Higher-Order Growth Curves and Mixture Modeling with Mplus

Kandauda A. S. Wickrama is a Professor in the Department of Human Development and Family Science at the University of Georgia, USA.Tae Kyoung Lee is a Lead Research Analyst at the University of Miami in the Department of Public Health Sciences, USA.Catherine Walker O’Neal is an Associate Research Scientist at the University of Georgia in the Department of Human Development and Family Science, USA.Frederick O. Lorenz is a University Professor Emeritus of Statistics and Psychology at Iowa State University, USA.

• 1 Introduction 2 Latent Growth Curves 3 Longitudinal Confirmatory Factor Analysis and Curve-of-Factors Growth Curve Models 4 Estimating Curve-of-Factors Growth Curve Models 5 Extending a Parallel Process Latent Growth Curve Model (PPM) to a Factor-of-Curves Model (FCM) 6 Estimating a Factor-of-Curves Model (FCM) and Adding Covariates 7 An Introduction to Growth Mixture Models (GMM) 8. Estimating a Conditional Growth Mixture Model (GMM) 9 Second-Order Growth Mixture Models (SOGMMs) 10. Growth Curve Analysis with Categorical Outcomes [NEW CHAPTER] 11. Higher-order Growth Curve Analysis with Categorical Outcomes [NEW CHAPTER]

"This book goes way beyond the basics of growth curve modeling. The authors manage to explain complicated and potentially confusing models like factor-of-curves and curve-of-factors very well. I like the way they explain how to interpret the models, including substantive interpretations of real world examples."Joop Hox, Utrecht University, The Netherlands"This timely work gives clear statistical advice and offers step-by-step coverage for Mplus users in the analysis of many kinds of growth curve models and also mixture models. A wealth of syntax examples and available data sets give additional opportunities for practice." Rex Kline, Concordia University, Canada"This book would be an excellent addition to graduate courses on the analysis of longitudinal data, advanced courses on structural equation modeling and multilevel regression, or for a workshop on how to conduct growth curve modeling analysis. This would also be an excellent resource for researchers conducting analyses of longitudinal data." Daniel W. Russell, Iowa State University, USA"The authors’ approach to explaining statistical analysis is one of the best I've seen, and each chapter is clear and easy to read. ... I would recommend it to people attending courses I run in the Quantitative Research Methods Training Unit. ... This project fills a significant gap ... by providing step by step procedures in the application of latent growth modelling techniques and translating complicated statistical language into simple English." D. Daniel Boduszek, University of Huddersfield, UK"This book has the potential to contribute greatly to the field. ... A resource that integrates ... Mplus with the analysis of different kinds of growth models will be widely used. ... The style is straightforward and easy to follow. ... I would consider adopting it for my graduate course ... Longitudinal Research Methods & Analysis. ... I would seriously consider ... using it for both research and teaching needs." Joel Hektner, North Dakota State University, USA"Examples are provided to help researchers see how to apply the methodology with actual data, and interpret the results. ... The book will be of interest to students, faculty and researchers working with longitudinal data in such areas as behavioral science, business, social sciences, and human development and family studies. … The book can be used … in advanced undergraduate or graduate level statistics courses, or a welcome addition to the methodological reference resources for researchers." Lisa L. Harlow, University of Rhode Island, USA"I find the writing style simple and straightforward. ... I would recommend this book to my colleagues. ... It is appropriate ... for an advanced SEM course. ... The Mplus codes that accompany the models ... would be very helpful to researchers. "Wen Luo, Texas A & M University, USA