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Related Concept Videos

Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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During the 1950s, the landmark Robbers Cave experiment demonstrated that when groups must compete with one another, intergroup conflict, hostility, and even violence may result. At the Oklahoman summer camp, two troops of boys—termed the Rattlers and the Eagles—took part in a week-long tournament. During this time, their negativity culminated in derogatory name-calling, fistfights, and even vandalism and destruction of property. However, this work also revealed that such tension could be...
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Updated: May 13, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Yahtzee: an anonymized group level matching procedure.

Jason J Jones1, Robert M Bond, Christopher J Fariss

  • 1Political Science Department and Medical Genetics Division, University of California San Diego, La Jolla, California, United States of America.

Plos One
|February 27, 2013
PubMed
Summary
This summary is machine-generated.

Researchers can now integrate sensitive data from multiple sources without compromising privacy. The Yahtzee procedure anonymizes individuals into groups, enabling secure data linkage for computational social science research.

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Last Updated: May 13, 2026

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Area of Science:

  • Computational social science
  • Data privacy
  • Information security

Background:

  • Integrating diverse datasets containing personal information is crucial for research.
  • Protecting individual privacy is paramount, especially with the rise of big data in social science.
  • Existing privacy protocols can impede the integration of sensitive behavioral data.

Purpose of the Study:

  • To present a novel procedure for secure data integration without a trusted third party.
  • To prevent the leakage of individual-specific information between data sources.
  • To facilitate the combination of anonymized data from disparate sources.

Main Methods:

  • The study introduces the 'Yahtzee procedure' for data anonymization.
  • Individuals are randomly assigned to anonymous groups before data merging.
  • The method was applied to datasets from Facebook and public voter records.

Main Results:

  • The Yahtzee procedure effectively prevents direct information leakage between data sources.
  • Theoretical predictions for the procedure's performance were validated.
  • Successful application to real-world datasets demonstrates practical utility.

Conclusions:

  • The Yahtzee procedure offers a viable solution for privacy-preserving data integration.
  • This method supports advancements in computational social science by enabling secure data linkage.
  • Researchers can confidently combine sensitive datasets while upholding subject confidentiality.