Introduction to Longitudinal Data Analysis

Date: 18 February 2020
Duration: 1 day (9.45am-4.30pm)
Instructor: Maria Pampaka
Course 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. 

Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.


The course covers basic concepts in longitudinal design and analysis.

The morning session focuses on the strengths and methodological difficulties of the longitudinal approach such as defining longitudinal populations and target samples; levels and dimensions of change; age, period and cohort effects. There will be one small group discussion.

After lunch, we start with an overview session on the sources and causes of missing data (attrition, etc), and how to adjust for missingness; this will be followed by another group exercise.


By the end of the course, students should have gained:

  • an understanding of the different ways of measuring and explaining change using longitudinal data;
  • an appreciation of the particular problems posed by missing data in longitudinal research;
  • a basic understanding of ways of adjusting for missing data and
  • confidence to address questions about longitudinal design and missing data.


Students should have some background in empirical social science and a basic grounding in statistical modelling, at least in linear regression.

Recommended reading

  • Firebaugh, G. (1997) Analyzing Repeated Surveys. Thousand Oaks, Ca.: Sage.
  • Lynn, P. (ed.) (2009) Methodology of Longitudinal Studies. Chichester: Wiley.

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.