Introduction to Longitudinal Data Analysis

Description:

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.

The course consists of five sessions spread over five weeks.

Topics covered by day:

• 13.03.2025  - Data cleaning and visualization of longitudinal data

• 21.03.2025 - Cross-lagged models (covering also an introduction to Structural Equation Modelling and auto-regressive models)

• 28.03.2025 - Multilevel model of change (covering also an introduction to multilevel modelling)

• 04.04.2025 -  Latent Growth Modelling

• 11.04.2025  - 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.

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.

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.

Cost:

The fee per teaching day is:
• £35 per day for students
• £75 per day for staff working for academic institutions, Research Councils and other recognised research institutions, registered charity organisations and the public sector
• £250 per day for all other participants.
In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. NO refunds are available after this date.
If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given.
NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of its cancellation of a course, including but not limited to any travel or accommodation costs. The University of Southampton’s Online Store T&Cs also continue to apply.

Website and registration:

Region:

North West

Keywords:

Qualitative Data Handling and Data Analysis, Multilevel Modelling , Longitudinal Data Analysis, Survival analysis, Structural equation models, Growth modelling

Related publications and presentations from our eprints archive:

Qualitative Data Handling and Data Analysis
Multilevel Modelling
Longitudinal Data Analysis
Survival analysis
Structural equation models

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