Higher-Order Growth Curves and Mixture Modeling with Mplus

Auteur

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.

Texte du rabat

This practical introduction to second-order and growth mixture models using Mplus introduces simple and complex techniques through incremental steps. The authors extend latent growth curves to second-order growth curve and mixture models and then combine the two using normal and non-normal (e.g., categorical) data. To maximize understanding, each model is presented with basic structural equations, figures with associated syntax that highlight what the statistics mean, Mplus applications, and an interpretation of results. Examples from a variety of disciplines demonstrate the use of the models and exercises allow readers to test their understanding of the techniques. A comprehensive introduction to confirmatory factor analysis, latent growth curve modeling, and growth mixture modeling is provided so the book can be used by readers of various skill levels. The book's datasets are available on the web. New to this edition: Two new chapters providing a stepwise introduction and practical guide to the application of second-order growth curves and mixture models with categorical outcomes using the Mplus program. Complete with exercises, answer keys, and downloadable data files. Updated illustrative examples using Mplus 8.0 include conceptual figures, Mplus program syntax, and an interpretation of results to show readers how to carry out the analyses with actual data. This text is ideal for use in graduate courses or workshops on advanced structural equation, multilevel, longitudinal or latent variable modeling, latent growth curve and mixture modeling, factor analysis, multivariate statistics, or advanced quantitative techniques (methods) across the social and behavioral sciences.

Contenu

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)
9. Growth Curve Analysis with Categorical Outcomes [NEW CHAPTER]
10. Higher-order Growth Curve Analysis with Categorical Outcomes [NEW CHAPTER]