Building latent class growth trees

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Abstract

Researchers use latent class growth (LCG) analysis to detect meaningful subpopulations that display different growth curves. However, especially when the number of classes required to obtain a good fit is large, interpretation of the encountered class-specific curves might not be straightforward. To overcome this problem, we propose an alternative way of performing LCG analysis, which we call LCG tree (LCGT) modeling. For this purpose, a recursive partitioning procedure similar to divisive hierarchical cluster analysis is used: Classes are split until a certain criterion indicates that the fit does not improve. The advantage of the LCGT approach compared to the standard LCG approach is that it gives a clear insight into how the latent classes are formed and how solutions with different numbers of classes relate. The practical use of the approach is illustrated using applications on drug use during adolescence and mood regulation during the day.
Original languageEnglish
Pages (from-to)331-342
JournalStructural Equation Modeling
Volume25
Issue number3
DOIs
Publication statusPublished - 2018

Fingerprint

Latent Class
Latent Class Analysis
Growth Analysis
Recursive Partitioning
Mood
Growth Curve
Cluster Analysis
Cluster analysis
Drugs
cluster analysis
mood
adolescence
drug use
Curve
Class
Latent class
Alternatives
Modeling
interpretation

Keywords

  • MIXTURE
  • MODEL
  • NUMBER
  • hierarchical clustering
  • latent class growth analysis
  • latent class growth trees
  • longitudinal data
  • mixture models

Cite this

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abstract = "Researchers use latent class growth (LCG) analysis to detect meaningful subpopulations that display different growth curves. However, especially when the number of classes required to obtain a good fit is large, interpretation of the encountered class-specific curves might not be straightforward. To overcome this problem, we propose an alternative way of performing LCG analysis, which we call LCG tree (LCGT) modeling. For this purpose, a recursive partitioning procedure similar to divisive hierarchical cluster analysis is used: Classes are split until a certain criterion indicates that the fit does not improve. The advantage of the LCGT approach compared to the standard LCG approach is that it gives a clear insight into how the latent classes are formed and how solutions with different numbers of classes relate. The practical use of the approach is illustrated using applications on drug use during adolescence and mood regulation during the day.",
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Building latent class growth trees. / Van Den Bergh, Mattis; Vermunt, Jeroen K.

In: Structural Equation Modeling, Vol. 25, No. 3, 2018, p. 331-342.

Research output: Contribution to journalArticleScientificpeer-review

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