Postgraduate Course: Core quantitative data analysis 1 and 2 (SCIL11009)
Course Outline
| School | School of Social and Political Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Statistical methods are a key element of social science practice. This course offers an engaging and practical introduction for students will little or no prior experience of statistical methods. This course will provide students will the skills to understand and critically engage with statistically orientated social science research, and the capacity to undertake statistical analyses using real social science datasets.
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| Course description |
This course provides an applied and critical overview of the key principles of statistically orientated social science research. The course will provide a thorough grounding in inferential statistics, and introduces a variety of common statistical data analysis techniques. With a practical focus, the course equips students with the skills to manage real social science datasets, conduct independent statistical analyses, and effectively present their results.
The course is delivered via lectures and interactive computer lab sessions.
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2026/27, Available to all students (SV1)
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Quota: 100 |
| Course Start |
Semester 1 |
| Course Start Date |
21/09/2026 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
1) Short Written Assignment (35% of overall mark)
2) Long Written Assignment (65% of overall mark)
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| Feedback |
Written feedback will be provided on all assessed work within the required feedback window. There will be regular opportunities for formative feedback during the semester (e.g. during weekly computer lab sessions). |
| 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 and have a thorough grounding in exploratory and descriptive data analysis
- Understand how to use computer software for statistical analysis of large datasets
- Understand and apply simple and multiple regression analyses with continuous and discrete data
- collect, clean and analyse data and present the results to professional standards.
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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 |
| Additional Class Delivery Information |
Lectures in weeks 1-10, plus weekly computer-based workshops. These will be supported by on-line materials to complement each week and drop-in tutorials. |
| Keywords | quantitative methods exploration description inference |
Contacts
| Course organiser | Dr Roxanne Connelly
Tel:
Email: Roxanne.Connelly@ed.ac.uk |
Course secretary | Ms Maria Brichs
Tel: (0131 6)51 3205
Email: mbrichs@ed.ac.uk |
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