Commentary on “Obtaining Interpretable Parameters From Reparameterized Longitudinal Models: Transformation Matrices Between Growth Factors in Two Parameter Spacesâ€

Ziwei Zhang, Corissa T. Rohloff and Nidhi Kohli
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Nidhi Kohli: University of Minnesota

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 2, 262-268

Abstract: To model growth over time, statistical techniques are available in both structural equation modeling (SEM) and random effects modeling frameworks. Liu et al. proposed a transformation and an inverse transformation for the linear–linear piecewise growth model with an unknown random knot, an intrinsically nonlinear function, in the SEM framework. This method allowed for the incorporation of time-invariant covariates. While the proposed method made novel contributions in this area of research, the use of transformations introduces some challenges to model estimation and dissemination. This commentary aims to illustrate the significant contributions of the authors’ proposed method in the SEM framework, along with presenting the challenges involved in implementing this method and opportunities available in an alternative framework.

Keywords: linear–linear piecewise growth models; unknown knot; random effects models; latent growth curve models; transformations (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:2:p:262-268

DOI: 10.3102/10769986221126747

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