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This book is unique in its focus on showing students in the behavioral sciences how to analyze longitudinal data using R software. The book focuses on application, making it practical and accessible to students in psychology, education, and related fields, who have a basic foundation in statistics. It provides explicit instructions in R computer programming throughout the book, showing students exactly how a specific analysis is carried out and how output is interpreted.

"This text excels in the explanation of models with the side-by-side use of R, so the audience can see the models in action. There is a gentle coverage of the mathematics driving the models, which does not seem intimidating to a non technical audience."William Anderson, Cornell University

About the Author
Chapter 1. Introduction
Chapter 2. Brief Introduction to R
Chapter 3. Data Structures and Longitudinal Analysis
Chapter 4. Graphing Longitudinal Data
Chapter 5. Introduction to Linear Mixed Effects Regression
Chapter 6. Overview of Maximum Likelihood Estimation
Chapter 7. Multimodel Inference and Akaike's Information Criterion
Chapter 8. Likelihood Ratio Test
Chapter 9. Selecting Time Predictors
Chapter 10. Selecting Random Effects
Chapter 11. Extending Linear Mixed Effects Regression
Chapter 12. Modeling Nonlinear Change
Chapter 13. Advanced Topics
Appendix: Soft Introduction to Matrix Algebra
Author Index
Subject Index

I am currently trying to introduce this text to my course this spring, though I am getting some resistance. I'm finding that most of my students are not familiar enough with R and I can't devote enough class time to help them learn R AND learn about growth modeling. At least for now, considering how the course is structured, I plan to use it as a supplemental text.

Dr Justin Heinze
Educational Psychology, University of Illinois - Chicago
March 5, 2012
Key features

· Uses a small data set throughout so that readers can reproduce each data analysis as they proceed through the book

· Employs linear mixed effects regression as the main method in the book, which is presented as an extension of traditional multiple regression, building on a foundation familiar to most behavioral science students

· Provides a relatively non-technical explanation with more technical material appearing in optional sections for readers who want additional details

· Includes chapters on important topics often neglected in other texts, such as data management (Chapter 3), graphing of longitudinal data (Chapter 4), multimodel inference using global fit statistics (Chapter 7), modeling of nonlinear trajectories (Chapter 12), and advanced topics such as dynamic covariates and multiple response variable models (Chapter 13)

For instructors

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ISBN: 9781483305721

ISBN: 9781412982689