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Data · dataset · 2026

Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees

Listed in IISH Dataverse and ODISSEI Portal — shown once because both records carry DOI 10.34894/5x3snz

Scripts and data for reproducing all simulations and application to Math, Reading and Science trajectories in the EARLY CHILDHOOD LONGITUDINAL STUDY: KINDERGARTEN CLASS OF 1998-1999 data.

Description

Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and researchers are typically interested in explaining or predicting this heterogeneity.

We show how generalized linear mixed-effects model (GLMM) trees can be used to identify subgroups with different trajectories in linear growth curve models. Originally developed for clustered cross-sectional data, GLMM trees are extended here to longitudinal data. The resulting extended GLMM trees are directly applicable to growth curve models as an important special case.

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In simulated and real-world data, we assess performance of the extensions and compare against other partitioning methods for growth curve models. Extended GLMM trees perform more accurately than the original algorithm and LongCART, and similarly accurate compared to structural equation model (SEM) trees. In addition, GLMM trees allow for modeling both discrete and continuous time series, are less sensitive to (mis-)specification of the random-effects structure and are much faster to compute.

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Where it is published

Catalogue records · 2

Topics

Stated by source
Social Sciences · Social Sciences
From keywords
Social Science · Social Science
Inferred from text
Longitudinal study 75% · Specialist studies in education 70%
Provenance · 2 source records, 12 field assertions
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IISH Dataversedoi:10.34894/5X3SNZ11 d agoJSON v1
ODISSEI Portaldoi:10.34894/5X3SNZ7 d agoJSON v1
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