A second-order conditionally linear mixed effects model with observed and latent variable covariates

Jeffrey R. Harring, Nidhi Kohli, Rebecca D. Silverman, Deborah L. Speece

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a nonlinear manner are common to all subjects. In this article we describe how a variant of the Michaelis-Menten (M-M) function can be fit within this modeling framework using Mplus 6.0. We demonstrate how observed and latent covariates can be incorporated to help explain individual differences in growth characteristics. Features of the model including an explication of key analytic decision points are illustrated using longitudinal reading data. To aid in making this class of models accessible, annotated Mplus code is provided.

Original languageEnglish (US)
Pages (from-to)118-136
Number of pages19
JournalStructural Equation Modeling
Volume19
Issue number1
DOIs
StatePublished - Sep 6 2012

Keywords

  • Conditionally linear models
  • Latent variables
  • Nonlinear mixed effects models
  • Second-order latent growth models
  • Structured latent curve models

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