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A model-based imputation procedure for multilevel regression models with random coefficients, interaction effects,

Craig K Enders1, Han Du1, Brian T Keller1

  • 1Department of Psychology, University of California, Los Angeles.

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This study introduces a new Bayesian imputation method for multilevel models, effectively handling missing data in complex behavioral science analyses. The approach provides accurate parameter estimates, closely matching complete data results.

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Area of Science:

  • Behavioral Science
  • Statistics
  • Data Analysis

Background:

  • Missing data handling is crucial in behavioral science.
  • Standard methods like multiple imputation can bias results in models with interaction or polynomial effects.
  • Bayesian imputation shows promise for single-level regression with missing data.

Purpose of the Study:

  • To extend fully Bayesian (model-based) imputation to multilevel models (up to 3 levels).
  • To accommodate mixtures of categorical and continuous variables within these models.
  • To address bias issues in common behavioral science analysis models with missing data.

Main Methods:

  • Developed a novel model-based imputation procedure for multilevel regression.
  • Incorporated functionality for mixed-type variables (categorical and continuous).
  • Evaluated the approach using computer simulations.

Main Results:

  • The new imputation method proved effective for multilevel models with random coefficients and interactions.
  • Imputation-based parameter estimates were highly accurate across examined scenarios.
  • Results closely aligned with complete data analyses, indicating minimal bias.

Conclusions:

  • Model-based imputation is a viable and accurate method for handling missing data in complex multilevel models.
  • This approach offers a robust solution for common analyses in behavioral science research.
  • The procedure is accessible via the Blimp software for cross-platform use.