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 organised 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.

IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the software noted below. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.

Course Description

Prerequisites

Good knowledge of regression modelling
Basic knowledge of R or good programming experience with 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.