Longitudinal Data Analysis
Date: 19 February 2020
Instructor: Professor Ian Plewis
Fee: £195 (£140 for those from educational, government and charitable institutions).
CMI offers up to five subsidised places at a reduced rate of £60 per course day to research staff and students within Humanities at The University of Manchester. These places are awarded in order of application.
Humanities PGR students at The University of Manchester can apply for a methods@manchester bursary to help cover their costs. All applications will be considered on a case-by-case basis and applicants will be required to provide a supporting statement from their supervisor. Applications for bursaries must be submitted at least two weeks in advance of the course date; applications submitted after this time will not be accepted. Retrospective applications cannot be made if courses have already taken place or payment has already been made.
Please click here to make a booking. If you are applying for a subsidised place, select the £60 University of Manchester option on the booking form. For queries about methods@manchester bursaries, contact firstname.lastname@example.org (please note, you must have a confirmed place on the course before requesting a bursary application form). For any other queries about short courses, please contact email@example.com.
Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.
The course covers two of the most useful ways of analysing longitudinal data. In the morning we cover growth curve analysis within a multilevel modelling framework. The theoretical ideas are embellished with practical work using data from the National Child Development Study. After lunch, basic concepts in survival analysis and event history analysis are introduced followed by practical work with a simple (pencil and paper) example.
By the end of the course, students should have gained (i) an understanding of how growth curve models can be used to analyse repeated measures data; (ii) an appreciation of the ways in which duration and transition data can be analysed using techniques initially developed in medicine and industry; (iii) confidence to carry out practical work with some kinds of longitudinal data.
Students should have a strong background in empirical social science and a good understanding of the basics of statistical modelling, at least up to multiple linear regression. Some experience with STATA would be useful but not essential.
- Lynn, P. (ed.) (2009) Methodology of Longitudinal Studies. Chichester: Wiley.
- Singer, J. D. and Willett, J. B. (2003) Applied Longitudinal Data Analysis. New York: OUP.
About the instructor
Ian Plewis is Emeritus Professor of Social Statistics at The University of Manchester with a wealth of practical experience and theoretical knowledge of designing longitudinal studies and using longitudinal data.