You are here

Longitudinal Data Analysis for the Behavioral Sciences Using R

Longitudinal Data Analysis for the Behavioral Sciences Using R

October 2011 | 568 pages | SAGE Publications, Inc
This book is a practical guide for the analysis of longitudinal behavioural data. Longitudinal data consist of repeated measures collected on the same subjects over time. Such data is collected by researchers in psychology, education, organization studies, public policy, and related fields. A variety of substantive research questions are addressed with longitudinal data, including how student achievement changes over time, how psychopathology develops, and how intra-group conflict evolves.
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

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

Preview this book

Sample Materials & Chapters

Chapter 1: Introduction

Chapter 2: Brief Introduction to R

Jeffrey D. Long

Jeffrey D. Long, PhD, is Professor of Psychiatry in the Carver College of Medicine at the University of Iowa. He is also the Head Statistician for Neurobiological Predictors of Huntington's Disease (PREDICT-HD), a longitudinal NIH-funded study of early detection of Huntington's Disease. His undergraduate degree is from the University of California at Los Angeles, and his doctoral degree is from the University of Southern California in the area of quantitative psychology. More About Author

Purchasing options

Please select a format:

ISBN: 9781412982689

SAGE Knowledge is the ultimate social sciences digital library for students, researchers, and faculty. Hosting more than 4,400 titles, it includes an expansive range of SAGE eBook and eReference content, including scholarly monographs, reference works, handbooks, series, professional development titles, and more.

The platform allows researchers to cross-search and seamlessly access a wide breadth of must-have SAGE book and reference content from one source.