Longitudinal Data Analysis

RADIANCE courses are targeted to the broad community of UK data scientists working in public health. They include epidemiologists, clinicians, data engineers/informaticians, statisticians, as well as quantitative researchers from other disciplines (e.g. psychology, social sciences, health economics).They may be from academia, charities, government departments and non-profitable organisations.

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

Lecturers Alexandru CernatFeifei BuCourse descriptionLongitudinal 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.Course StructureThere will be two pre-recorded lectures sessions and two 1.5-hour live computer practical sessions in R or Stata.

Pre-requisites:-	Good knowledge of regression modelling-	Good understanding of longitudinal data-	Good knowledge of Stata/RMore informationThis 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.

Longitudinal Data Analysis

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Longitudinal Data Analysis

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