Longitudinal Data Analysis Using R
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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 as well 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 insure 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 visualizations. Finally, the book 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.

About the Author

Alexandru Cernat
Alexandru Cernat

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

Table of Contents

  • Preface
  • 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 Importing Data
    • 3.2 Merging and Reshaping
    • 3.3 Cleaning Data
    • 3.3 Efficient Data Preparation
    • 3.3 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 Further Reading
  • 6. Introduction to Path Analysis
    • 6.1 Auto-Regressive Models
    • 6.2 Fit Indices and Model Comparison
    • 6.3 Further Reading
  • 7. The Cross-Lagged Models
    • 7.1 The Cross-Lagged Model
    • 7.2 Running the Cross-Lagged Model in R
    • 7.3 Testing the Equality of Cross-Lagged Coefficients
    • 7.4 Including Control Variables
    • 7.5 Conclusions
    • 7.6 Further Reading
  • 8. The Multilevel Model for Change
    • 8.1 What is Multilevel Modelling?
    • 8.2 Multilevel Modeling and Longitudinal Data
    • 8.3 Treating Time Flexibly
    • 8.4 Explaining Change
    • 8.5 Model Building and Model Comparison
    • 8.6 Further Reading
  • 9. The Latent Growth Model
    • 9.1 What is Latent Growth Modelling?
    • 9.2 Estimating Latent Growth Model in R
    • 9.3 Treating Time Flexibly
    • 9.4 Explaining Change Using LGM
    • 9.5 Model Building and Model Comparison
    • 9.6 Comparison with the Multilevel Model for Change
    • 9.7 Conclusions
    • 9.8 Further Reading
  • 10. Measurement Error and Longitudinal Data
    • 10.1 Confirmatory Factor Analysis
    • 10.2 Longitudinal Equivalence
    • 10.3 Second Order Models
    • 10.4 The Quasi-Simplex Model
    • 10.5 Further Reading
  • References

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