Introduction to Longitudinal Data Analysis

Longitudinal data is essential in a number of research fields as it enables analysts to concurrently understand aggregate and individual level change in time, the occurrence of events and improves our understanding of causality in the social sciences.

In this course you will learn both how to clean longitudinal data as well as 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 organized as a mixture of lectures and hands on practicals using real world data. During the course there will also be opportunities to discuss also how to apply these models in your own research.

Objectives:

-       To gain competence in the concepts, designs and terms of longitudinal research;

-       To be able to apply a range of different methods for longitudinal data analysis;

-       To have a general understanding of how each method represents different kinds of longitudinal processes;

To be able to choose a design, a plausible model and an appropriate method of analysis for a range of research questions

Course Description

The course will be spread over five days of teaching in five different weeks.

Topics covered by day:

03.05 .2024 - Data cleaning and visualization of longitudinal data

10. 05.2024 - Cross-lagged models (covering also an introduction to Structural Equation Modelling and auto-regressive models)

17. 05.2024 - Multilevel model of change (covering also an introduction to multilevel modelling)

24. 05.2024- Latent Growth Modelling

31. 05. 2024 - Survival models (also known as event history analysis)

 Teaching will take place online (using Zoom) between 09:00 to 16:00 UK time. There will be 1 hour lunch break from 12:00 to 13:00.

Prerequisites

- Good knowledge of regression modelling

- Basic knowledge of R or good programming experience with a different statistical software

 Recommended reading

-       Cernat, A. (in press). Longitudinal Data Analysis using R. LeanPub.

-       Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data (First edition). O’Reilly. (also available free online)

Singer, J., & Willett, J. (2003). Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press.

-       Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A Comprehensive Introduction. Routledge