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Random effects structure for confirmatory hypothesis testing: Keep it maximal.

Dale J Barr1, Roger Levy2, Christoph Scheepers1

  • 1Institute of Neuroscience and Psychology, University of Glasgow, 58 Hillhead St., Glasgow G12 8QB, United Kingdom.

Journal of Memory and Language
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Summary
This summary is machine-generated.

Linear mixed-effects models (LMEMs) generalize best with maximal random effects structures justified by the experimental design. Data-driven or simpler structures may reduce generalizability and statistical power in psycholinguistic research.

Keywords:
Monte Carlo simulationgeneralizationlinear mixed-effects modelsstatistics

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Area of Science:

  • Psycholinguistics
  • Cognitive Science
  • Statistics

Background:

  • Linear mixed-effects models (LMEMs) are widely used in psycholinguistics.
  • Understanding the impact of random effects structures on generalizability is crucial.
  • Current practices may not fully leverage LMEMs for robust confirmatory hypothesis testing.

Purpose of the Study:

  • To investigate how different random effects structures in LMEMs influence the generalizability of findings.
  • To provide recommendations for optimal LMEM usage in psycholinguistic research.
  • To establish best practices for confirmatory hypothesis testing using LMEMs.

Main Methods:

  • Theoretical arguments regarding random effects structures.
  • Monte Carlo simulations to evaluate LMEM performance.
  • Comparison of maximal, data-driven, and random-intercepts-only LMEMs.
  • Analysis of generalization performance across different sample sizes and modeling criteria.

Main Results:

  • Maximal random effects structures, justified by the experimental design, yield the best generalizability for LMEMs.
  • Data-driven structures show variable performance dependent on criteria and sample size, offering little power advantage over maximal models.
  • Random-intercepts-only models perform worse than separate F1/F2 tests, especially when subject/item sensitivity varies.

Conclusions:

  • Maximal LMEMs should be the gold standard for confirmatory hypothesis testing in psycholinguistics.
  • Researchers must carefully consider random effects structures to ensure the generalizability of their LMEM analyses.
  • Adherence to established standards for random effects is essential for reliable research outcomes.