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Updated: Jun 24, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

R2 effect-size measures for mediation analysis.

Amanda J Fairchild1, David P Mackinnon, Marcia P Taborga

  • 1University of South Carolina, Columbia, South Carolina 29208, USA. afairchi@mailbox.sc.edu

Behavior Research Methods
|April 14, 2009
PubMed
Summary
This summary is machine-generated.

New R(2) effect-size measures help researchers quantify variance in mediation models. These statistics evaluate component paths and overall mediated effects, with simulations showing acceptable bias for robust analysis.

Related Experiment Videos

Last Updated: Jun 24, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Health Promotion Research

Background:

  • Mediation models are crucial for understanding indirect effects in various research fields.
  • Quantifying the variance explained by mediation models is essential for effect size interpretation.
  • Existing effect-size measures may not fully capture the nuances of mediation pathways.

Purpose of the Study:

  • To introduce novel R(2) effect-size measures for mediation analysis.
  • To provide tools for evaluating both individual path contributions and the total mediated effect.
  • To facilitate the accurate assessment of variance accounted for in complex mediation models.

Main Methods:

  • Development of new R(2) effect-size statistics tailored for mediation models.
  • Conducting statistical simulations to assess the bias and performance of the proposed measures.
  • Application of the measures to a real-world dataset from a firefighter health promotion intervention.

Main Results:

  • The proposed R(2) effect-size measures effectively quantify variance in mediation models.
  • Statistical simulations demonstrated acceptable bias across diverse parameter and sample-size conditions.
  • The measures were successfully applied to a practical health promotion program example.

Conclusions:

  • The new R(2) effect-size measures offer a valuable enhancement for mediation analysis.
  • These measures provide researchers with a standardized way to report and interpret effect sizes in mediation.
  • Availability of SAS and SPSS code promotes the adoption and application of these measures in empirical research.