Research topic: Modelling multivariate longitudinal data subject to dropout using latent variable models
Abstract: Longitudinal data are collected for studying changes across time. Studying many variables simultaneously across time (e.g. items from a questionnaire) is common when the interest is in measuring unobserved constructs such as democracy, happiness, fear of crime, social status, etc. The observed variables are used as indicators for the unobserved constructs of interest. Dropout is a common problem in longitudinal studies where subjects exit the study prematurely. Ignoring the dropout mechanism can lead to biased estimates, especially when the dropout is non-ignorable. Another possible type of missingness is item non-response where an individual chooses not to respond to a specific question. Our proposed approach uses latent variable models to capture the evolution of the latent phenomenon over time while accounting for dropout (possibly non-random), together with item non-response.
Supervisors: Professor Irini Moustaki / Dr Jouni Kuha