Postgraduate Course: Advanced statistical methods for psychology using R (PSYL11040)
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 | The course provides an advanced level examination of a variety of statistical analysis techniques and methodology issues relevant to psychological research. The course gives students an introduction to R, 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.
The environment is highly extensible, and is increasingly being adopted as the platform of choice for research in statistical methodology and in modern applied statistics in the social sciences, including psychology (see: Social Sciences and Graphics at http://cran.r-project.org/src/contrib/Views/).
Lecture sessions are designed to give students an overview and introduction to the individual methodologies. These will be supplemented by Lab sessions which will give students extensive practical experience of working with a highly interactive statistical package.
The course comprises ten seminar and lab sessions. A typical syllabus would include:
1. Research with children and special groups
2. Qualitative approaches to data analysis
3. Single case studies.
4. Introduction to the R statistical programming language: data manipulation and re-shaping.
5. Statistical graphics.
6. Descriptive and inferential statistics. (usually including t-tests, non-parametric tests, chi-squared and correlations).
7. Power analysis.
8. Modelling I: regression, ANOVA.
9. Modelling II: mixed-effects, SEM.
10.Exploratory factor analysis |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | Students should have a background in basic statistical techniques. |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
| Not being delivered |
Summary of Intended Learning Outcomes
1. Understand a variety of issues regarding the choice of statistical analysis techniques for standard and unusual data sets.
2. Understand how to use the R language as a tool for data manipulation, analysis and graphics.
3. Become adept in expressing statistical models typically used in psychological research and interpreting their results.
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Assessment Information
| A Data Analysis Exercise. This will comprise the analysis of a given set of data and presentation of results. The analysis should be performed and then written in the style of a results section of a journal article with commentary. Word limit: approximately 3000 words. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | |
Course secretary | Miss Toni Noble
Tel: (0131 6)51 3188
Email: Toni.noble@ed.ac.uk |
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