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. 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
Table of Contents
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I The Basics
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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
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2. Introduction to R
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2.1 R and RStudio
- 2.1.1 R Workflow
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2.2 Object Types in R
- 2.2.1 Atomic Vectors in R
- 2.2.2 Matrices and Data Frames
- 2.2.3 Lists
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2.3 Subsetting
- 2.3.1 Subsetting Complex Objects
- 2.3.2 Subsetting and Assignment
- 2.4 Importing and Exporting Data
- 2.5 Extending R Using Packages
- 2.6 Further Reading
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2.1 R and RStudio
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3. Preparing Longitudinal Data
- 3.1 Longitudinal Data Workflow
- 3.2 Importing Data
- 3.3 Merging and Reshaping
- 3.4 Cleaning Data
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3.5 Efficient Data Preparation
- 3.5.1 Iteration
- 3.5.2 Writing a Function
- 3.5.3 More Complex Iteration
- 3.6 Further Reading
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4. Describing Longitudinal Data
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4.1 Tables and Summaries
- 4.1.1 Simple Tables and Descriptives
- 4.1.2 Grouped Summary Tables
- 4.1.3 Transition Tables and Correlation Matrices
- 4.1.4 Describing Relationships Over Time
- 4.1.5 Exporting Tables
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4.2 Using Graphs with Longitudinal Data
- 4.2.1 Simple Univariate Visualizations
- 4.2.2 Univariate Visualizations Over Time
- 4.2.3 Simple Change Plots for 2-3 Time Points
- 4.2.4 Line Graphs
- 4.2.5 Area Graphs
- 4.2.6 Exporting Graphs
- 4.3 Further Reading
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4.1 Tables and Summaries
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5. Introduction to Regression Models
- 5.1 Correlation and Regression
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5.2 Modelling Different Types of Relationships
- 5.2.1 Categorical Predictors
- 5.2.2 Non-linear Relationships
- 5.2.3 Interactions
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5.3 Introduction to Generalized Linear Models (GLM)
- 5.3.1 Logistic Regression
- 5.3.2 Probit Regression
- 5.4 Further Reading
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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
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1. Introduction to Longitudinal Data
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II Understanding Causality Using Longitudinal Data
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7. Fixed and Random Effects
- 7.1 Within and Between Variation
- 7.2 Fixed Effects Model
- 7.2.1 Running a Fixed Effects Model in R
- 7.3 Random Effects Model
- 7.3.1 Running a Random Effects Model in R
- 7.4 Choosing Between the Models
- 7.5 Hybrid Models
- 7.5.1 Running Hybrid Models in R
- 7.6 Conclusion
- 7.7 Further Reading
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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
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7. Fixed and Random Effects
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III Understanding Change in Time
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9. The Multilevel Model for Change
- 9.1 What is Multilevel Modelling?
- 9.2 Multilevel Modeling and Longitudinal Data
- 9.2.1 The Unconditional Means Model (Random Effects Model)
- 9.2.2 The Unconditional Change Model
- 9.3 Treating Time Flexibly
- 9.3.1 Changing the Meaning of the Intercept
- 9.3.2 Non-Linear Change
- 9.4 Explaining Change
- 9.4.1 Including Time Constant Predictors
- 9.4.2 Including Time Varying Predictors
- 9.5 Model Building and Model Comparison
- 9.6 Further Reading
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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
- 10.3.1 Changing the Meaning of the Intercept
- 10.3.2 Non-Linear Change in LGM
- 10.4 Explaining Change Using LGM
- 10.4.1 Including Time Constant Predictors
- 10.4.2 Including Time Varying Predictors
- 10.4.3 Parallel Latent Growth Models
- 10.5 Model Building and Model Comparison
- 10.6 Comparison with the Multilevel Model for Change
- 10.6.1 Restricting the LGM
- 10.6.2 When to Use Each Model
- 10.7 Conclusions
- 10.8 Further Reading
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9. The Multilevel Model for Change
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IV Longitudinal Analysis in the Real World
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11. Measurement Error and Longitudinal Data
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11.1 Confirmatory Factor Analysis
- 11.1.1 Identification Strategies for CFA
- 11.2 Longitudinal Equivalence
- 11.3 Second Order Models
- 11.4 The Quasi-Simplex Model
- 11.5 Further Reading
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11.1 Confirmatory Factor Analysis
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11. Measurement Error and Longitudinal Data
- References
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