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

Jitske J Sijbrandij1, Tialda Hoekstra1, Josué Almansa1

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Growth mixture modeling is the most suitable statistical method for identifying developmental trajectory classes, outperforming latent class analysis and latent class growth analysis in bias and fit.

Keywords:
Developmental trajectoryGrowth Mixture ModelingLatent class analysisLatent class growth analysisLongitudinal data analysisMonte Carlo simulation study

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Area of Science:

  • Psychometrics
  • Developmental Psychology
  • Statistical Modeling

Background:

  • Identifying developmental trajectory classes is crucial for understanding individual differences in development.
  • Existing statistical methods like latent class analysis (LCA), latent class growth analysis (LCGA), and growth mixture modeling (GMM) have limitations in suitability.
  • Comparative studies are needed to guide the selection of the most appropriate method.

Purpose of the Study:

  • To compare the suitability of LCA, LCGA, and GMM for identifying developmental trajectory classes.
  • To determine which method demonstrates the least bias and best performance across various simulation scenarios.
  • To evaluate the performance of these methods using real-world longitudinal data.

Main Methods:

  • A simulation study was conducted, varying sample size and class separation to compare LCA, LCGA, and GMM.
  • The simulation was replicated using longitudinal data on anxiety/depression symptoms from the Tracking Adolescent Individuals' Lives Survey (TRAILS).
  • Model fit indices, interpretability, and clinical relevance were used to assess performance.

Main Results:

  • Growth mixture modeling (GMM) exhibited the least bias, performing comparably to LCA and LCGA in all simulated scenarios.
  • Analysis of TRAILS data showed similar trajectory shapes across methods, with minor differences in class sizes.
  • A 4-class GMM demonstrated superior performance based on fit indices, interpretability, and clinical relevance.

Conclusions:

  • Growth mixture modeling (GMM) is recommended as the most suitable statistical approach for identifying developmental trajectory classes.
  • GMM offers advantages in terms of reduced bias and robust performance in longitudinal data analysis.
  • The findings provide valuable guidance for researchers selecting statistical methods in developmental trajectory research.