Variance constraints strongly influenced model performance in growth mixture modeling: a simulation and empirical study

Variance constraints strongly influenced model performance in growth mixture modeling: a simulation and empirical study

Abstract Background Growth Mixture Modeling (GMM) is commonly used to group individuals on their development over time, but convergence issues and impossible values are common. This can result in unreliable model estimates. Constraining variance parameters across classes or over time can solve these...

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Bibliographic Details
Main Authors: Jitske J. Sijbrandij, Tialda Hoekstra, Josué Almansa, Margot Peeters, Ute Bültmann, Sijmen A. Reijneveld
Format: Article
Language:English
Published: BMC 2020-11-01
Series:BMC Medical Research Methodology
Subjects:
Simulation studies
Longitudinal studies
Developmental trajectories
Growth mixture model
Variance misspecification
Model selection
Online Access:http://link.springer.com/article/10.1186/s12874-020-01154-0

Internet

http://link.springer.com/article/10.1186/s12874-020-01154-0

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