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Random effects won't solve the problem of generalizability.

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Summary

Researchers often use inadequate statistical models for broad inferences. This study argues that random effects are unsuitable for generalizability because variation sources are systematic, not random.

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Area of Science:

  • Statistical modeling
  • Research methodology
  • Generalizability in research

Background:

  • Broad inferences in research rely on statistical models.
  • Yarkoni suggests including more sources of variation as random effects.
  • Current models may be insufficient for drawing general conclusions.

Purpose of the Study:

  • To challenge the universal applicability of random effects in statistical modeling.
  • To propose that systematic variation, not random, is key for general inferences.
  • To highlight limitations of current statistical approaches for broad research conclusions.

Main Methods:

  • Critique of statistical models used for broad inferences.
  • Analysis of the nature of variation in common study designs.
  • Theoretical argument against the use of random effects for generalizability.

Main Results:

  • Random effects are often inappropriate for drawing general inferences.
  • The source of variation in many studies is systematic, not random.
  • Impoverished statistical models hinder accurate broad conclusions.

Conclusions:

  • Researchers should reconsider the use of random effects for generalizability.
  • Understanding variation as systematic is crucial for robust inferences.
  • Methodological improvements are needed for more reliable research conclusions.