Longitudinal Data Analysis
Date: 22 Feb 2019
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. In some instances, such as for unfunded PhD students, we may be able to offer free or bursary places.
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.