Postgraduate Course: Longitudinal Data Analysis (PGSP11487)
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 | There is now a great deal of longitudinal data available to social science researchers and many questions, particularly those that consider temporal relationships, are better addressed using longitudinal rather than cross-sectional data. This course is designed to introduce students to a variety of statistical approaches to analyzing longitudinal data. The course will have a practical focus and introduce students to analyzing existing large-scale household panel datasets. These datasets will include the British Household Panel Survey and Understanding Society (the UK Household Longitudinal Survey). The course will be taught using Stata software. |
| Course description |
Topics covered
1. Introduction to longitudinal data and longitudinal data analyses
2. Examples of existing longitudinal datasets (e.g. the British Household Panel Survey; Understanding Society the UK Household Longitudinal Study)
3. Approaches to longitudinal data analysis (e.g. repeated cross-sectional analysis; cohort analysis; panel modelling; duration analysis; dynamic models)
4. Managing longitudinal social survey data analysis (e.g. understanding the workflow)
5. Using Stata software to analyze longitudinal data
6. Exploring existing longitudinal data
7. Modelling longitudinal data
8. Interpreting results from longitudinal data analyses
9. Presenting results from longitudinal data analyses
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Students should have completed Statistical Modelling in the Social Sciences (PGSP11486). |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: 20 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
196 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Online Assessment (100%)
The online assessment will include the production of Stata syntax and test data enabling skills. These elements will account for 5% and 20% of the assessment.
There will be a set of short answer questions relating to longitudinal data analysis. This will account for 40% of the marks. This section will examine the postgraduate student's understanding of the principal theories and key concepts associated with longitudinal data analysis. Students will be required to interpret analyses of longitudinal data.
The assessment will also include a critical evaluation of a set of advanced longitudinal data analyses that apply an advanced statistical technique and designing and planning a replication study. This section of the assessment will account for 35% of the assessment.
This part of the assessment will show that the student has substantial autonomy and initiative in the organisation and management of large and complex longitudinal datasets. It will allow students to critically assess the suitability of variables and measures within complex longitudinal datasets for social science research. It will test their ability to creatively and independently plan and design a study using existing longitudinal data and creatively cast scientific questions in longitudinal terms.
Overall, the assessment will allow the student to demonstrate their overall understanding of longitudinal data analysis. |
| Feedback |
Verbal feedback will be given throughout the course.
Written feedback will be given on the assignments.
A completed and annotated version of the test will be published on Learn.
Informal one-to one meetings will be made available during the data enabling phase.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Exercise substantial autonomy and initiative in the organisation and management of large and complex longitudinal datasets
- Critically assess the suitability of variables and measures within complex longitudinal datasets for social science research
- Creatively and independently plan and design a study using existing longitudinal data
- Undertake critical analysis of longitudinal data using statistical models
- Creatively cast scientific questions in longitudinal terms and interpret and report related analyses of longitudinal data
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Reading List
Core Text: Gayle, V. and Lambert, P. 2020. Quantitative Longitudinal Data Analysis. London: Bloomsbury Press. ISBN9781350188853
Davies, R.B. 1994. From Cross-Sectional to Longitudinal Analysis¿, in Analyzing Social and Political Change. Edited by A. Dale and R.B. Davies. London: Sage. ISBN: 0803982984. (An excellent chapter in excellent book).
Kohler, U. and Kreuter, F. 2009. Data Analysis Using Stata (Second Edition). College-Station Texas: Stata Press. ISBN 9781597180467. (A very good book, ideal for students working in Stata).
Long, J.S. 2009. The Workflow of Data Analysis Using Stata. College-Station Texas: Stata Press. ISBN 9781597180474. (A great book on the practice of data analysis and data management).
Longhi, S., & Nandi, A. 2014. A practical guide to using panel data. Sage. ISBN-10: 1446210871. (A stellar tome!!).
Skrondal, A. and Rabe-Hesketh, S. 2004. Generalized Latent Variable Modelling: Multilevel, Longitudinal and Structural Equations Models. New York: Chapman and Hall. ISBN: 1-58488-000-7. (A very advanced, dense text which summarizes a wide array of statistical models which may be used for longitudinal analyses, highlighting the technical connections between them).
Singer, J.D. and Willett, J.B. 2003. Applied Longitudinal Data Analysis: Modelling change and event occurrence. New York: Oxford University Press. ISBN: 0-19-515296-4. (Wide coverage illustrating a selection of relatively advanced analytical strategies, although with less applied guidance than the title might suggest).
Taris, T. W. 2000. A Primer in Longitudinal Data Analysis. London: Sage. ISBN: 0761960260. (Excellent accessible explanations of many panel analysis methods)
Treiman, D. J. 2009. Quantitative Data Analysis ¿ Doing Social Research to Test Ideas. San Francisco: Jossey-Bass. ISBN: 9780470380031. (Overall an excellent book).
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Additional Information
| Graduate Attributes and Skills |
Generic cognitive skills (e.g. evaluation, critical analysis).
Communication, numeracy and IT skills.
Autonomy, accountability and working with others.
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| Keywords | Not entered |
Contacts
| Course organiser | Prof Vernon Gayle
Tel: (0131 6)50 4069
Email: Vernon.Gayle@ed.ac.uk |
Course secretary | Mrs Casey Behringer
Tel: (0131 6)50 2456
Email: Casey.behringer@ed.ac.uk |
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