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Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation.

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|May 23, 2024
PubMed
Summary
This summary is machine-generated.

Misspecifying linear mixed-effects models in psychological research leads to inaccurate results. The Deviance Information Criterion (DIC) generally outperforms the Akaike Information Criterion (AIC) for model selection and estimation accuracy.

Keywords:
Bayesian modelLinear mixed-effects modelaccuracy in parameter estimationmodel selectionpower analysis

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Area of Science:

  • Psychological research methods
  • Statistical modeling

Background:

  • Linear mixed-effects models (LMMs) are widely used for dependent data in psychology.
  • Increasing model complexity in LMMs poses computational and convergence challenges.
  • Guidance is needed for applied users on selecting appropriate random effects estimation methods.

Purpose of the Study:

  • To investigate the impact of misspecifying restricted maximum likelihood (REML) and Bayesian estimation models in LMMs.
  • To compare the model selection performance of Akaike Information Criterion (AIC) and Deviance Information Criterion (DIC).

Main Methods:

  • A Monte Carlo simulation study was conducted.
  • The study examined misspecified models with and without random effects.
  • AIC and DIC were compared for their effectiveness in model selection.

Main Results:

  • Models omitting existing random effects showed inflated Type I errors, poor coverage, and inaccurate R-squared.
  • Models with superfluous random effects experienced convergence issues and reduced power, particularly with Bayesian estimation.
  • DIC outperformed AIC in identifying correct models, improving convergence, and estimating effect sizes, especially for simpler models.

Conclusions:

  • Model misspecification in LMMs significantly compromises analytical integrity in psychological research.
  • DIC demonstrates superior performance over AIC for model selection and accuracy in complex LMM analyses.
  • Careful consideration of random effects and appropriate model selection criteria are crucial for reliable LMM results.