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Fixed Effects or Mixed Effects Classifiers? Evidence From Simulated and Archival Data.

Anthony A Mangino1,2, Jocelyn H Bolin1, W Holmes Finch1

  • 1Ball State University, Muncie, IN, USA.

Educational and Psychological Measurement
|July 3, 2023
PubMed
Summary

This study compared fixed and mixed effects models for multilevel data classification. Findings show fixed effects models performed comparably to mixed effects models, emphasizing predictor type and data structure over model choice.

Keywords:
Monte Carlo simulationProgram for International Student Assessmentmixed effects modelspredictive classification

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Area of Science:

  • Statistical modeling
  • Machine learning
  • Educational research

Background:

  • Multilevel data structures are common in social sciences and education.
  • Predictive classification models are crucial for understanding complex phenomena.
  • Choosing between fixed and mixed effects models can impact classification accuracy.

Purpose of the Study:

  • To compare the predictive classification performance of fixed effects models versus mixed effects models.
  • To evaluate logistic regression and random forests within both fixed and mixed effects frameworks.
  • To assess model performance using both simulated data and real-world educational data.

Main Methods:

  • Monte Carlo simulation to generate and analyze multilevel data.
  • Comparison of fixed effects logistic regression, mixed effects logistic regression, and random forests.
  • Application to U.S. PISA data for predicting student retention.

Main Results:

  • Fixed effects models demonstrated comparable performance to mixed effects models.
  • Model performance was consistent across simulation studies and the PISA dataset analysis.
  • The type of predictors and the data structure were more influential than the model type itself.

Conclusions:

  • Fixed effects models are a viable alternative to mixed effects models for predictive classification with multilevel data.
  • Researchers should prioritize careful consideration of predictor variables and data structure.
  • Model selection should be guided by data characteristics rather than a default preference for mixed effects models.