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||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.
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.
- Linear Mixed Modelling (5 lectures)
- Principal Components Analysis & Factor Analysis (3 lectures)
- Path Analysis (1 lecture)
- Confirmatory Factor Analysis & Structural Equation Modelling (2 lectures)
A detailed week by week syllabus will be provided prior to the start of the course.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2016/17, 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. Detailed reading lists for specific lectures will be given at the beginning of the course where necessary.|
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
Data Reduction & SEM:
Schmitt, T. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29, 304-321.
Revelle (In Prep.) Constructs, Components and Factor Models. Psychometric Theory. Chap.6:
Field, A., Miles, J. & Field, Z. (2012). Exploratory Factor Analysis. In Discovering Statistics using R. (Chapter 17) Note: For those of you who already have the field book, chapter 17 is OK to read. If you do not have this book, please do not buy it for the treatment of factor analysis!
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 but provides a technical treatment of factor analysis.
Child, D. (2006). The Essentials of Factor Analysis. 3rd Edition. Continuum. London. You may also wish to look at the R-package information for 'psych' and 'lavaan' as these will be the primary packages used in the labs. There is also a paper describing the 'lavaan' package:
Rosseel, Y. (2012). lavaan: An R Package for structural Equation Modeling. Journal of Statistical Software, 48. Available at http://www.jstatsoft.org/v48/i02/paper
|Graduate Attributes and Skills
|Course organiser||Dr Antje Nuthmann
Tel: (0131 6)50 3459
|Course secretary||Miss Toni Noble
Tel: (0131 6)51 3188
© Copyright 2016 The University of Edinburgh - 3 February 2017 5:11 am