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| Module Availability |
| Autumn semester, second year |
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| Assessment Pattern |
| Two analysis exercises. |
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| Module Overview |
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| Prerequisites/Co-requisites |
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| Module Aims |
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| Learning Outcomes |
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| Module Content |
Course Syllabus
- Description, definition of, and assumptions regarding statistical models
- The General Linear Model
- Models for continuous response (multiple regression)
- Models for a binary response (logistic regression)
- Models for a categorical response
- Implications for modelling when data are consored, hierarchical or from longidudinal studies.
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| Methods of Teaching/Learning |
By the end of the module the students should:
Be familiar with the rationale and terminology of statistical modelling and to understand the logic of model development and testing.
Be able to develop multiple regression, ligistic regression and multinomial logistic regression models using SPSS and to interpret the results
To be familiar with other modelling techniques such as survival analysis and ultilevel modelling
Tobe aware of other statistical modelling software packages.
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| Selected Texts/Journals |
Tarling, R (2008) Statistical Modelling for Social Researchers, Principles and Practice, Routlege. |
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| Last Updated |
| 27th August 2009 |
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