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About the Book
Longitudinal data is essential for understanding the world around us. It allows us to investigate change in time and get better causal estimates. Nevertheless, this type of data is also more complex, making it difficult to manipulate, explore, and analyse.
This book covers all the key skills needed for working with longitudinal data using a hands-on approach and real-world data. To ensure a good foundation, it starts by introducing the basics of R, regression modelling, path analysis and the key concepts of longitudinal data. It then covers how to efficiently prepare longitudinal data by importing, recoding and reshaping data. This is followed by a comprehensive introduction to data exploration using tables, summary statistics and visualisations. The book also offers an in-depth guide to state-of-the-art statistical models for the analysis of longitudinal data, such as the multilevel model for change, the latent growth model and the cross-lagged model. Finally, it discusses practical challanges to doing longitudinal analysis such as missing data, measurement error, presenting results and following a reproducible workflow.
About the Author
Alexandru Cernat is an associate professor in the social statistics department at the University of Manchester. He has a PhD in survey methodology from the University of Essex and was a post-doc at the National Centre for Research Methods and the Cathie Marsh Institute. His research and teaching focus on: survey methodology, longitudinal data, measurement error, latent variable modelling, new forms of data and missing data. You can find out more about him and his research at: www.alexcernat.com