- 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
- 1.4 The Definition of Time and the Age-Period-Cohort Conundrum
- 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 Multilevel Models with Categorical Outcomes
- 9.6 Model Building and Model Comparison
- 9.7 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 Latent Growth Models with Categorical Outcomes
- 10.6 Model Building and Model Comparison
- 10.7 Comparison with the Multilevel Model for Change
- 10.8 Conclusions
- 10.9 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
Longitudinal Data Analysis Using R
The book covers all the key skills needed for preparing, exploring, and analysing longitudinal data. To facilitate understanding and help readers learn these skills, it interweaves statistical modelling with computer code and visualizations. It does this using real-world data, code, and outputs that readers can replicate.
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The book covers all the key skills needed for preparing, exploring, and analysing longitudinal data. To facilitate understanding and help readers learn these skills, it interweaves statistical modelling with computer code and visualizations. It does this using real-world data, code, and outputs that readers can replicate.
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Longitudinal data is essential for understanding the world around us. It allows us to investigate changes over time and obtain 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 necessary for working with longitudinal data, employing a hands-on approach and real-world data. To ensure a solid foundation, it begins by introducing the basics of R, regression modelling, path analysis, and 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 provides an in-depth guide to state-of-the-art statistical models for analysing longitudinal data, including the multilevel model for change and the latent growth model. It discusses practical challenges to conducting longitudinal analysis, such as missing data, measurement error, separating age-period-cohort effects, presenting results, and following a reproducible workflow.
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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.
Episode 267
An Interview with Alexandru Cernat
Contents
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