Postgraduate Course: Core quantitative data analysis 1 and 2 (SCIL11009)
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 | 20 |
| 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 is divided into two free-standing, separately assessed 10-credit parts, although most students take the entire 20-credit course in one semester. Part 1 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. Part 2 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. |
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 | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
| Not being delivered |
Summary of Intended Learning Outcomes
By the end of the course students will:
1. Understand the links between theory and method and the potential and limits of quantitative evidence
2. Be able to understand and apply a range of quantitative methods
3. Know how to produce and interpret basic statistics, especially data in tables
4. Have a thorough grounding in descriptive and exploratory data analysis techniques
5. Understand the difference between correlation and causation
6. Have experience of working with large data sets
7. Have an understanding of the capabilities of computer software for statistical analysis
8. Understand statistical modelling and be capable of using SPSS to perform advanced statistical analysis
9. Be able to understand and apply simple and multiple linear regression analysis
10. Be able to understand and apply logistic regression analysis
11. Be able to fit and interpret models for categorical dependent variables |
Assessment Information
| Part 1 is assessed by means of a multiple choice exam. Part 2 is assessed by means of a take home exercise that requires students to analyze quantitative data from a variety of sources and report their findings. For those students taking both parts 1 and 2, the assessment marks in each will be aggregated to provide one overall mark. |
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
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 | Mr Ross Bond
Tel: (0131 6)50 3919
Email: |
Course secretary | Miss Keira Farrell
Tel: (0131 6)51 5067
Email: |
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