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Increasing Transparency Through a Multiverse Analysis.

Sara Steegen1, Francis Tuerlinckx1, Andrew Gelman2

  • 1KU Leuven, University of Leuven.

Perspectives on Psychological Science : a Journal of the Association for Psychological Science
|October 4, 2016
PubMed
Summary
This summary is machine-generated.

Researchers can avoid misleading results by performing a multiverse analysis. This method explores multiple data processing choices, revealing how arbitrary decisions impact statistical findings and highlighting the fragility of conclusions.

Keywords:
arbitrary choicesdata processinggood research practicesmultiverse analysisselective reportingtransparency

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Area of Science:

  • Social Sciences
  • Statistics
  • Quantitative Research Methods

Background:

  • Empirical research requires data processing, involving choices in data exclusion, transformation, and coding.
  • A single data analysis may not capture the full picture due to these processing choices.

Purpose of the Study:

  • To introduce and advocate for multiverse analysis as a robust alternative to single-dataset analysis.
  • To demonstrate how multiverse analysis can reveal the impact of data processing choices on research conclusions.

Main Methods:

  • Proposed multiverse analysis involves conducting all analyses across a comprehensive set of alternatively processed datasets.
  • Applied multiverse analysis to examine the effect of fertility on religiosity and political attitudes.

Main Results:

  • Analyzing a single dataset can yield misleading conclusions regarding the relationship between fertility, religiosity, and political attitudes.
  • Multiverse analysis quantifies the sensitivity of findings to arbitrary data processing decisions.

Conclusions:

  • Multiverse analysis provides insight into the robustness of research findings.
  • This approach identifies the most influential data processing choices affecting result fragility, enhancing research transparency and reliability.