4. Latent Growth Models for Longitudinal Data Analysis

  1. Jichuan Wang1 and
  2. Xiaoqian Wang2

Published Online: 14 AUG 2012

DOI: 10.1002/9781118356258.ch4

Structural Equation Modeling: Applications Using Mplus

Structural Equation Modeling: Applications Using Mplus

How to Cite

Wang, J. and Wang, X. (2012) Latent Growth Models for Longitudinal Data Analysis, in Structural Equation Modeling: Applications Using Mplus, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118356258.ch4

Author Information

  1. 1

    Children's National Medical Center, The George Washington University, USA

  2. 2

    Mobley Group Pacific Ltd., P.R. China

Publication History

  1. Published Online: 14 AUG 2012
  2. Published Print: 7 SEP 2012

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9781119978299

Online ISBN: 9781118356258

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Keywords:

  • LGM in longitudinal data analysis;
  • SEM application in longitudinal data analysis;
  • linear unconditional LGM;
  • selected Mplus output, unconditional linear LGM;
  • LGM with time-invariant and time-varying covariates;
  • nonlinear LGM;
  • polynomial function, specifying nonlinear outcome change;
  • selected Mplus output, quadratic linear LGM;
  • unconditional LGM with free time scores;
  • multi-process LGM

Summary

This chapter contains sections titled:

  • Linear LGM

  • Nonlinear LGM

  • Multi-process LGM

  • Two-part LGM

  • LGM with categorical outcomes