Longitudinal Data Analysis
Date:
05/06/2023 - 06/06/2023
Organised by:
University College London
Presenter:
Dr Feifei Bu, Dr Eduardo Fe
Level:
Intermediate (some prior knowledge)
Contact:
Venue: Online
Description:
Course description
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.
Learning objectives
- 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 R and Stata
- Learn about extensions of the model such as non-linear change in time and the inclusion of time varying predictors.
Cost:
Free
Website and registration:
https://www.eventbrite.co.uk/e/longitudinal-data-analysis-tickets-486777493587
Region:
International
Keywords:
Quantitative Data Handling and Data Analysis, Longitudinal Data Analysis
Related publications and presentations:
Quantitative Data Handling and Data Analysis
Longitudinal Data Analysis