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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

¹Warwick Business School, University of Warwick.

|February 25, 2026

Summary

Indirect question techniques, like the Crosswise Model, improve truthful survey responses on sensitive topics. This study introduces a new methodology and Bayesian methods for better baseline item selection and more efficient data analysis.

Keywords:

Bayesian statistics item response theory sensitive questions surveys truth-telling techniques

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Area of Science:

Social Sciences
Statistics
Survey Methodology

Background:

Direct survey questions on sensitive topics can lead to response bias.
Indirect questioning techniques aim to improve data accuracy by protecting participant privacy.
The Crosswise Model is an indirect technique pairing sensitive and non-sensitive items.

Purpose of the Study:

To develop an integrated methodology for selecting appropriate baseline items in the Crosswise Model.
To propose novel Bayesian estimation methods for analyzing data from the Crosswise Model.
To enhance the efficiency and relax assumptions of existing Crosswise Model applications.

Main Methods:

Developed a methodology for baseline item selection considering conceptual and statistical factors.
Proposed four distinct statistical models within the methodology.
Introduced novel Bayesian estimation techniques for implementing these models.
Assessed model efficiency compared to common Crosswise Model applications.

Main Results:

The new methodology provides a structured approach to baseline item selection.
The proposed Bayesian methods offer improved efficiency and potentially relaxed statistical assumptions.
Empirical application on LGBT attitudes demonstrates the Crosswise Model's utility.

Conclusions:

The integrated methodology and Bayesian estimation enhance the Crosswise Model's applicability and reliability.
This research facilitates more accurate data collection on sensitive issues.
Available tools (app, code) support wider adoption of the improved Crosswise Model.