Growth Curve Analysis and Visualization Using R

Abstract

"Accessible to quantitative psychology researchers, this book introduces growth curve analysis (GCA) methods for applications in the behavioral sciences. It introduces the challenges involved with this type of data, discusses the basics of GCA, and explains how the methods can be used to analyze the data. The book takes a very practical approach, emphasizing visualization and keeping mathematical details to a minimum. It includes many real data examples from cognitive science and social psychology and integrates R code for the implementation of the methods"-- "This book is intended to be a practical, easy-to-understand guide to carrying out growth curve analysis (multilevel regression) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. Multilevel regression is becoming a more and more prominent statistical tool in the behavioral sciences and it is especially useful for time course data, so many researchers know they should use it, but they do not know how to use it. In addition, analysis of individual differences (developmental, neuropsychological, etc.) is an important subject of behavioral science research but many researchers don't know how to implement analysis methods that would help them quantify individual differences. Multilevel regression provides a statistical framework for quantifying and analyzing individual differences in the context of a model of the overall group effects. There are several excellent, detailed textbooks on multilevel regression, but I believe that many behavioral scientists have neither the time nor the inclination to work through those texts. If you are one of these scientists -- if you have time course data and want to use growth curve analysis, but don't know how -- then this book is for you. I have tried to avoid statistical theory and technical jargon in favor of focusing on the concrete issue of applying growth curve analysis to behavioral science data and individual differences."-- 1. Time course data -- 2. Conceptual overview of growth curve analysis -- 3. When change over time is not linear -- 4. Structuring random effects -- 5. Categorical predictors -- 6. Binary outcomes : logistic GCA -- 7. Individual differences -- 8. Complete examples.