A Second-Order Conditionally Linear Mixed Effects Model With Observed and Latent Variable Covariates |
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| Authors: | Jeffrey R Harring Nidhi Kohli Rebecca D Silverman Deborah L Speece |
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| Institution: | 1. Department of Measurement , Statistics, and Evaluation, University of Maryland;2. Department of Special Education , University of Maryland |
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| 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. |
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| Keywords: | conditionally linear models latent variables nonlinear mixed effects models second-order latent growth models structured latent curve models |
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