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 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 |
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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
| Not being delivered |
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
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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 |
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). |
| 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: |
Course secretary | Miss Mollie Fordyce
Tel:
Email: |
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