The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories

DOI10.1007/s00180-018-0815-xzbMath1417.65058OpenAlexW2803828297MaRDI QIDQ1729325

Timothy Hayes, Satoshi Usami, Ross Jacobucci

Publication date: 27 February 2019

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-018-0815-x

zbMATH Keywords

classificationlongitudinal datadecision treesmodel misspecificationlatent change score model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Applications of statistics to psychology (62P15)

Uses Software

• R
• lavaan
• OpenMx
• KmL
• Onyx

Cites Work

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• OpenMx: an open source extended structural equation modeling framework
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• Multivariate decision trees
• Tests of measurement invariance without subgroups: a generalization of classical methods
• KmL: k-means for longitudinal data
• Latent Curve Models
• PERFORMANCE OF INFORMATION CRITERIA FOR MODEL SELECTION IN A LATENT GROWTH CURVE MIXTURE MODEL
• Problems in the Analysis of Survey Data, and a Proposal