Longitudinal Data Analysis Using R
Longitudinal Data Analysis Using R
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
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Reader Testimonials
Kim Backström
PhD student, Åbo Akademi, Finland
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
Emily Lowthian
Lecturer in Education, Swansea University
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.
Benedict Hignell
PhD Student, University of Essex
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.
Table of Contents
- I The Basics
- 1. Introduction to Longitudinal Data
- 1.1 Designed Longitudinal Data Collection in the Social Sciences
- 1.2 Longitudinal Data Structures
- 1.3 Longitudinal Research Questions
- 2. Introduction to R
- 2.1 R and RStudio
- 2.2 Object Types in R
- 2.3 Subsetting
- 2.4 Importing and Exporting Data
- 2.5 Extending R Using Packages
- 2.6 Further Reading
- 3. Preparing Longitudinal Data
- 3.1 Longitudinal Data Workflow
- 3.2 Importing Data
- 3.3 Merging and Reshaping
- 3.4 Cleaning Data
- 3.5 Efficient Data Preparation
- 3.6 Further Reading
- 4. Describing Longitudinal Data
- 4.1 Tables and Summaries
- 4.2 Using Graphs with Longitudinal Data
- 4.3 Further Reading
- 5. Introduction to Regression Models
- 5.1 Correlation and Regression
- 5.2 Modelling Different Types of Relationships
- 5.3 Introduction to Generalized Linear Models (GLM)
- 5.4 Further Reading
- 6. Introduction to Path Analysis
- 6.1 Auto-Regressive Models
- 6.2 Fit Indices and Model Comparison
- 6.3 Longitudinal Mediation
- 6.4 Multi-Group Analysis
- 6.5 Categorical Outcomes
- 6.6 Further Reading
- 1. Introduction to Longitudinal Data
- II Understanding Causality Using Longitudinal Data
- 7. Fixed and Random Effects
- 7.1 Within and between variation
- 7.2 Fixed Effects Model
- 7.3 Random Effects Model
- 7.4 Choosing Between the Models
- 7.5 Hybrid Models
- 7.6 Conclusion
- 7.7 Further Reading
- 8. The Cross-Lagged Models
- 8.1 The Cross-Lagged Model
- 8.2 Running the Cross-Lagged Model in R
- 8.3 Testing the Equality of Cross-Lagged Coefficients
- 8.4 Including Control Variables
- 8.5 The Random Intercept Cross-Lagged Panel Model
- 8.6 Conclusions
- 8.7 Further Reading
- 7. Fixed and Random Effects
- III Understanding Change in Time
- 9. The Multilevel Model for Change
- 9.1 What Is Multilevel Modelling?
- 9.2 Multilevel Modeling and Longitudinal Data
- 9.3 Treating Time Flexibly
- 9.4 Explaining Change
- 9.5 Model Building and Model Comparison
- 9.6 Further Reading
- 10. The Latent Growth Model
- 10.1 What is Latent Growth Modelling?
- 10.2 Estimating Latent Growth Model in R
- 10.3 Treating Time Flexibly
- 10.4 Explaining Change Using LGM
- 10.5 Model Building and Model Comparison
- 10.6 Comparison with the Multilevel Model for Change
- 10.7 Conclusions
- 10.8 Further Reading
- 9. The Multilevel Model for Change
- IV Longitudinal Analysis in the Real World
- 11. Measurement Error and Longitudinal Data
- 11.1 Confirmatory Factor Analysis
- 11.2 Longitudinal Equivalence
- 11.3 Second Order Models
- 11.4 The Quasi-Simplex Model
- 11.5 Further Reading
- 12. Dealing with Missing Data
- 12.1 Causes for Missing Data
- 12.2 Methods for Dealing With Missing Data
- 12.3 Working With Weights and Complex Survey Designs
- 12.4 Using Full Information Maximum Likelihood in SEM
- 12.5 Multiple Imputation
- 12.6 Conclusions and Further Reading
- 13. Workflows and Presenting Results
- 13.1 Workflows for Data Analysis
- 13.2 Basics of Dynamic Documents
- 13.3 Presenting Results from Longitudinal Data Analysis
- 13.4 Further Reading
- 11. Measurement Error and Longitudinal Data
- References
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