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Effect size measures for mediation models: quantitative strategies for communicating indirect effects
Kristopher J Preacher1, Ken Kelley
1Department of Psychology, University of Kansas, 1415 Jayhawk Boulevard, Room 426, Lawrence, KS 66045-7556, USA. preacher@ku.edu
Psychological Methods
|April 20, 2011
Summary
This study introduces new effect size measures for mediation analysis, addressing the lack of attention to practical importance in statistical findings. New R tools are provided for communicating these crucial mediation effect sizes.
Area of Science:
- Statistics
- Psychology
- Social Sciences
Background:
- Statistical mediation analysis is vital for understanding causal processes.
- Current methods inadequately address effect size and practical importance in mediation analysis.
Purpose of the Study:
- To establish criteria for effective effect size measures in mediation.
- To evaluate existing methods for communicating mediation effect size.
- To develop novel effect size indices and practical communication tools for mediation analysis.
Main Methods:
- Developed a residual-based effect size index quantifying variance explained in mediators and outcomes.
- Proposed an indirect effect index as a proportion of the maximum possible indirect effect.
- Created user-friendly R tools for numerical and visual effect size communication.
Main Results:
- Introduced two new effect size indices for mediation analysis.
- Provided practical tools (R packages) for implementing these indices.
- Addressed the need for better communication of mediation effect size and practical significance.
Conclusions:
- Enhanced methods for quantifying and communicating mediation effect sizes are essential.
- The proposed indices and tools facilitate a clearer understanding of mediation's practical importance.
- Improved effect size communication aids scientific interpretation and application of mediation analyses.
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