Data Science Intermediate

Longitudinal Data Analysis

Feifei Bu

Longitudinal data (data collected multiple times from the same cases) is becoming increasingly popular due to the important insights it can bring us. For example, it can be used to track how individuals change in time and what are the causes of change, it can also be used to understand causal relationships or used as part of impact evaluation. Unfortunately, traditional models such as OLS regression are not appropriate as repeated measures are nested within individuals. For this reason, specialised statistical models are needed.

Multilevel Modelling (MLM) and Structural Equation Modelling (SEM) offer flexible frameworks in which longitudinal data can be analysed. They offer a series of advantages compared to other approaches such as: the separation of within and between variation, the inclusion of both time constant and time varying variables, the inclusion of multiple relationships (path analysis, mediation, etc.), the inclusion of measurement error, the estimation of change in measurement error, multi-group analysis, etc.

The course will give an introduction to the Multilevel Model for change and the Latent Growth Model (LGM) using the Stata.

  • Understand how multilevel and latent growth models can be used to model change in time
  • Understand the similarities and differences between MLM and LGM
  • Estimate change in time using Stata
  • Learn about extensions of the model such as non-linear change in time and the inclusion of time varying predictors.

There are two pre-recorded lectures, each accompanied by a computer practical task in Stata with solutions.

  • Good knowledge of regression modelling
  • Good understanding of longitudinal data
  • Some knowledge of Stata or another statistical package