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Area of Science:

  • Statistics
  • Educational Research
  • Social Sciences

Background:

  • Understanding how higher-level factors influence individual outcomes is crucial in many research fields.
  • Existing methods may not adequately capture heterogeneity in these effects across different groups or units.

Purpose of the Study:

  • To propose and demonstrate a novel exploratory approach for assessing differential effects of level-2 predictors across level-1 units.
  • To identify latent classes at level-1 that exhibit distinct patterns in the influence of level-2 predictors.

Main Methods:

  • Utilizing multilevel regression mixture models to identify latent classes.
  • Employing Monte Carlo simulations to evaluate the proposed approach under various conditions, including different sample sizes and constraints on random effects.

Main Results:

  • The proposed method successfully identifies latent classes where the effects of level-2 predictors vary.
  • Simulations show the approach's performance across different sample sizes and the impact of constraining random effects.

Conclusions:

  • Multilevel regression mixture models offer a flexible framework for exploring heterogeneity in predictor effects.
  • The method provides valuable insights into research questions concerning variations in effects, such as the impact of classroom practices on students.