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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014 -
- ARCHIVE as at 1 September 2013 for reference only
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DRPS : Course Catalogue : School of Social and Political Science : Postgrad (School of Social and Political Studies)

Postgraduate Course: Core quantitative data analysis for social research: part 2 (PGSP11079)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Humanities and Social Science
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits10
Home subject areaPostgrad (School of Social and Political Studies) Other subject areaNone
Course website None Taught in Gaelic?No
Course descriptionThe 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Core quantitative data analysis for social research: part 1 (PGSP11078)
Co-requisites
Prohibited Combinations Other requirements A pass in part 1 (PGSP11078) or equivalent prior learning
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2013/14 Semester 1, Available to all students (SV1) Learn enabled:  Yes Quota:  None
Web Timetable Web Timetable
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 )
Additional Notes Course organiser now Ross Bond
Breakdown of Assessment Methods (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
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
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
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.
Study Abroad Not entered
Study Pattern Not entered
KeywordsNot entered
Contacts
Course organiserProf Vernon Gayle
Tel: (0131 6)50 4069
Email: Vernon.Gayle@ed.ac.uk
Course secretaryMr Andrew Macaulay
Tel: (0131 6)51 5067
Email: Andrew.Macaulay@ed.ac.uk
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