Are common language effect sizes easier to understand than traditional effect sizes?

Area of Science:

• Psychology
• Communication Studies
• Research Methods

Background:

• Communicating complex research findings to the public is challenging.
• Traditional statistical effect size metrics (e.g., r, r²) are often difficult for non-statisticians to understand.
• Nontraditional effect size indicators have been developed to improve clarity.

Purpose of the Study:

• To empirically test the understandability and usefulness of nontraditional effect size indicators compared to traditional ones.
• To assess how different effect size metrics influence perceptions of intervention effectiveness.

Main Methods:

• Empirical testing of nontraditional effect size indicators (e.g., binomial effect size display, common language effect size indicator).
• Comparison of audience perception of understandability and usefulness against traditional metrics (e.g., r, r²).
• Assessment of willingness to pay and perceived program effectiveness based on the type of effect size metric used.

Main Results:

• Nontraditional effect size indicators were perceived as significantly more understandable and useful than traditional indicators.
• Participants rated training programs as more effective when effectiveness was communicated using nontraditional metrics.
• Individuals were willing to pay more for programs described with nontraditional effect size indicators.

Conclusions:

• Nontraditional effect size indicators enhance public understanding of research results.
• These metrics improve the perceived effectiveness and value of interventions.
• Adopting clearer communication tools is crucial for disseminating research impact effectively.