Alexandru Cernat and 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 and R.
There will be two pre-recorded lectures sessions and two 1.5-hour live computer practical sessions in R or Stata.
From 30 May 2023
Pre-recorded lectures available
Introduction to the multilevel model for change
Introduction to Latent Growth Model
Monday 5th of June
Session 1 in either Stata or R
Tuesday 6th of June
Session 2 in either Stata or R
This is a UKRI funded project offering rigorous training in longitudinal data science. Please note that this training is NOT available to undergraduate or masters students.