Obtaining Interpretable Parameters From Reparameterized Longitudinal Models: Transformation Matrices Between Growth Factors in Two Parameter Spaces

Jin Liu, Robert A. Perera, Le Kang, Roy T. Sabo and Robert M. Kirkpatrick
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Roy T. Sabo: Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
Robert M. Kirkpatrick: Department of Psychiatry, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA

Journal of Educational and Behavioral Statistics, 2022, vol. 47, issue 2, 167-201

Abstract: This study proposes transformation functions and matrices between coefficients in the original and reparameterized parameter spaces for an existing linear-linear piecewise model to derive the interpretable coefficients directly related to the underlying change pattern. Additionally, the study extends the existing model to allow individual measurement occasions and investigates predictors for individual differences in change patterns. We present the proposed methods with simulation studies and a real-world data analysis. Our simulation study demonstrates that the method can generally provide an unbiased and accurate point estimate and appropriate confidence interval coverage for each parameter. The empirical analysis shows that the model can estimate the growth factor coefficients and path coefficients directly related to the underlying developmental process, thereby providing meaningful interpretation.

Keywords: linear spline growth models; unknown knots; individual measurement occasions; time-invariant covariates; simulation studies (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:47:y:2022:i:2:p:167-201

DOI: 10.3102/10769986211052009

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