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The Crosswise Model for Surveys on Sensitive Topics: A General Framework for Item Selection and Statistical Analysis.

Marco Gregori1, Martijn G De Jong2, Rik Pieters3

  • 1Department of Marketing (Room 3.201), Warwick Business School, University of Warwick, Scarman Road, Coventry, CV4 7AL, UK. Marco.Gregori@wbs.ac.uk.

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This study introduces a new method for selecting baseline items in the Crosswise Model, improving truthful survey responses on sensitive topics. The enhanced methodology and Bayesian estimation offer more efficient and reliable data collection.

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Bayesian statisticsitem response theorysensitive questionssurveystruth-telling techniques

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Area of Science:

  • Survey Methodology
  • Psychometrics
  • Statistical Modeling

Background:

  • Direct questions on sensitive topics can lead to response bias.
  • Indirect questioning techniques aim to improve data accuracy by concealing responses.
  • The Crosswise Model uses paired sensitive and non-sensitive items to elicit truthful answers.

Purpose of the Study:

  • To develop an integrated methodology for selecting baseline items in the Crosswise Model.
  • To propose novel Bayesian estimation methods for these models.
  • To demonstrate improved efficiency and relaxed assumptions compared to existing Crosswise Model applications.

Main Methods:

  • Developed a methodology for baseline item selection based on conceptual and statistical criteria.
  • Introduced four distinct statistical models within this framework.
  • Proposed new Bayesian estimation techniques for implementing the models.

Main Results:

  • The new methodology offers improved efficiency over common Crosswise Model applications.
  • The proposed models may relax previously required statistical assumptions.
  • Empirical application on LGBT attitudes demonstrates the Crosswise Model's potential.

Conclusions:

  • The integrated methodology and Bayesian estimation enhance the Crosswise Model's utility.
  • Improved efficiency and relaxed assumptions facilitate broader application.
  • Available tools (app, code) support wider adoption of the methodology.