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Comparing linear mixed model (LMM) building strategies, the top-down approach, starting with many fixed effects, performed better than the step-up method for identifying true population models in longitudinal data analysis.

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Area of Science:

  • Statistics
  • Psychometrics
  • Longitudinal Data Analysis

Background:

  • Model building with linear mixed models (LMMs) is complex due to the interdependence of fixed and random effects.
  • Common LMM approaches in psychology and education often use a step-up method, starting with simpler models and adding complexity.
  • The performance of different LMM building strategies in accurately identifying true population models remains underexplored.

Purpose of the Study:

  • To compare the performance of step-up versus top-down model building approaches for linear mixed models.
  • To evaluate the ability of these methods to identify the true population model in exploratory longitudinal data analysis.

Main Methods:

  • Simulations were conducted using student achievement data from the Chicago longitudinal study.
  • The study examined a step-up model building approach, incrementally adding fixed and random effects.
  • The study also examined a top-down model building approach, starting with an overelaborate fixed effects model and determining random effects.

Main Results:

  • The top-down model building approach demonstrated superior performance in identifying the true population model compared to the step-up method.
  • Findings suggest that starting with a more complex fixed effects structure aids in correctly specifying the random effects structure.

Conclusions:

  • The top-down approach is a viable and potentially more effective strategy for LMM building in longitudinal research.
  • Researchers should consider the top-down method for exploratory longitudinal data analysis to improve model identification accuracy.