Postgraduate Course: Core quantitative data analysis for social research: part 1 (PGSP11078)
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 introduces key statistical ideas and methods for social and political research. It is designed for students who have little or no previous experience or knowledge of statistics, or even a phobia for numbers, or for those who feel they need a refresher course on the subject. 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.
The course focuses on exploratory and descriptive data analysis. It considers the theoretical basis for using numbers in social research and examines the production and interpretation of tables as a way of presenting empirical evidence. It introduces fundamental concepts and areas such as cases, variables and values; levels of measurement; the graphical representation of data; measures of central tendency and dispersion; and patterns of causality in three or more variables.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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| Delivery period: 2013/14 Semester 1, Available to all students (SV1)
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Learn enabled: Yes |
Quota: None |
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Web Timetable |
Web Timetable |
| Class Delivery Information |
Students intending to take Core Quantitative Data Analysis parts 1 AND 2 should register for the 20 credit course (SCIL11009). |
| Course Start Date |
16/09/2013 |
| Breakdown of 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 Notes |
Course organiser now Ross Bond
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| Breakdown of Assessment Methods (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| No Exam Information |
Summary of Intended Learning Outcomes
By the end of the course students will:
- Understand the links between theory and method and the potential and limits of quantitative evidence
- Be able to understand and apply a range of quantitative methods
- Know how to produce and interpret basic statistics, especially data in tables
- Have a thorough grounding in descriptive and exploratory data analysis techniques
- Understand the difference between correlation and causation
- Have experience of working with large data sets
- Have an understanding of the capabilities of computer software for statistical analysis
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Assessment Information
| One multiple choice exam. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Introduction to quantitative data analysis; Levels of measurement; Discrete and continuous variables
Summarising data: Measures of spread and central tendency; Presenting data in table and charts
Relationships between variables: correlation, association and causation; simple linear regression
Measures of association; Modelling nominal and ordinal variables
Relationships between more than two variables: controlling for a third variable
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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 | Prof Vernon Gayle
Tel: (0131 6)50 4069
Email: Vernon.Gayle@ed.ac.uk |
Course secretary | Mr Andrew Macaulay
Tel: (0131 6)51 5067
Email: Andrew.Macaulay@ed.ac.uk |
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© Copyright 2013 The University of Edinburgh - 13 January 2014 4:54 am
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