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 |
This is a practical course in which students will learn the basics of conducting their own quantitative research using the SPSS statistical software package. It assumes a basic knowledge of statistical thinking and concentrates on issues of hypothesis testing and multivariate analysis, notably through the use of multiple and logistic regression.
Lectures are provided throughout the course in order to provide students with key information about the different statistical techniques covered by the course. However, the focus of teaching will be on 'learning through doing'. Online teaching materials are provided for each topic, allowing students to study at their own pace and to access detail of the different statistical techniques at a level with which they feel comfortable. These online materials are interactive, providing illustrations of the key research design issues involved in conducting quantitative research. They provide worked examples of the statistical techniques taught on the course, step-by-step examples of how to conduct your own analysis in SPSS and guidance as to how to interpret the results provided by the analysis.
In addition to the online materials and lectures, students attend a weekly microlab session in which they complete guided practicals to learn the use of the SPSS software package. These small group sessions, led by experienced quantitative researchers, provide a setting in which students can ask for further in depth advice on any techniques, or topics, they feel they do not fully understand.
The material covered on the course will help students develop a critical understanding of the pitfalls and strengths associated with the use of regression analysis in the social sciences.
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2017/18, Available to all students (SV1)
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Quota: 0 |
| 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) |
Students will submit a piece of coursework in which they demonstrate their knowledge of the statistical techniques taught throughout the course, and their proficiency with the software package SPSS. This coursework will involve running statistical analysis and reporting the results of that analysis for a series of guided statistical exercises. |
| Feedback |
Each set of online teaching materials, which students are required to engage with on a weekly basis, concludes with a quiz intended to test students understanding of the topics covered. While not providing part of the formal grading for the course, these quizzes provide students with guidance as to the strength of their understanding and help highlight areas where students might wish to engage in further study.
Written feedback will be provided to all students with regards to their final coursework submission. This will be returned within 15 working days. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand the links between theory and method, including the potential and limitations of quantitative evidence
- Understand the theoretical basis for making inferences about populations from samples
- Understand how to use computer software for inferential statistical analysis of large datasets
- Understand and apply simple and multivariate regression analyses with continuous and discrete data
- Communicate statistical evidence through graphs, tables and text
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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 | Dr Paul Norris
Tel: (0131 6)50 3922
Email: p.norris@ed.ac.uk |
Course secretary | Ms Nicole Develing-Bogdan
Tel: (0131 6)51 5067
Email: v1ndeve2@exseed.ed.ac.uk |
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