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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 Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe semester long course provides an advanced level overview of a variety of statistical analysis techniques and methodology issues relevant to psychological research. It is taught using a combination of lab and lecture sessions and focusses on techniques used by students following Masters programmes in Psychology and Linguistics and researchers practicing in these areas.
Course description R is a language and environment for statistical computing and graphics that is highly flexible and increasingly popular for statistical analysis. It provides a wide variety of statistical and graphical techniques, including facilities to produce well-designed publication-quality plots.

Design and analysis are taught under a unifying framework which shows a) how research problems and design should inform which specific statistical method to use and b) that all statistical methods are special cases of a more general model. This course focuses on situations in which 2 or more outcome variables are being studied simultaneously.

Typical Syllabus
- Linear Mixed Modelling (5 lectures)
- 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 Statistics (DENT11001) OR Univariate Statistics and Methodology using R (PSYL11053) OR Algebra (MATH10021)
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) Quota:  100
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Seminar/Tutorial Hours 22, Supervised Practical/Workshop/Studio Hours 20, Feedback/Feedforward Hours 22, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 12 )
Assessment (Further Info) Written Exam 0 %, Coursework 0 %, Practical Exam 100 %
Additional Information (Assessment) End of course assignment: a data analysis exercise (take home exam) 100%.

Page limit: 6 pages for the write-up (2 pages of text and 4 pages of tables/figures). You may include references which do not count towards the page allowance. The report should be written in a standard font, size 12, with standard 1 inch (2.54cm) margins on all sides.

R-code: You must submit the R-code used to produce the results presented in the report. This code must run without error. Any errors will result in a 10% deduction from the final grade. There is no limit for the R-code which will be submitted alongside the report.
Feedback Lab practicals that provide direct feedback on exercises and queries. Q&A sessions held once a week with course Teaching Assistants. Model answers for all lab and homework exercises.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. understand a variety of issues regarding the choice of statistical analysis techniques for standard and unusual data sets
  2. understand how to use the R language as a tool for data manipulation, analysis and graphics
  3. become adept in expressing statistical models typically used in psychological research and interpreting their results
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 Models:
Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. London: Sage. - Chapter 19 on 'Multilevel linear models'

Baayen, R. H. (2008). Analyzing linguistic data: A practical introduction to statistics using R. Cambridge: University Press.

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press.

Bates, D. M. (2010). lme4: Mixed-Effects Modeling with R. New York: Springer. Prepublication version at:

Faraway, J. (2005). Extending the Linear Model with R. Boca Raton: Chapman & Hall/CRC Texts.

Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390-412. doi: 10.1016/j.jml.2007.12.005

Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255-278. doi: 10.1016/j.jml.2012.11.001

Factor Analysis and Structural Equation Modeling:
Loehlin, J.C. (2004). Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis. Psychology Press.

Schmitt, T. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29, 304-321.
Revelle (online) Constructs, Components and Factor Models.

Reise, S. P., Waller, N. G., & Comrey, A. L. (2000). Factor analysis and scale revision. Psychological Assessment, 12, 287-297.

Mulaik, S.A. (2009) Foundations of Factor Analysis, 2nd Edition. Chapman Hall. Note: This book is excellent for a technical treatment of factor analysis.

R-package information for ¿factanal' and ¿umx¿ are useful, as these will be the primary packages used in the labs. There is also a paper describing the ¿umx¿ package:

Bates, Maes, and Neale (2017) umx: Twin and Path-based Structural Equation Modeling in R.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Luna De Ferrari
Tel: (0131 6)50 8285
Course secretaryMiss Toni Noble
Tel: (0131 6)51 3188
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