Undergraduate Course: Research Methods and Statistics 2 (PSYL10142)
|School||School of Philosophy, Psychology and Language Sciences
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||This course will focus on the general linear model (GLM) and discuss multiple regression, ANOVA as a linear model and data reduction methods.
This course builds on the knowledge acquired in Research Methods and Statistics, focusing on the linear model and data reduction methods. Specifically, the course will begin with a refresher of simple regression, and then build to discuss multiple regression including interactions, model evaluation and model building. The course will then discuss and demonstrate the equivalence of ANOVA and regression. The last section of the course will cover data reduction methods and cover the fundamentals of survey design, principal components analysis, and factor analysis.
The course is taught via a mix of lectures, labs, problem sets and homework. Students will be encouraged to participate in group discussions in all aspects of the course, but are especially encouraged to make use of office hours. The regular homework exercises will provide a means of tracking student development and be a source of regular formative feedback.
Information for Visiting Students
|Pre-requisites||Visiting Students welcome. This course teaches using the R statistical package. If you have no experience with R, please contact the course organizer to arrange access to tutorial materials to be completed before the start of the course.
|High Demand Course?
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Understand multiple regression for continuous outcomes including interactions, interpretation, model assumptions, and when models should be applied.
- Understand the relationship between ANOVA and regression under the general linear model and be able to implement basic coding schemes for categorical predictors.
- Understand the principles of scale construction and data reduction methods including interpretation, model assumptions and methods to assess them.
- Be able to run the above mentioned analyses in R.
- Be able to present and interpret the results of the above mentioned analyses.
|Graduate Attributes and Skills
|Course organiser||Dr Thomas Booth
Tel: (0131 6)50 8405