Longitudinal data is essential for social and health research because it allows us to understand how people, organisations, and societies change, why events happen, and to support stronger causal inference.

In this course, you will learn both how to clean longitudinal data and the main statistical models used to analyse it. The course will cover three fundamental frameworks for analysing longitudinal data: multilevel modelling, structural equation modelling and event history analysis.

The course is organised as a mixture of lectures and hands-on practicals using real-world data. During the course, there will also be opportunities to discuss how to apply these models in your own research.

The weekly format is designed to allow you to complete the guided reading for each session and apply the techniques we cover to your own research. This learn-apply-review format gives you time to consolidate each method, use it with your own data, and resolve problems while support is still available. The follow-up data clinics will further help you apply these methods to your own research.

By the end of the course, you will be able to:
– Prepare and visualise longitudinal data in R
– Choose between multilevel, SEM and event-history models
– Fit and interpret the principal longitudinal approaches
– Recognise their assumptions and limitations
– Develop a defensible analysis plan for your own research

Schedule (09:00 to 16:00 UK time)
– Mon 12 Oct – Data cleaning and visualisation of longitudinal data
– Mon 19 Oct – Cross-lagged models (intro to SEM and autoregressive models)
– Mon 26 Oct – Multilevel model of change (intro to multilevel modelling)
– Mon 2 Nov – Latent Growth Modelling
– Mon 9 Nov – Survival models (event history analysis)

Learn more at: https://longitudinalanalysis.com/intro-long-analysis-course/