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
13 - Weighting the Multiverse
from Part III - Expanding the Multiverse
Published online by Cambridge University Press: 28 February 2025
- 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
Are some models better than others? Yes. But can we weight models by the probability that they are true? That is harder than it sounds. In this chapter we cover various methods for weighting the models in a multiverse and assess their strengths and weaknesses using a dataset on how air pollution near schools can affect student learning. Weighting models creates a tension between model selection and model robustness, and authors must be clear about how model weights change the distribution of results. We recommend uniform weights as a transparent default, and if further weighting is desired, either double lasso or influence weighting appears best for inference.
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- Information
- Multiverse AnalysisComputational Methods for Robust Results, pp. 208 - 225Publisher: Cambridge University PressPrint publication year: 2025