THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2020/2021

Information in the Degree Programme Tables may still be subject to change in response to Covid-19

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Psychology

Undergraduate Course: Research Methods and Statistics 2 (PSYL10126)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course will focus on the general linear model (GLM) and discuss multiple regression, ANOVA as a linear model and data reduction methods.
Course description 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Research Methods and Statistics (PPLS08001)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting students should be studying Psychology as their degree major, and have completed at least 3 Psychology courses at grade B or above. We will only consider University/College level courses.
This course teaches using the R statistical package. If you have no experience with R, please contact the course organiser to arrange access to tutorial materials to be completed before the start of the course. Applicants should note that, as with other popular courses, meeting the minimum does NOT guarantee admission.
**Please note that upper level Psychology courses are high-demand, meaning that they have a very high number of students wishing to enrol in a very limited number of spaces.** These enrolments are managed strictly by the Visiting Student Office, in line with the quotas allocated by the department, and all enquiries to enrol in these courses must be made through the CAHSS Visiting Student Office. It is not appropriate for students to contact the department directly to request additional spaces.
High Demand Course? Yes
Course Delivery Information
Academic year 2020/21, Available to all students (SV1) Quota:  0
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Quizzes: 25%
Report: 75%
Feedback 10 weekly marked homework exercises delivered via LEARN.
Weekly office hours with lecturers.
Weekly lab.
Weekly problem sets with answers provided.
Online Q&A
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. 1. Understand multiple regression for continuous outcomes including interactions, interpretation, model assumptions, and when models should be applied.
  2. 2. Understand the relationship between ANOVA and regression under the general linear model and be able to implement basic coding schemes for categorical predictors.
  3. 3. Understand the principles of scale construction and data reduction methods including interpretation, model assumptions and methods to assess them.
  4. 4. Use R to practically conduct the analytic methods taught in the course.
  5. 5. Present and interpret the results of the analytic methods taught in the course.
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
Keywordspsychology,statistics,research methods
Contacts
Course organiserDr Thomas Booth
Tel: (0131 6)50 8405
Email: Tom.Booth@ed.ac.uk
Course secretaryMs Alex MacAndrew
Tel: (0131 6)51 3733
Email: alexandra.macandrew@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Prospectuses
Important Information