Building latent class growth trees

Mattis Van Den Bergh, Jeroen K. Vermunt

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
106 Downloads (Pure)


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
Issue number3
Publication statusPublished - 2018


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


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