Multicurious
A Multidisciplinary Guide to Multiverse Analysis
Datos Bibliográficos
Multiverse analysis offers a comprehensive response to a core vulnerability in empirical research: the uncertainty of scientific conclusions arising from defensible yet flexible data-processing and -analysis decisions. By systematically mapping and computing all or a sample of all plausible data-processing pipelines, multiverse analysis reports the robustness of findings across analytical flexibility and increases transparency in the research process. As its adoption grows across disciplines, so too does the need for clarity on how to design, report, and interpret multiverse results responsibly. In this article, we provide interdisciplinary guidance on key procedural considerations, including defensibility and equivalence evaluations, preregistration, and computational demands. We aim to harmonize terminology, promote best practices, and foster conceptual cohesion across fields, supported by reference to domain-specific resources when appropriate. By doing so, we contribute to the broader movement toward more robust, reproducible, and transparent science, one that not only reports results but also interrogates the analytical pipelines that produce them.
Discipline · Social science · Sociology · Psychological and Educational Research Studies
Robustness Tests for Quantitative Research
Why Most Discovered True Associations Are Inflated
The Statistical Crisis in Science
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Helping Doctors and Patients Make Sense of Health Statistics
Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis
Increasing Transparency Through a Multiverse Analysis
Promoting an open research culture
Missing Data in Educational Research
Many Analysts, One Data Set
Inference and missing data
False-Positive Psychology
The multiverse of universes
Antifragile
Frontal alpha asymmetry as a marker of approach motivation? Insights from a cooperative forking path analysis
We Ran 9 Billion Regressions
Specification curve analysis
Understanding Patterns and Trends in Income Mobility through Multiverse Analysis
Has the Credibility of the Social Sciences Been Credibly Destroyed? Reanalyzing the 'Many Analysts, One Data Set' Project
Model Uncertainty and Robustness
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |