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The Ultimate Cheat Sheet for Longitudinal Data Analysis in R

Learn key concepts, commands and analyses for longitudinal data analysis

Longitudinal data are powerful but complex, requiring new concepts, data structures, and models that can feel overwhelming to learn. This cheat sheet brings together the key ideas, R commands, and modelling approaches into a single workflow, helping you understand how everything fits together and providing the building blocks for mastering longitudinal data analysis.

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About

About the Book

Longitudinal data are becoming increasingly common across the social sciences, offering powerful ways to study change over time in individuals, groups, and institutions.

At the same time, working with longitudinal data is genuinely complex: it introduces new concepts, new data structures, and new statistical models that go far beyond cross-sectional analysis. Researchers are expected to understand ideas such as dependence over time, within- and between-person change, unbalanced panels, and a growing set of specialised methods, often with little guidance on how these pieces fit together. For many people, learning longitudinal analysis feels overwhelming.

The Ultimate Cheat Sheet for Longitudinal Data Analysis in R is designed to help you make sense of this complexity. It brings together the key concepts, commands, and models you need to know in order to confidently work with longitudinal data. Rather than teaching everything in depth, the cheat sheet gives you a clear map showing how ideas connect, how methods differ, and where each approach fits—so you can build knowledge systematically instead of piecing it together from scattered sources.

What’s included:

  • The workflow of longitudinal data analysis
  • Core longitudinal concepts
  • Main types of longitudinal data
  • Key R commands for preparing longitudinal data
  • Commands to describe and visualize longitudinal data
  • An overview of the main longitudinal models used in practice
  • Guidance on when to use different modelling approaches
  • Interpretation of main models for longitudinal data

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About the Author

Alexandru Cernat

Alexandru Cernat is a 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. He am also the founder of longitudinalanalysis.com, a platform that helps researchers and analysts learn to collect, clean, and analyse longitudinal data. You can find out more about him and his research at: alexcernat.com.

Leanpub Podcast

Episode 267

An Interview with Alexandru Cernat

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