Model fit criteria curve behaviour in class enumeration - a diagnostic tool for model (mis)specification in longitudinal mixture modelling

G. van der Nest*, V.L. Passos, M.J.J.M. Candel, G.J.P. van Breukelen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The use of longitudinal finite mixture models (FMMs) to identify latent classes of individuals following similar paths of temporal development is gaining traction in applied research. However, FMM's users may be unaware of how data features as well as the inappropriate specification of the model's covariance structure impacts class enumeration. To elucidate this, we investigated model fit-criteria curve behaviour across an array of data conditions and covariance structures. Fit statistic patterns were variable among the fit criteria and across a range of data conditions. This variability was greatly attributable to the level of class separation and the presence/absence of random effects. Our findings support some widely held notions (e.g. BIC outperforms other criteria) while debunking others (adding random effects is not always the solution). Based on the obtained results, we present guidelines on how the behaviour of fit criteria curves can be used as a diagnostic aid during class enumeration.
Original languageEnglish
Pages (from-to)1640-1672
Number of pages33
JournalJournal of Statistical Computation and Simulation
Volume92
Issue number8
Early online date23 Nov 2021
DOIs
Publication statusPublished - 24 May 2022

Keywords

  • Growth mixture model
  • latent class growth analysis
  • trajectory
  • repeated measures
  • covariance misspecification
  • class extraction
  • LATENT CLASSES
  • TRAJECTORIES
  • PERFORMANCE
  • NUMBER
  • IMPACT
  • ASSUMPTIONS
  • ANXIETY
  • AGE

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