Undergraduate Course: Research Methods and Statistics 2 (PSYL10126)
|School||School of Philosophy, Psychology and Language Sciences
||College||College of Humanities and Social Science
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||This course will focus on the generalized linear model (GLM). The course will include a refresher of material from RMS1 and broadly cover:
- ANOVA as a linear model
- linear models for non-continuous outcomes
- Assumption checking, model selection and model evaluation
- As time allows, advanced topics in linear modelling.
Emphasis will be placed on how and when these different models should be applied to psychological data and how to run such models using R.
This course builds on the knowledge acquired in Research Methods and Statistics, focussing on the linear model and extensions into the generalized linear model. Specifically, the course will begin with a refresher of simple regression, and then build to consider many aspects of multiple regression including interactions, model evaluation and model building. Importantly, throughout the course it will be shown how ANOVA and regression are equivalent models. In the latter stages of the course we will consider generalized linear models with outcome variables which are binary or count.
The course is taught via a mix of large group lectures, smaller group 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||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
|Academic year 2018/19, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||10 weekly marked homework exercises delivered via LEARN.
Weekly office hours with lecturers.
Weekly problem sets with answers provided.
||Hours & Minutes
|Main Exam Diet S1 (December)||2:00|
|Resit Exam Diet (August)||Research Methods and Statistics 2||2:00|
On completion of this course, the student will be able to:
- Understand multiple regression for different forms of outcome variables including interpretation and model assumptions and methods to assess them; when these models should be applied.
- Understand the relationship between ANOVA and regression under the general linear model framework; and how to specify ANOVA designs as regression models including understanding of appropriate coding schemes (dummy, effect & contrasts).
- Understand, estimate and interpret continuous-continuous and continuous-categorical interactions.
- Have a conceptual understanding of some advanced issues in quantitative analysis (e.g. non-parametric methods; transformations; missing data).
- Be able to run the above statistical tests in R, present and interpret the findings.
|Graduate Attributes and Skills
|Course organiser||Dr Thomas Booth
Tel: (0131 6)50 8405
|Course secretary||Ms Alexandra MacAndrew
Tel: (0131 6)51 3733