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Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
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Negligible interaction test for continuous predictors.

Yasaman Jabbari1, Robert Cribbie2

  • 1Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Canada.

Journal of Applied Statistics
|June 27, 2022
PubMed
Summary
This summary is machine-generated.

Researchers can now test for negligible interactions between continuous predictors using equivalence testing. This method is more appropriate than traditional null hypothesis tests for demonstrating small, meaningful effects in behavioral science research.

Keywords:
Moderationinteractionlinear modelsmultiple regressionnegligible interaction

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Area of Science:

  • Behavioral Science
  • Psychology
  • Statistics

Background:

  • Researchers often need to test for negligible interactions between continuous predictors.
  • Traditional null hypothesis tests are ill-suited for demonstrating small, meaningful effects due to sample size sensitivity and hypothesis setup.

Purpose of the Study:

  • To investigate a method for testing negligible interaction effects between continuous predictors.
  • To compare equivalence testing with traditional association-based null hypothesis tests.

Main Methods:

  • Utilized unstandardized and standardized regression-based models.
  • Employed equivalence testing.
  • Conducted a Monte Carlo simulation study.

Main Results:

  • Equivalence testing demonstrated effectiveness in identifying negligible interaction effects.
  • The proposed method proved more suitable than traditional approaches for this research question.

Conclusions:

  • Equivalence testing offers a superior approach for assessing negligible interactions in behavioral science.
  • This method provides a more appropriate framework for researchers aiming to demonstrate consistency in predictor effects.