Higher-Order Growth Curves and Mixture Modeling with Mplus

Higher-Order Growth Curves and Mixture Modeling with Mplus

  • Kandauda Wickrama
  • Tae Kyoung Lee
  • Catherine Walker O’Neal
  • Frederick Lorenz
Publisher:RoutledgeISBN 13: 9781000465808ISBN 10: 1000465802

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Higher-Order Growth Curves and Mixture Modeling with Mplus is written by Kandauda Wickrama and published by Routledge. It's available with International Standard Book Number or ISBN identification 1000465802 (ISBN 10) and 9781000465808 (ISBN 13).

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.