Undergraduate Course: Research Methods & Statistics 3 (PSYL10127)
Course Outline
| 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 |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course will cover a number of quantitative topics and their relation to the generalized linear model as well as covering qualitative methodology. The first 5 weeks of the course will focus on topics of scale construction and data reduction. 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. In the second 5 weeks, a variety of methodologies for qualitative analysis will be presented. |
| Course description |
This course is split into two halves covering quantitative methods for data reduction and scale development, and qualitative methodologies. In the quantitative aspect of the course, students will be introduced to a range of data reduction methods including cluster analysis, principal components analysis and factor analysis. We will discuss the use of these methods, factor analysis in particular, for scale development and discuss related issues of reliability and validity. In the qualitative section of the course, consideration will be given to differing approaches to psychological research, with a focus on qualitative study design and data, discursive analysis, and interpretative phenomenological analysis.
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.
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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? |
Yes |
Course Delivery Information
|
| Academic year 2017/18, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
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
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Homework: 10%
Reports: 40% (2x 20%)
Exam: 50%
|
| Feedback |
Weekly marked homework exercises delivered via LEARN.
Weekly office hours with lecturers.
Weekly problem sets with answers.
Online Q&A sessions.
Weekly lab.
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| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Research Methods & Statistics 3 | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand concepts in scale construction and assessment.
- Understand concepts of data reduction methods including interpretation, model assumptions and methods to assess them.
- Be able to run the above statistical tests in R and evaluate applications of these methods in published research.
- Understand the rationale underlying qualitative methodologies and know about various means of collecting and analyzing qualitative data.
- Be able to conduct qualitative analyses and evaluate applications of these methods in published research.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Sue Widdicombe
Tel: (0131 6)50 3411
Email: S.Widdicombe@ed.ac.uk |
Course secretary | Ms Catherine Renton
Tel: (0131 6)50 3602
Email: crenton2@ed.ac.uk |
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