Longitudinal Data Analysis
Module title | Longitudinal Data Analysis |
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Module code | SSIM913 |
Academic year | 2023/4 |
Credits | 15 |
Module staff | Dr Nitzan Peri-Rotem (Convenor) |
Duration: Term | 1 | 2 | 3 |
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Duration: Weeks | 11 |
Number students taking module (anticipated) | 20 |
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Module description
This module introduces applied methods in longitudinal data analysis, where the same individuals are observed multiple times. You will learn about major data sources from the UK and other countries used for longitudinal studies. You will also obtain practical knowledge of key concepts in longitudinal methods and the strengths and weaknesses of this approach. The second part of the module will focus on event history analysis, which is used in various areas of social and biomedical sciences. This module includes hands-on lab sessions, using STATA software, to gain practical experience in implementing longitudinal methods. Students taking this module should be familiar with basic techniques of regression analysis.
Module aims - intentions of the module
This module will equip you with an understanding and practical knowledge of longitudinal data analysis and its applications. It will provide you with tools to critically assess existing research that uses longitudinal analysis and to carry out independent research. In addition, you will gain experience in using Stata software for advanced data analysis.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Develop detailed understanding of key concepts, principles and uses of longitudinal methods;
- 2. Understand the advantages and limitations of longitudinal techniques;
- 3. Critically assess empirical studies that employ longitudinal methods;
- 4. Gain knowledge of methodological applications of life course analysis.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 5. Become familiar with applied research methods for policy evaluation;
- 6. Develop understanding of principles of research design, causal inference and data quality;
- 7. Develop understanding of a range of advanced quantitative methods.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 8. Become familiar with a range of data analysis techniques;
- 9. Apply quantitative research methods in your own research.
Syllabus plan
Whilst the module’s precise content and order of syllabus coverage may vary, it is envisaged that it will include the following topics:
- Introduction to longitudinal data analysis and its applications.
- Testing assumptions in panel data analysis.
- Dealing with attrition and missing data.
- Fixed and random-effects models.
- Event history (Survival) models: What they are and when to use them.
- Survivor and hazard functions
I. Kaplan-Meier estimators
II. Life tables
- Continuous time models
- Discrete time models
- Using time-varying predictors
- Complex models: repeated events and competing risks models.
- Testing for unobserved heterogeneity (frailty models).
Learning activities and teaching methods (given in hours of study time)
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
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22 | 128 | 0 |
Details of learning activities and teaching methods
Category | Hours of study time | Description |
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Scheduled Learning and Teaching | 22 | 11x 2 hours of lectures, seminars and practical labs - lectures cover the main concepts and data analysis skills of the course. |
Guided Independent Study | 38 | Reading and preparing for seminars (around 4-6 hours per week); |
Guided Independent Study | 90 | researching and writing assessments and assignments (researching, planning and writing the course work). |
Formative assessment
Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Research plan | 500 words | 1-9 | Written feedback |
Summative assessment (% of credit)
Coursework | Written exams | Practical exams |
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100 | 0 | 0 |
Details of summative assessment
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Research report | 100 | 3,000 words plus tables, graphs based on data analysis | 1-9 | Written feedback |
Details of re-assessment (where required by referral or deferral)
Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
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3,000 word research report | 3,000 word research report | 1-9 | Referral/Deferral period |
Re-assessment notes
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to submit a further assessment as necessary. The mark given for a re-assessment taken as a result of referral will be capped at 50%.
Indicative learning resources - Basic reading
Basic reading:
Allison, P. D. (2014). Event History and Survival Analysis: Regression for Longitudinal Event Data (2nd Edition). Thousand Oaks, CA: Sage Publications Inc.
Berrington, A. (2004) Perpetual postponers? Women's, men’s and couple’s fertility intentions and subsequent fertility behaviour. Population Trends, 117: 9-19.
Box-Steffensmeier, J. & Jones, B. S. (2004). Event History Modeling: A Guide for Social Scientists. Cambridge University Press.
Ermisch, J. & Francesconi, M. (2013). The Effect of Parental Employment on Child Schooling. Journal of Applied Econometrics, 28: 796–822.
Hsiao, C. (2003). Analysis of Panel Data (2nd Edition). Cambridge University Press.
Kaplan, A. & Stier, H. (2017). Political economy of family life: couple’s earnings, welfare regime and union dissolution. Social Science Research, 61: 43-56.
Kristman, V. L., Manno, M., & Côté, P. (2005). Methods to account for attrition in longitudinal data: Do they work? A simulation study. European Journal of Epidemiology, 20 (8), 657–662.
Singer, J. D. & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.
Indicative learning resources - Web based and electronic resources
UK Data Services - https://www.ukdataservice.ac.uk
Generations and Gender Programme - http://www.ggp-i.org/
ELE – College to provide hyperlink to appropriate pages
Indicative learning resources - Other resources
There are a range of data sets that will be used in the course:
British Household Panel Survey: Waves 1-18, 1991-2009
Understanding Society: Waves 1-6, 2009-2015
GGP: Waves 1-2.
Credit value | 15 |
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Module ECTS | 7.5 |
Module pre-requisites | Quantitative Data Analysis |
Module co-requisites | None |
NQF level (module) | 7 |
Available as distance learning? | No |
Origin date | 22/06/2017 |
Last revision date | 07/09/2022 |