Identification of developmental trajectory classes: Comparing three latent class methods using simulated and real data

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Introduction Several statistical methods are available to identify developmental trajectory classes, but it is unclear which method is most suitable. The aim of this study was to determine whether latent class analysis, latent class growth analysis or growth mixture modeling was most appropriate for identifying developmental trajectory classes. Methods We compared the three methods in a simulation study in several scenarios, which varied regarding e.g. sample size and degree of separation between classes. The simulation study was replicated with a real data example concerning anxiety/depression symptoms measured over 6 time points in the Tracking Adolescent Individuals’ Lives Survey (TRAILS, N = 2227). Results Growth mixture modeling was least biased or equally biased compared to latent class analysis and latent class growth analysis in all scenarios. In TRAILS, the shapes of the trajectories were rather similar over the three methods, but class sizes differed slightly. A 4-class growth mixture model performed best, based on several fit indices, interpretability and clinical relevance. Conclusions Growth mixture modeling seems most suitable to identify developmental trajectory classes.
Original languageEnglish
Article number100288
Number of pages10
JournalAdvances in Life Course Research
Publication statusPublished - Dec-2019


  • Developmental trajectory
  • Growth Mixture Modeling
  • Latent class analysis
  • Latent class growth analysis
  • Longitudinal data analysis
  • Monte Carlo simulation study

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