Postgraduate Course: Multivariate Statistics and Methodology using R (PSYL11054)
|School||School of Philosophy, Psychology and Language Sciences
||College||College of Humanities and Social Science
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|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 (http://www.drps.ed.ac.uk/current/dpt/cxpsyl11053.htm)
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.
The ability to choose and execute appropriate statistical analyses, along the reciprocal skill of design studies so that they provide the data needed to test hypotheses is core to modern research in complex phenomena, such as those studied in Psychology and in Linguistics.
The R language and environment supports these statistical computations and graphics in ways that are leading edge, highly flexible and in extremely high and growing demand.
Multivariate Statistics and Methodology using R uses a problem-oriented approach to introduce analysis tools that extend to cases where multiple outcome variables are being studied simultaneously, and cases where the data contain a structure, e.g. children nested in classes, in schools, or more complex cases.
- Linear Mixed Modelling (5 lectures)
- Structural Equation Modelling
- Factor Analysis (1 lecture)
- Confirmatory Factor Analysis (1 lecture)
- Path Analysis & Structural Equation Modelling (3 lectures)
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2018/19, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|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.
||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
On completion of this course, the student will be able to:
- understand a variety of issues regarding the choice of statistical analysis techniques for standard and unusual data sets
- understand how to use the R language as a tool for data manipulation, analysis and graphics
- become adept in expressing statistical models typically used in psychological research and interpreting their results
|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: http://lme4.r-forge.r-project.org/book/
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. http://www.personality-project.org/revelle/syllabi/405/405factoranalysis.pdf
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. https://peerj.com/preprints/3354
|Graduate Attributes and Skills
|Course organiser||Prof Timothy Bates
Tel: (0131 6)51 1945
|Course secretary||Miss Toni Noble
Tel: (0131 6)51 3188