Undergraduate Course: Doing Social Research with Statistics (SSPS08007)
Course Outline
| School | School of Social and Political Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Statistical data analysis skills are crucial across the social sciences and in various professional fields because they enable us to systematically interpret complex data, identify patterns, and draw meaningful conclusions. In employment sectors ranging from finance and marketing to healthcare and the third sector, statistical data analysis informs decision-making and contributes to positive societal and organizational change. This course provides intermediate-level statistical data analysis skills and is aimed at students undertaking 'with Quantitative Methods' degree programs in SPS, but is open to those outwith these programs who have the required prerequisites. |
| Course description |
This course provides an applied overview of a variety of Generalized Linear Models used in the social sciences. The course has a practical focus and covers the key skills required throughout the process of conducting social research with statistics. This includes skills needed to: wrangle real social science data resources, handle complex survey designs in statistical analysis, effectively interpret regression models, produce automated tables of results, and present regression results effectively for various audiences.
The course offers hands-on experience in undertaking research with real social science data, culminating in a research project on a topic of your choice for your final assessment. Principles of open social science are central to this course, and you will be introduced to skills promoting research transparency and reproducibility.
The course is delivered via lectures and interactive computer lab sessions.
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Course Delivery Information
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| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: 48 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 11,
Seminar/Tutorial Hours 22,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
163 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
1) 70% A short research report on a substantive topic containing the application of binary logistic regression analysis. The report should be no more than 2000 words long.
2) 20% A short take home practical exercise.
3) 10% An annotated Stata .do file which contains all the code required to reproduce the analysis presented in the report. There is no word limit for the .do file, but parsimony is encouraged. |
| Feedback |
Students will receive feedback on an analysis project to be submitted in late March. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Use the statistical data analysis package Stata to effectively and efficiently analyse social science data resources.
- Appropriately undertake analyses using regression models for linear and binary dependent variables (e.g. linear regression and binary logistic regression).
- Appreciate the core differences between linear regression models and binary logistic regression models.
- Accurately interpret analyses of regression models.
- Effectively report analyses of regression models
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Reading List
Indicative Readings
Acock, A.C., 2016. A gentle introduction to Stata., College Station, Texas: Stata Press.
Kohler, U. and Kreuter, F., 2009. Data Analysis Using Stata. College Station Texas: Stata Press.
Long, J.S. 2009. The Workflow of Data Analysis Using Stata. College Station Texas: Stata Press.
Long, J.S. & Freese, J., 2014. Regression models for categorical dependent variables using Stata., College Station, Texas: Stata Press.
Mehmetoglu, M. and Jakobsen, T.G., 2016. Applied statistics using Stata: a guide for the social sciences. London: Sage.
Treiman, D. J., 2009. Quantitative Data Analysis: Doing Social Research to Test Ideas. San Francisco: Jossey-Bass.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Roxanne Connelly
Tel:
Email: Roxanne.Connelly@ed.ac.uk |
Course secretary | Mr Ian McClory
Tel: (0131 6)50 3932
Email: Ian.McClory@ed.ac.uk |
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