Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model

Yuan Liu () and Hongyun Liu ()
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Yuan Liu: Southwest University
Hongyun Liu: Beijing Normal University
Authors registered in the RePEc Author Service: Hongyun Han

Journal of Classification, 2019, vol. 36, issue 3, No 16, 659-677

Abstract: Abstract The piecewise growth mixture model is used in longitudinal studies to tackle non-continuous trajectories and unobserved heterogeneity in a compound way. This study investigated how factors such as latent distance and shape influence the model. Two simulation studies were used exploring the 2- and 3-class situation with sample size, latent distance (Mahalanobis distance), and shape being considered as the influencing factor. The results of two simulations showed that a non-parallel shape led to a slightly better overall model fit. Parameter estimation is affected by the shape, mainly through the parameter differences between latent classes.

Keywords: Piecewise growth mixture model (PGMM); Latent distance; Shape; Parameter difference (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00357-018-9291-9

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