Clustering longitudinal data using R: a Monte Carlo study

Peter Verboon*, R.J. Pat El

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


The analysis of change within subjects over time is an ever more important research topic. Besides modelling the individual trajectories, a related aim is to identify clusters of subjects within these trajectories. Various methods for analyzing these longitudinal trajectories have been proposed. In this paper we investigate the performance of three different methods under various conditions in a Monte Carlo study. The first method is based on the non-parametric k-means algorithm. The second is a latent class mixture model, and the third a method based on the analysis of change indices. All methods are available in R. Results show that the k-means method performs consistently well in recovering the known clustering structure. The mixture model method performs reasonably well, but the change indices method has problems with smaller data sets.
Original languageEnglish
Pages (from-to)144-163
Number of pages20
JournalMethodology-European Journal of Research Methods for the Behavioral and Social Sciences
Issue number2
Publication statusPublished - Jun 2022


  • Monte Carlo
  • R
  • TIME
  • change
  • clustering
  • k-means
  • latent class
  • longitudinal
  • mixture model
  • trajectories


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