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Journal of Classification

Erschienen in:

30.04.2019

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

verfasst von: Yuan Liu, Hongyun Liu

Erschienen in: Journal of Classification | Ausgabe 3/2019

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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.

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Titel: Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model
verfasst von: Yuan Liu
Hongyun Liu
Publikationsdatum: 30.04.2019
Verlag: Springer US
Erschienen in: Journal of Classification / Ausgabe 3/2019
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI: https://doi.org/10.1007/s00357-018-9291-9

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