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Related Experiment Video

Updated: Feb 26, 2026

The Modified Temptation Resistance Task: A Paradigm to Elicit Children's Strategic Lie-telling
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Modeling Evasive Response Bias in Randomized Response: Cheater Detection Versus Self-protective No-Saying.

Khadiga H A Sayed1,2, Maarten J L F Cruyff1, Peter G M van der Heijden1,3

  • 1Utrecht University.

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This study introduces new models for the "ever/last year" randomized response design to better account for evasive response bias in sensitive surveys. These models improve data accuracy for sensitive topics like doping use.

Keywords:
anabolicsdopingever/last yearsensitive questions

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Area of Science:

  • Social Sciences
  • Statistics
  • Survey Methodology

Background:

  • Randomized response techniques (RRT) aim to reduce evasive response bias in surveys on sensitive topics.
  • Existing models, like the cheater detection and self-protective no sayers models, partially address this bias but have limitations.
  • A hybrid
  • ever/last year
  • design offers potential for improved data collection.

Purpose of the Study:

  • To develop and present statistical models for the hybrid
  • ever/last year
  • RRT design.
  • To account for both self-protective no-saying and cheating behaviors within this design.
  • To illustrate the application of these models using doping use surveys.

Main Methods:

  • The study establishes the correspondence between existing RRT bias models.
  • New models are introduced for the
  • ever/last year
  • design, incorporating parameters for response bias.
  • Extensions of the design with additional questions and degrees of freedom are modeled.

Main Results:

  • The proposed models for the
  • ever/last year
  • design allow for the inclusion of response bias parameters.
  • Models with multiple degrees of freedom are developed for more complex survey designs.
  • The models were successfully illustrated using real-world doping use survey data.

Conclusions:

  • The
  • ever/last year
  • RRT design, with the proposed models, offers a promising approach for sensitive data collection.
  • These models enhance the ability to detect and correct for response biases.
  • Further research is warranted to explore the full potential of this design and its extensions.