Postgraduate Course: Core quantitative data analysis for social research: part 2 (PGSP11079)
Course Outline
| School | School of Social and Political Science |
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 | Postgrad (School of Social and Political Studies) |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | The course builds on the key statistical ideas and methods for social and political research learned in part 1 (PGSP11078) or through equivalent prior learning. It explores principles of inference and the logic of obtaining empirical evidence about populations from samples; confidence intervals; hypothesis formulation and testing; elementary multivariate analysis; and linear and logistic regression. The emphasis is on learning and understanding by doing, using $ùreal&© data, rather than memorising formulae or rules of procedure. Each online learning module is supplemented by self-tests and activities to give students practice in the exploration and analysis of quantitative data using the SPSS software package, copies of which may also be provided free of charge to students for use on their own personal computers. In line with ESRC postgraduate research training guidelines, the aim of the course is to ensure that students are able to understand and use basic quantitative methods. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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| Delivery period: 2012/13 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 |
| No Classes have been defined for this Course |
| First Class |
First class information not currently available |
| No Exam Information |
Summary of Intended Learning Outcomes
By the end of the course students will:
&· Be able to understand and apply a range of quantitative methods
&· Have experience of working with large data sets
&· Have an understanding of the capabilities of computer software for statistical analysis
&· Understand statistical modelling and be capable of using SPSS to perform advanced statistical analysis
&· Be able to understand and apply simple and multiple linear regression analysis
&· Be able to understand and apply logistic regression analysis
&· Be able to fit and interpret models for categorical dependent variables
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Assessment Information
| A take home exercise that requires students to analyze quantitative data from a variety of sources and report their findings. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Probability; The normal distribution; Sampling and inference
Hypothesis formulation and testing for categorical variables
Multiple linear regression
Logistic regression
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| Transferable skills |
Not entered |
| Reading list |
Elliot J. and Marsh C. (2008) Exploring Data (2nd edition), Cambridge: Polity.
Fielding J. and Gilbert N. (2006) Understanding Social Statistics (2nd edition), London: Sage.
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| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr John Macinnes
Tel: (0131 6)50 3867
Email: |
Course secretary | Mrs Gillian Macdonald
Tel: (0131 6)51 3244
Email: |
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