Book contents
- Multiverse Analysis
- Analytical Methods for Social Research
- Multiverse Analysis
- Copyright page
- Contents
- Figures
- Tables
- Acknowledgments
- Part I Introduction
- Part II The Computational Multiverse
- Part III Expanding the Multiverse
- 9 Functional Form Robustness
- 10 Data Processing
- 11 Data Processing Multiverse Analysis of Regnerus and Critics
- 12 Retractions in Social Science
- 13 Weighting the Multiverse
- 14 Conclusion
- Appendix: Coding with MULTIVRS in Stata
- References
- Index
14 - Conclusion
from Part III - Expanding the Multiverse
Published online by Cambridge University Press: 28 February 2025
Book contents
- Multiverse Analysis
- Analytical Methods for Social Research
- Multiverse Analysis
- Copyright page
- Contents
- Figures
- Tables
- Acknowledgments
- Part I Introduction
- Part II The Computational Multiverse
- Part III Expanding the Multiverse
- 9 Functional Form Robustness
- 10 Data Processing
- 11 Data Processing Multiverse Analysis of Regnerus and Critics
- 12 Retractions in Social Science
- 13 Weighting the Multiverse
- 14 Conclusion
- Appendix: Coding with MULTIVRS in Stata
- References
- Index
Summary
This chapter advocates a simple principle: Good analysis should be easier to publish than bad analysis. Multiverse methods promote transparency over asymmetric information and emphasize robustness, countering the fragility inherent in single-path analysis. In an era when the credibility of scientific results is often challenged, the use of multiverse analysis is crucial for bolstering both the credibility and persuasiveness of research findings.
Keywords
- Type
- Chapter
- Information
- Multiverse AnalysisComputational Methods for Robust Results, pp. 226 - 234Publisher: Cambridge University PressPrint publication year: 2025