Postgraduate Course: Multivariate Statistics and Methodology using R (PSYL11098)
Course Outline
| School | School of Philosophy, Psychology and Language Sciences |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This semester long course provides an advanced level overview of statistical analysis techniques and methodology issues relevant to psychological research.
The course builds on the concepts and skills developed in Univariate Statistics and Methodology using R.
It is taught using a combination of lab and lecture sessions and focuses on techniques used by students following Masters programmes in Psychology and Linguistics and researchers practising in these areas.
Course is open only to those students enrolled within the School of Philosophy, Psychology and Language Sciences (PPLS). Students outwith PPLS may contact the Course Organiser to query if any space is available after week 2. |
| Course description |
Typical Syllabus
- Linear Mixed Effects Modelling (5 weeks)
- Exploratory Factor Analysis (1 week)
- Confirmatory Factor Analysis (1 week)
- Path Analysis & Structural Equation Modelling (3 weeks)
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | It is RECOMMENDED that students have passed Univariate Statistics and Methodology using R (PSYL11099) |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: 110 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
156 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Quizzes 20%
Written Report 80%
|
| Feedback |
Formative feedback is provided throughout the course during discussions and guidance in practical sessions. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- conduct linear mixed effects models in R
- conduct data reduction in R
- conduct structural equation modeling in R
|
Reading List
Neither section of this course directly follows a single text. Below are a list of references which are indicative of content of the course.
Linear Mixed Effects Models:
Mirman, D. (2016). Growth curve analysis and visualization using R. CRC press.
Bates, D. M. (2010). lme4: Mixed-Effects Modeling with R. New York: Springer. Prepublication version at: http://lme4.r-forge.r-project.org/book/
Factor Analysis and Structural Equation Modeling:
Booth, Doumas, Murray (forthcoming). Data analysis for Psychology in R. Draft chapters on principal components analysis, exploratory factor analysis, structural equation modelling. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Special Arrangements |
Course is open only to those students enrolled within the School of Philosophy, Psychology and Language Sciences (PPLS). Students outwith PPLS may contact the Course Organiser to query if any space is available after week 2. |
| Keywords | multivariate,statistics,techniques,methodology |
Contacts
| Course organiser | Dr Aja Murray
Tel: (0131 6)50 3455
Email: Aja.Murray@ed.ac.uk |
Course secretary | Miss Mollie Fordyce
Tel:
Email: mfordyc2@ed.ac.uk |
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