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 |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | 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 'real' 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. |
| Course description |
Probability; The normal distribution; Sampling and inference
Hypothesis formulation and testing for categorical variables
Multiple linear regression
Logistic regression
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Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2014/15, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
78 )
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| Additional Information (Learning and Teaching) |
Course organiser now Ross Bond
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
A take home exercise that requires students to analyze quantitative data from a variety of sources and report their findings. |
| Feedback |
Not entered |
| No Exam Information |
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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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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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Prof Andrew Thompson
Tel: (0131 6)51 1562
Email: Andrew.Thompson@ed.ac.uk |
Course secretary | Mr Andrew Macaulay
Tel: (0131 6)51 5067
Email: Andrew.Macaulay@ed.ac.uk |
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