Longitudinal data can offer important insights into how study participants develop and change across their life course and across generations, but obtaining those insights requires us to use appropriate methods for analysing the data. This module will introduce some of the useful analysis methods that can help you explore and understand relationships within longitudinal datasets.
In this module you will learn:
Suggested citation: Kaye, N., Hardy, R. & O’Neill, D. (2020). Analysing longitudinal data. CLOSER Learning Hub, London, UK: CLOSER
Longitudinal studies collected data on individuals over multiple time points. Analysing the data they produce provides useful insights into how and why people change over time.
Regression analysis is a commonly-used analysis technique, which examines the relationship between variables. Within longitudinal studies, we can use regression models to examine how early-life circumstances or characteristics relate to outcomes in adulthood, middle age or later life.
Example research questions:
Multi-level modelling is a technique that takes into account the fact that participants who are clustered within the same group – e.g. they are from the same family, they attend the same school, they live in the same neighbourhood, etc.) – will be more similar that people from outside this group. Multi-level models can also be used to analyse patterns of change over time using growth curve modelling – here repeated measurements are clustered within individuals.
Example research questions:
Survival analysis models the time to an event of interest – e.g. marriage, unemployment, retirement, death, etc. It is interested in the duration of time that passes from when a participant enters a study (baseline) until this event occurs, and can be used to examine the impact of certain characteristics (e.g. childhood obesity measures, socio-economic measures) on time to event.
Example research questions:
How much have you learned about techniques for analysing longitudinal data? When you have completed all the sections in this module, take the quiz to test how much you know.
Why not check out our Teaching Dataset pages, where you can find information about CLOSER’s teaching dataset, including instructions on how to access the data, exercises you may find useful to explore data analysis further, and a detailed guide to accompany more advanced regression analysis techniques.
CLOSER’s Training Hub provides more in-depth learning on analysing data, in our data management section, which covers understanding your data, documenting code, specific types of data and more.
The Learning Hub is a resource for students and educators
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