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DRPS : Course Catalogue : School of Social and Political Science : School (School of Social and Political Studies)

Undergraduate Course: Researching Contemporary Britain using Longitudinal Data (SSPS10028)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThere is now a great deal of household panel data available to social science researchers. This course is designed to introduce students to a variety of statistical approaches to analyzing household panel 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 The course is delivered via interactive lab sessions and individual feedback. The course will be taught using the "cookery school" approach i.e. a short demonstration by the member of staff is followed by a hands-on practical attempt from the student. In medicine this teaching technique is known as "see one - do one".

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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Statistical Modelling (SSPS10027)
Prohibited Combinations Other requirements For those students who are required to take a Quantitative Methods course as part of their degree programme, this course can be counted towards that condition.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) Quota:  25
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 10, Supervised Practical/Workshop/Studio Hours 10, Feedback/Feedforward Hours 1, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 175 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 25% An annotated Stata syntax (.do) file which fully replicates the analysis of the research report. There is no limit for the length of the .do file, but parsimony and elegance of programming will be encouraged.

75 % A short research report on a substantive topic (following from their research plan) containing the application of a suitable advanced statistical technique The overall report should be about 6 pages long and include 2,000 words (plus or minus 10%) that accompany the statistical output. This may appear shorter than a standard (e.g. theoretical) essay on a 4th year course, however the emphasis is on the application of an advance statistical technique.

The report will mainly comprise of tables of summary information and statistical modelling outputs. Students will be expected to employ an advanced technique for example a panel data regression model or a hazard model.

The report will include

i. A substantive introduction
ii. Descriptive statistics
iii. An appropriate multivariate analysis (e.g. a random effects model)
iv. A set of substantive conclusions
Feedback Students will be encouraged to submit a research plan (500 words) using the British Household Panel Survey data or a similar data resource.

Students will receive formative feedback on their plan.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Organize and manage large and complex longitudinal datasets
  2. Assess the suitability of variables and measures within complex longitudinal datasets for social science research
  3. Plan and design a study using existing longitudinal data
  4. Undertake analysis of longitudinal data using statistical models
  5. Interpret and report analyses of longitudinal data
Reading List
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).
Additional Information
Graduate Attributes and Skills By the end of the course students should have strengthened their skills in general numeracy, data management, elementary software programming and time-management.
KeywordsNot entered
Course organiserProf Vernon Gayle
Tel: (0131 6)50 4069
Course secretaryMr Euan Morse
Tel: 0131 (6)51 1137
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