade)
Note: only the 7 best scores out of the 9 quizzes will count towards the final grade.
Furthermore, there will be:
- 1 graded report (75% of final grade)
Dr Tom Booth (Course organiser)
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.