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Longitudinal Data Analysis for the Behavioral Sciences Using R
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Longitudinal Data Analysis for the Behavioral Sciences Using R



October 2011 | 568 pages | SAGE Publications, Inc

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
 
Preface
 
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
 
References
 
Author Index
 
Subject Index

This is a great book for longitudinal analysis with R. Especially appreciated the detailed discussion about data preparation (which is usually ignored) and the discussion of model selection. Would have wanted to see additional methods such as survival analysis or sequence analysis. Also, I wish there were examples from different, more realistic datasets.

Overall a great applied book for longitudinal analysis with R.

Dr Alexandru Cernat
Social Science , Manchester University
November 8, 2016

This did not fit my requirements

Professor Corey Sparks
Demography, UTSA
June 29, 2015

If Maximum Likelihood Estimation is part of your Syllabus, Chapter 6 of this book should be one of your recommended readings. It is the most clear explanation of ML I ever seen! Practical examples using R are an extraordinary pedagogical tool to facilitate student's comprehension of the process involved in this estimation procedure.
Chapter 4, "Graphing Longitudinal Data" is highly recommended too!
This books has very powerful pedagogical tools for a complex topic.

Dr Guillermo Perez Algorta
Division of Health and Research, Lancaster University
December 26, 2014

Unfortunately, SPSS ist the statistical software of choice at the department, so this book is too advanced to introduce R and the longitudinal analysis at the same time.

Ms Freya Sukalla
Institut für Medien und Bildungstechnologie, Universität Augsburg
October 16, 2014

This textbook is one of the only textbooks on longitudinal data analysis that incorporates R, which is a bonus. However, if one is using it as a textbook for a course, there are no end of chapter exercises in the textbook. Additionally, the authors use the same data set for the entire book. More data sets that could be used both in examples in the book and on homework exercises would be beneficial.

Dr Stacey Hancock
Statistics Dept, Univ Of California-Irvine
September 22, 2014

I would definitely recommend this book as part of the longitudinal session during my Advanced Survey Methods module.

Dr Maria Pampaka
Social Science, Univ. of Manchester
August 29, 2014

This book is excellent, but the selection of methods presented was not broad enough to be used in the course I had planned. I might use chapters of it as the text is extremely well written, but as a general introduction to longitudinal analysis in epidemiology it is not was I was looking for: The chapters on estimation and testing would be a bit tangential for my course, and I lacked something more on time to even data.

Professor Laust Mortensen
Department of Public Health, University of Copenhagen
February 14, 2014

I recommend this as supplemental reading for postgraduate students. It is readable text on relatively complex statistics. The R focus is especially useful

Dr Denis O'Hora
Please select your department, National University of Ireland, Galway
January 22, 2014

On the recommendation list for the upcoming semester.

Dr Birgit Burboeck
International Business, Fh Joanneum
December 12, 2013

This is an excellant text and features within our course- many of our students have purchsed this.

Ben Carter
School of Medicine, Cardiff University
December 11, 2013
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)

Sample Materials & Chapters

Chapter 1: Introduction

Chapter 2: Brief Introduction to R


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