An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software

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Abstract

The use of finite mixture modelling (FMM) is becoming increasingly popular for the analysis of longitudinal repeated measures data. FMMs assist in identifying latent classes following similar paths of temporal development. This paper aims to address the confusion experienced by practitioners new to these methods by introducing the various available techniques, which includes an overview of their interrelatedness and applicability. Our focus will be on the commonly used model-based approaches which comprise latent class growth analysis (LCGA), group-based trajectory models (GBTM), and growth mixture modelling (GMM). We discuss criteria for model selection, highlight often encountered challenges and unresolved issues in model fitting, showcase model availability in software, and illustrate a model selection strategy using an applied example.
Original languageEnglish
Article number100323
Number of pages17
JournalAdvances in Life Course Research
Volume43
DOIs
Publication statusPublished - Mar 2020

Keywords

  • Growth mixture model
  • Classification
  • Trajectory
  • Hidden heterogeneity
  • Latent class growth analysis
  • Repeated measures
  • CLASS GROWTH ANALYSIS
  • R PACKAGE
  • SAS PROCEDURE
  • MONTE-CARLO
  • 2 DECADES
  • SELECTION
  • NUMBER
  • TRAJECTORIES
  • IMPACT
  • PERFORMANCE

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