Postgraduate Course: Multivariate Statistics and Methodology using R (PSYL11054)
Course Outline
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 |
SCQF Credits | 10 |
ECTS Credits | 5 |
Summary | The 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)
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Information for Visiting Students
Pre-requisites | None |
High Demand Course? |
Yes |
Course Delivery Information
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Academic year 2017/18, Available to all students (SV1)
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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 )
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Assessment (Further Info) |
Written Exam
0 %,
Coursework
0 %,
Practical Exam
100 %
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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:
- 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
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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 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 |
Additional Information
Graduate Attributes and Skills |
Not entered |
Keywords | Not entered |
Contacts
Course organiser | Dr Luna De Ferrari
Tel: (0131 6)50 8285
Email: luna.deferrari@ed.ac.uk |
Course secretary | Miss Toni Noble
Tel: (0131 6)51 3188
Email: Toni.noble@ed.ac.uk |
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