## **Research Transparency: Bridging Diversity and Innovation via Data Analysis**### **Introduction:**Contemporary research faces a credibility crisis, undermining trust in vital debates such as climate change or diversity promotion. Researchers have increasingly adopted preregistration for randomized control trials, but extending this to analysis of existing data is challenging. We demonstrate a novel framework for data-dependent analysis transparency, focusing on the impact of ownership diversity on business innovation.### **Protocol:**Our protocol includes:1. **Split sample design:** Dataset division for specification testing and hypothesis testing ensures de novo statistical validity.2. **Dual method approach:** Exploratory stage provides priors for confirmatory Bayesian estimation, enhancing statistical power.3. **Axiom-based diversity measures:** Four axioms ensure that the chosen diversity index is meaningful and controlled.4. **False discovery rate and family-wise error rate corrections:** Prevent spurious findings from multiple hypothesis testing.### **Application to Diversity-Innovation Relationship:**We analyze diversity in ownership teams and its association with new-to-market innovation using the 2018 Annual Business Survey. We find:- **Diverse ownership teams:** Ownership teams with diversity in academic discipline, race, ethnicity, and foreign-born status are more likely to report innovation.- **Multidimensional approach:** Composite measures of diversity, incorporating multiple dimensions, provide a more robust assessment of diversity's impact.- **Educational specialization:** The strongest contribution to diversity's effect on innovation is found in different academic specializations.- **Maximally diverse teams:** Teams with the highest diversity on these dimensions are six times more likely to innovate than homophilic teams.### **Implications:**- **Transparency:** Our protocol promotes transparency and replication, reducing the risk of false discovery.- **Statistical Power:** Bayesian estimation enhances statistical power over traditional frequentist methods.- **Data-Dependence Problem:** The protocol addresses data-dependent analysis, a persistent issue in observational research.- **Policy Implications:** Findings suggest that promoting diversity in key dimensions can foster innovation and economic growth.### **Conclusion:**Our protocol provides a proof of concept for increasing research transparency in contentious topics. It resolves practical and ethical concerns surrounding data-dependent analysis, safeguarding the credibility and impact of research in fields like economics, sociology, and policy.