Overview

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. 

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

This course is specifically designed to enhance knowledge, self-confidence and expertise among researchers who wish to develop their skills in analysing complex longitudinal biosocial data. It makes key concepts and approaches accessible to quantitative researchers from a wide range of disciplines and sectors, with particular emphasis on how these can be practically applied in their work. It is part of the RADIANCE programme, which has been developed by an expert team from UCL and the University of Manchester to offer comprehensive, state-of-the-art longitudinal data science training all in one place.

Who this course is for

This course is aimed at quantitative researchers working with biomedical and social data who wish to use Longitudinal Data Analysis in their research and would like a short introduction to this topic.

Course content

This course provides an insight to the following topics:    

• An introduction to the Multilevel Model for change and the latent growth model using Stata.
• Understand the key elements of Multilevel Modelling (MLM and Structural Equation Modelling (SEM) and how these can be used for analysing longitudinal data.
• How to use longitudinal data to understand causal relationships or used as part of impact evaluation.

Teaching and structure

The course consists of 2 pre-recorded lectures, 1 computer practical (in Stata) and solutions. Participants will need to be familiar with the use Stata.

Certificates

A certificate of completion will be available to download once the required activities have been completed.

Learning outcomes

This course will enable participants to: 

• 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.

Cost and concessions

The standard fee for this course is £100.

A 10% discount is available for UCL students and staff – please email radiance@ucl.ac.uk for details.

Course team

FeiFei Bu

FeiFei is Senior Research Fellow in Statistics and Epidemiology at UCL. Feifei’s research focuses on the impact of social factors on health and wellbeing. She is experienced in using administrative health and social care data. She has a strong interest in statistical methods, including panel data analysis, multilevel modelling, survival analysis, and structural equation modelling.

Course information last modified: 31 Jan 2024, 14:40