Longitudinal Data Analysis Using R

The book covers all the key skills needed for preparing, exploring, and analysing longitudinal data. To facilitate understanding and help readers learn these skills, it interweaves statistical modelling with computer code and visualizations. It does this using real-world data, code, and outputs that readers can replicate.

Alexandru Cernat

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About the Book

Longitudinal data is essential for understanding the world around us. It allows us to investigate changes over time and obtain 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 necessary for working with longitudinal data, employing a hands-on approach and real-world data.

To ensure a solid foundation, it begins by introducing the basics of R, regression modelling, path analysis, and 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 provides an in-depth guide to state-of-the-art statistical models for analysing longitudinal data, including the multilevel model for change and the latent growth model. It discusses practical challenges to conducting longitudinal analysis, such as missing data, measurement error, separating age-period-cohort effects, presenting results, and following a reproducible workflow.

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Author

About the Author

Alexandru Cernat

Alexandru Cernat is a Professor at the University of Manchester, specialising in collecting and analysing longitudinal data.

Over the past decade, he has published over 50 papers and book chapters using advanced statistical models to investigate how people and societies change.

He has extensive experience teaching survey methodology and longitudinal data analysis to researchers and practitioners worldwide, including for organisations such as the European Survey Research Association, the Social Research Association, GESIS, Essex Summer School and the International Program in Survey and Data Science.

He is also the founder of longitudinalanalysis.com, a platform developed to help researchers and analysts learn how to collect, clean, and analyse longitudinal data with confidence.

Episode 267

An Interview with Alexandru Cernat

Testimonials

Reader Testimonials

Analysing longitudinal data is challenging, and while there has been a lot written on the subject before, no other book compiles the relevant topics in such a pedagogical way as Alex does in this book. He manages to balance going in-depth while still keeping complex concepts grounded. The book is a must-read for both people new to longitudinal data analysis and those looking to improve their existing knowledge on the subject further
Kim Backström
PhD student, Åbo Akademi, Finland
What I really liked about the book was how unique it was, there were not many up-to-date books on longitudinal analysis in R that were accessible. The way it is laid out is also excellent, particularly around multi-level modelling.
Emily Lowthian
Lecturer in Education, Swansea University
I purchased this book after attending a short course taught by Dr. Cernat because he was great at clearly and methodically explaining complex longitudinal analysis techniques. This book perfectly replicates his teaching style and has been an incredibly useful resource for me whilst developing code for longitudinal measurement invariance tests and structural equation models in R.
Benedict Hignell
PhD Student, University of Essex
I had a longitudinal dataset that was ideal for answering an important research question in my field, but I had no experience with longitudinal analyses. After reading this book, I felt confident enough to run the analyses myself. The paper is now ready to be submitted. I was honestly surprised that a book could help someone with zero background in longitudinal analysis do complex analyses for a real research project.
Omid Ghasemi
Postdoctoral Research Associate, University of New South Wales

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