Longitudinal Data Analysis for the Behavioral Sciences Using R
1st Edition
Published on 13. December 2011
Book
Hardback
568 pages
978-1-4129-8268-9 (ISBN)
Description
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
"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
More details
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 35 mm
Weight
1250 gr
ISBN-13
978-1-4129-8268-9 (9781412982689)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
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.
Content
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
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