Guides the creation of rigorous pre-analysis plans (PAPs) for experimental social science following DA-RT and APSA transparency standards.
This skill assists researchers in drafting comprehensive Pre-Analysis Plans (PAPs) to establish analytic transparency before data collection begins. It operationalizes high-level social science standards like DA-RT and JARS, providing expert guidance on selecting appropriate registries (OSF, AEA, AsPredicted), structuring documents, and specifying complex analytical strategies. By helping users define locked, conditional, and exploratory analysis tiers and guiding the pre-registration of analysis code using simulated data, it ensures research integrity and helps mitigate the 'garden of forking paths' in experimental workflows.
主要功能
01Detailed contingency planning for attrition, imbalance, and design failures
02Guidance for pre-registering analysis code using simulated data structures
03Three-tier analytical classification (locked, conditional, and exploratory)
04Structured PAP document drafting aligned with APSA and JARS standards
05Expert registry selection guidance for OSF, AEA RCT, and AsPredicted
0615 GitHub stars
使用场景
01Developing contingency trees for potential experimental design failures
02Drafting a complete pre-analysis plan for a new social science field experiment
03Pre-registering R or Python analysis code to ensure pipeline reproducibility
