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Effect size measure for mediation analysis with a multicategorical predictor.

Zihuan Cao1, Heining Cham1, Jordan Stiver1

  • 1Psychology Department, Fordham University, New York, NY, United States.

Frontiers in Psychology
|March 27, 2023
PubMed
Summary

This study introduces the mediation effect size measure υ for nominal predictors with multiple categories. The Olkin-Pratt adjusted R-squared estimator demonstrated the best performance, showing minimal bias and mean squared error.

Keywords:
R-squaredcategorical predictoreffect sizemediation analysissimulation studies

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Quantitative psychology

Background:

  • Existing effect size measures for mediation are limited with nominal predictors having three or more categories.
  • The mediation effect size measure υ is suitable for these complex predictor scenarios.

Purpose of the Study:

  • To evaluate the performance of different estimators for the mediation effect size measure υ.
  • To identify the most reliable estimator for υ in mediation analysis with nominal predictors.

Main Methods:

  • A simulation study was conducted manipulating factors like the number of groups, sample size per group, and path effect sizes.
  • Various R-squared (R²) shrinkage estimators were compared for their accuracy in estimating υ.

Main Results:

  • The Olkin-Pratt extended adjusted R² estimator exhibited the least bias and smallest Mean Squared Error (MSE) across all simulated conditions.
  • Performance of different υ estimators was also assessed using a real-world data example.

Conclusions:

  • The Olkin-Pratt extended adjusted R² estimator is recommended for estimating mediation effect size (υ) with nominal predictors.
  • Guidelines are provided for the practical application of this recommended estimator in statistical analyses.