Crowdsourcing multiverse analyses to explore the impact of different data-processing and analysis decisions: A...

Area of Science:

• Empirical research methodology
• Psychological science
• Data analysis

Background:

• Empirical data analysis often involves arbitrary choices (e.g., outlier definition).
• Single-pathway analyses limit result generalizability due to unexplored alternatives.
• Multiverse analyses address this by exploring diverse analytical pathways.

Purpose of the Study:

• To introduce a novel, principled crowdsourcing approach for conducting multiverse analyses.
• To enhance objectivity and transparency in empirical research.
• To provide a practical tutorial and illustration for implementing this method.

Main Methods:

• Developed a crowdsourcing framework for systematic multiverse analysis.
• Created a step-by-step tutorial for implementation.
• Illustrated the approach using the Semantic Priming Across Many Languages project.

Main Results:

• Demonstrated the feasibility of crowdsourced multiverse analyses.
• Showcased increased objectivity and transparency in research findings.
• Highlighted the potential to mitigate bias in data analysis.

Conclusions:

• Crowdsourced multiverse analyses offer a more objective and transparent alternative to traditional methods.
• This approach enhances the robustness and generalizability of empirical findings.
• Facilitates wider adoption of rigorous analytical practices in research.