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
- 3 Hurricane Names
- 4 The Multiverse Algorithm
- 5 Empirical Multiverses
- 6 Influence Analysis and Scope Conditions
- 7 Good and Bad Controls
- 8 Some Alternative Approaches
- Part III Expanding the Multiverse
- Appendix: Coding with MULTIVRS in Stata
- References
- Index
4 - The Multiverse Algorithm
from Part II - The Computational 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
- 3 Hurricane Names
- 4 The Multiverse Algorithm
- 5 Empirical Multiverses
- 6 Influence Analysis and Scope Conditions
- 7 Good and Bad Controls
- 8 Some Alternative Approaches
- Part III Expanding the Multiverse
- Appendix: Coding with MULTIVRS in Stata
- References
- Index
Summary
In this chapter we explain how to build a plausible space using the all-combinations algorithm, and how to evaluate its results. When the true model is unknown, what models are plausible? What set of models would a task force of rival scholars embrace? This provides a central foundation for a broader multiverse that develops in later chapters. We also show how to incorporate concepts of necessary controls and estimated consistency.
Keywords
- Type
- Chapter
- Information
- Multiverse AnalysisComputational Methods for Robust Results, pp. 43 - 58Publisher: Cambridge University PressPrint publication year: 2025