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Journal of Classification
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30-04-2019

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

Authors: Yuan Liu, Hongyun Liu

Published in: Journal of Classification | Issue 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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Metadata
Title
Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model
Authors
Yuan Liu
Hongyun Liu
Publication date
30-04-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 3/2019
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-018-9291-9

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Note: t for Two (Clusters)

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