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Score-based tests for detecting heterogeneity in linear mixed models.

Ting Wang1, Edgar C Merkle2, Joaquin A Anguera3

  • 1The American Board of Anesthesiology, Raleigh, NC, USA. twb8d@mail.missouri.edu.

Behavior Research Methods
|July 16, 2020
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Summary
This summary is machine-generated.

New score-based tests can distinguish cross-level interactions from variance heterogeneity in linear mixed models (LMMs). These methods identify specific clusters exhibiting heterogeneity, improving statistical accuracy for multilevel modeling.

Keywords:
HeterogeneityLinear mixed modelsScore-based tests

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Area of Science:

  • Statistics
  • Multilevel Modeling
  • Psychometrics

Background:

  • Linear mixed models (LMMs), also known as multilevel models, can encounter complex interactions.
  • Heterogeneity in random effects and residuals can complicate fixed-effect tests in LMMs.
  • Existing methods for detecting variance heterogeneity in LMMs are limited.

Purpose of the Study:

  • To introduce and evaluate score-based tests for distinguishing cross-level interactions from variance heterogeneity in LMMs.
  • To provide a systematic approach for detecting heterogeneity among clusters in LMMs.
  • To offer practical guidance and tools for researchers using LMMs.

Main Methods:

  • Utilizing a family of score-based tests previously applied in psychometric models.
  • Extending these tests to the domain of linear mixed models.
  • Performing simulations to assess the implementation and performance of the tests.
  • Illustrating application with an empirical example.

Main Results:

  • The proposed score-based tests effectively differentiate between cross-level interactions and variance heterogeneity.
  • The tests provide information on specific clusters exhibiting heterogeneity.
  • The methods require only estimation of the null model, simplifying their application.

Conclusions:

  • Score-based tests offer a valuable tool for addressing confounding effects in LMMs.
  • These methods enhance the reliability of fixed-effect tests by accounting for variance heterogeneity.
  • The study provides practical implementation details and code for wider adoption.