On the Correspondence between the Latent Growth Curve and Latent Change Score Models

Sarfaraz Serang, Kevin J. Grimm, Zhiyong Zhang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

There has been a great deal of work in the literature on the equivalence between the mixed-effects modeling and structural equation modeling (SEM) frameworks in specifying growth models (Willett & Sayer, 1994). However, there has been little work on the correspondence between the latent growth curve model (LGM) and the latent change score model (see Grimm, Zhang, Hamagami, & Mazzocco, 2013). We demonstrate that four popular variants of the latent change score model–the no change, constant change, proportional change, and dual change models–have LGM equivalents. We provide equations that allow the translation of parameters from one approach to the other and vice versa. We then illustrate this equivalence using mathematics achievement data from the National Longitudinal Survey of Youth.

Original languageEnglish (US)
Pages (from-to)623-635
Number of pages13
JournalStructural Equation Modeling
Volume26
Issue number4
DOIs
StatePublished - Jul 4 2019

Keywords

  • latent change score model
  • latent growth curve model
  • longitudinal
  • structural equation modeling

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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