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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2021/2022

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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Psychology

Postgraduate Course: Multivariate Statistics and Methodology using R (PSYL11054)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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 lectures)
- Structural Equation Modelling
- Factor Analysis (1 lecture)
- Confirmatory Factor Analysis (1 lecture)
- Path Analysis & Structural Equation Modelling (3 lectures)
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Univariate Statistics and Methodology using R (PSYL11053)
Co-requisites
Prohibited Combinations Other requirements 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.
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2021/22, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 66, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 32 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 20% quizzes and 80% written report
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:
  1. conduct linear mixed effects models in R
  2. conduct data reduction in R
  3. 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
Keywordsmultivariate,statistics,techniques,methodology
Contacts
Course organiserDr Aja Murray
Tel: (0131 6)50 3455
Email: Aja.Murray@ed.ac.uk
Course secretaryMiss Toni Noble
Tel: (0131 6)51 3188
Email: Toni.noble@ed.ac.uk
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