Longitudinal Data Analysis Using R +

Longitudinal data analysis is essential for understanding change, dynamics, and causal processes in the social sciences. By following individuals, households, organisations, or countries over time, longitudinal data allows researchers to move beyond static descriptions and answer questions about development, change, and causality.

Researchers often struggle with longitudinal data because the practical workflow is hard and seldom discussed. Issues such as preparing data, exploring change over time, choosing appropriate models, handling missing data, and translating statistical output into substantive conclusions are often not part of courses or books on longitudinal data analysis. Many existing resources focus on isolated methods, assume strong statistical backgrounds, or skip over the messy steps that dominate real research.

Longitudinal Data Analysis Using R helps solve these problems.

This book provides a complete, hands-on guide to longitudinal data analysis, covering the full research workflow from raw data to published results. Using real-world datasets and fully reproducible R code, it shows not only which models to use, but how and why they are applied in practice.

• Understand longitudinal data and the key concepts underlying change over time
• Prepare longitudinal data efficiently, including importing, reshaping, recoding, and structuring data correctly
• Explore longitudinal data using tables, summary statistics, and visualisations
• Apply core models for longitudinal analysis, including fixed and random effects models, cross-lagged models, multilevel models for change, and latent growth models
• Handle common pitfalls in longitudinal research, such as missing data, measurement error, and age–period–cohort confounding
• Develop a reproducible workflow for longitudinal data analysis and communicate results clearly and transparently

The book starts by building a solid foundation, introducing the essentials of R, regression modelling, path analysis, and the core ideas behind longitudinal data. It then moves step by step through the practical stages of longitudinal analysis: preparing data, describing patterns of change, and estimating some of the most popular models for longitudinal analysis. Advanced chapters focus on substantive challenges that, if ignored, can undermine research, including missing data, measurement error, and the presentation of results.

Throughout, the emphasis is on applied longitudinal data analysis: real-world data, clear explanations, use of visualisation to understand complex concepts and code that can be adapted directly to your own research.

This book is designed for:

• PhD students and early-career researchers working with longitudinal data
• Applied researchers using surveys, panels, cohort studies, or administrative data
• Analysts transitioning to R for longitudinal analysis
• Researchers who want a single, coherent reference for longitudinal data analysis rather than a collection of disconnected methods

You do not need to be a statistical specialist or an expert in R to benefit from the book. The book covers foundational concepts, offers a thorough introduction to R and ensures you have the statistical knowledge needed to understand complex longitudinal models.

All the R code used in the book is available on the companion website together with the equivalent code in Mplus and Stata.

Paperback version is available here.

Table of contents for Longitudinal Data Analysis Using R

1. Introduction to Longitudinal Data
2. Introduction to R
3. Preparing Longitudinal Data
4. Describing Longitudinal Data
5. Introduction to Regression Models
6. Introduction to Path Analysis
7. Fixed and Random Effects
8. The Cross-Lagged Models
9. The Multilevel Model for Change
10. The Latent Growth Model
11. Measurement Error and Longitudinal Data
12. Dealing with Missing Data
13. Workflows and Presenting Results

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