A Guide to Transformations for Standardized Mean Difference Effect Sizes: Presenting Meta-Analytic Findings With...

Area of Science:

• Evidence synthesis
• Meta-analysis
• Scientific communication

Background:

• Meta-analyses are crucial for policy and practice but are often underutilized outside healthcare.
• A primary barrier is the complexity of statistical estimates reported by researchers.
• Non-research audiences struggle to comprehend advanced statistical measures used in meta-analyses.

Purpose of the Study:

• To provide evidence synthesis researchers with accessible effect size transformations for meta-analytic estimates.
• To bridge the communication gap between researchers and non-research audiences.
• To enhance the practical use of meta-analysis findings in decision-making.

Main Methods:

• The guide focuses on transformations of average standardized mean difference (SMD) effect sizes from random-effects models.
• It includes calculations, R code, and interpretations for both statistical and non-research audiences.
• Four specific transformations are detailed: Cohen's U3, overlapping coefficient, common language effect size, and probability of study effect superiority.

Main Results:

• The guide offers practical tools for transforming SMD effect sizes.
• It summarizes existing literature on the comprehension of these transformations by non-research audiences.
• Identifies areas where further research is needed to understand non-researcher interpretation.

Conclusions:

• Translating complex effect sizes can significantly improve the accessibility of evidence synthesis.
• Researchers can use these tools to present findings in ways that resonate with policymakers and practitioners.
• Enhancing comprehension of meta-analytic results supports better-informed decision-making across various fields.