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 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)
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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)
This course is only open to MSc students enrolled within the School of Philosophy, Psychology and Language Sciences (PPLS), with priority given to Psychology students and those students with ESRC funding. PhD students and students outwith PPLS may audit the course depending on space - please contact the Course Organiser and the PPLS Teaching organisation pplspgoffice@ed.ac.uk for permission to enrol. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2025/26, Available to all students (SV1)
|
Quota: 0 |
| 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%
Report (80%) - Five A4 pages (approx. 2000 words, including figures and tables)
|
| Feedback |
Summative feedback is available for all assessments. Written feedback is available for group based reports and weekly homework quizzes. Opportunities for formative feedback through weekly office hours, online forums, labs and lectures |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand and interpret analyses using generalised multilevel models for hierarchical data arising from cross sectional, longitudinal, and repeated measures designs.
- Understand and interpret data reduction techniques such as principal components analysis (PCA) and exploratory factor analysis (EFA).
- Understand the importance of measurement error and the implementation and interpretation of confirmatory factor analysis (CFA) and structural equation models (SEM).
- Understand the above referenced analyses when implemented in R and how results can be presented and interpreted.
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Reading List
Weekly open access readings will be provided via Learn each week. Examples can be found at https://uoepsy.github.io/lmm/ (please note these readings are updated annually).
Additional readings that contain a list of indicative topics include: - 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/ - 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 |
Core skills gained on this course: advanced programming/coding, data and statistical analysis/evaluation, written communication, report writing, independence, problem solving, learning from mistakes, argumentation (justify their point of view with evidence). |
| 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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