Data Analysis for Psychology in R 1 (dapR1) is your first step on the road to being a data, programming and applied statistics guru!
Overview of Structure
Students are expected to attend 3 hours per week of taught sessions:
- 1x 1hour lecture
- 1x 2hour lab
In addition students will be given:
- a weekly homework quiz (part of the assessment),
- assigned reading and
- there may be some work left over from your labs.
In total, it is anticipated that students spend approximately 5-6 hours a week on dapR1 work (inclusive of 3 compulsory hours). More information about how to structure your time can be found in the course Study Guide on LEARN.
This course provides a introduction to data, R and statistics. It is designed to work slowly through conceptual content that form the basis of understanding and working with data to perform statistical testing. At the same time, we will be introducing you to basic programming in R, covering the fundamentals of working with data, visualization and simple statistical tests. The overall aim of the course is to provide you with all the necessary skills to feel confident working with R and data, before we move on to discuss a broader array of statistical methods in year 2.
How to approach your research methods training
Work hard. Work across the year. Ask questions. You will do well.
It is really important that you establish good working patterns for dapR courses early. It will help you throughout your degree.
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.