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 |
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| 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 package, to competence in the standard univariate methodology and analysis using R.
R is a language and environment for statistical computing and graphics, based on the S language. R is a flexible and increasingly popular package 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.
Typical Syllabus:
* Methods of science and refresher on descriptive
* Introduction to R statistics
* Refresher in inferential statistics including Hypothesis testing, Type I vs. Type II errors, p-values, power, correlation, chi-squares, linear regression
* Introduction to data analysis using R
* Multiple regression
* Multiple regression analysis using R
* The general linear model (GLM) including continuous DV models, including ANOVA, ANCOVA, mixed designs, Defries-Fulker analysis
* General linear models with continuous DVs in R
Lectures: Weeks 1, 3, 5, 7, 9 held in Old High School Lecture Theatre Wednesdays 9am to 11am
Labs: Weeks 2, 4, 6, 8, 10 held in 3.02 Appleton Tower. Students must choose one 1-hour session on either Friday 11-12, Friday 12-1 OR Thursday 12-1 |
Entry Requirements
| Pre-requisites |
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Co-requisites |
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| Prohibited Combinations |
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Other requirements |
None
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| Additional Costs |
None |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 1, Available to all students (SV1)
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WebCT enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | Lecture Weeks TBC in 2.14 Appleton Tower | 1-11 | | | 09:00 - 10:50 | | | | Central | Laboratory | Lab Weeks TBC in 3.02 Appleton Tower | 1-11 | | | | 12:10 - 13:00 | or 11:10 - 12:00or 12:10 - 13:00 |
| First Class |
Week 1, Wednesday, 09:00 - 10:50, Zone: Central. 2.14 Appleton Tower |
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
50% A number of short statistics exercises issued and completed during the course - usually 5.
50% Final end of course assignment: a data analysis exercise.
All components must be submitted in order to pass the course. |
| Please see Visiting Student Prospectus website for Visiting Student Assessment information |
Special Arrangements
| Not entered |
Contacts
| Course organiser |
Dr Antje Nuthmann
Tel: (0131 6)50 3459
Email: antje.nuthmann@ed.ac.uk |
Course secretary |
Miss Toni Noble
Tel: (0131 6)51 3188
Email: Toni.noble@ed.ac.uk |
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copyright 2010 The University of Edinburgh -
1 September 2010 6:37 am
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