Postgraduate Course: Univariate Statistics and Methodology using R (PSYL11053)
Course Outline
| School | School of Philosophy, Psychology and Language Sciences |
College | College of Humanities and Social Science |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Psychology |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | This semester long course is taught using a combination of lab and lecture sessions and is suitable for students following Masters programmes in Psychology and Linguistics It takes students from introduction to basic statistics and an introduction to the basics of the R programme, to competence in the standard univariate methodology and analysis using R.
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 statistical method to use and b) that all statistical methods are special cases of a more general model. The course concentrates on research designs and analysis for problems in which there is a single outcome variable and would be taught using the general linear model as a framework to design and analysis.
The course is co-taught between Dr Tom Booth and Dr Martin Corley.
Formative feedback;
- The lab practicals will provide direct feedback on exercises and queries.
- Q&A sessions held once a week with course TAs.
- Model answers will be provided for all lab and homework exercises. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | No |
Course Delivery Information
|
| Delivery period: 2014/15 Semester 1, Available to all students (SV1)
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Learn enabled: Yes |
Quota: 100 |
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Web Timetable |
Web Timetable |
| Course Start Date |
15/09/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 20,
Feedback/Feedforward Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
48 )
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| Additional Notes |
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| Breakdown of Assessment Methods (Further Info) |
Written Exam
0 %,
Coursework
0 %,
Practical Exam
100 %
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| No Exam Information |
Summary of Intended Learning Outcomes
- Students should know which statistical analyses are appropriate to the research design of particular studies.
- Understand how a common framework unifies seemingly disparate data analysis methods.
- Use the R statistical package to analyze real data, be able to interpret the outputs, and create any required graphs.
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Assessment Information
End of course assignment: a data analysis exercise (take home exam) 100%
Assignment deadline: Monday 19th January 2015, 12 noon
Page limit: 6 pages for the write-up (4 pages of text and 2 pages of tables/figures). The report should be written in a standard font, size 12, with standard 1 inch (2.54cm) margins on all sides. There is no limit for the R-code which will be submitted alongside the report.
Return Date: 10th February 2015
Throughout the course a series of homework exercises will be set. These do not form part of the final grade but we strongly advised that these are completed as part of progress monitoring. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Typical Syllabus:
* Introduction to the use of statistical methods in research.
* Introduction to R for statistics
* Refresher in inferential statistics including Hypothesis testing, Type I vs. Type II errors, p-values, power, correlation, chi-squares.
* Linear regression: including diagnostics, transformation, different families of models.
* Multiple regression: extending linear regression to multiple IVs and including interactions, effects coding.
* The general linear model (GLM) as an inclusive framework (including ANOVA, ANCOVA, mixed designs). |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Thomas Booth
Tel: (0131 6)50 8405
Email: Tom.Booth@ed.ac.uk |
Course secretary | Miss Toni Noble
Tel: (0131 6)51 3188
Email: Toni.noble@ed.ac.uk |
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