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Predicting and reasoning about replicability using structured groups
Bonnie C Wintle1, Eden T Smith2, Martin Bush2
1MetaMelb Research Initiative, School of Ecosystem and Forest Sciences, University of Melbourne, Parkville 3010, Australia.
Royal Society Open Science
|June 9, 2023
Summary
Experts accurately predicted research replicability using the IDEA protocol. Broader reasoning, considering factors like effect size and reputation, improved prediction accuracy in social and behavioral sciences.
Area of Science:
- Social and behavioral sciences
- Research methodology
- Scientific judgment
Background:
- Assessing the replicability of research is crucial for scientific progress.
- Judgments about replicability can be influenced by various cognitive and contextual factors.
Purpose of the Study:
- To explore how experts judge research replicability.
- To identify factors driving accurate replicability judgments.
Main Methods:
- Utilized a mixed-methods approach with the IDEA protocol (Investigate, Discuss, Estimate, Aggregate).
- Employed five expert groups to evaluate 25 research claims with replication data.
- Collected qualitative and quantitative data on participants' judgments and reasoning.
Main Results:
- Participants achieved 84% accuracy in predicting research replicability.
- Broader reasoning and consideration of factors like 'effect size' and 'reputation' correlated with higher accuracy.
- Evidence suggests a link between statistical literacy and accuracy in replicability judgments.
Conclusions:
- Expert judgments can be highly accurate in predicting research replicability.
- Encouraging diverse reasoning strategies and statistical literacy may enhance the reliability of scientific assessments.
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