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Puzzling Answers to Crosswise Questions - Examining Overall Prevalence Rates, Primacy Effects and Learning Effects

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  • Walzenbach, Sandra
  • Hinz, Thomas

Abstract

This validation study on the crosswise model (CM) examines five survey experiments that were implemented in a general population survey. Our first crucial result is that in none of these experiments was the crosswise model able to verifiably reduce social desirability bias. In contrast to most previous CM applications, we use an experimental design that allows us to distinguish a reduction in social desirability bias from heuristic response behaviour, such as random ticking, leading to false positive or false negative answers. In addition, we provide insights on two potential explanatory mechanisms that have not yet received attention in empirical studies: primacy effects and panel conditioning. We do not find consistent primacy effects, nor does response quality improve due to learning when respondents have had experiences with crosswise models in past survey waves. We interpret our results as evidence that the crosswise model does not work in general population surveys and speculate that the question format causes mistrust in participants.

Suggested Citation

  • Walzenbach, Sandra & Hinz, Thomas, 2022. "Puzzling Answers to Crosswise Questions - Examining Overall Prevalence Rates, Primacy Effects and Learning Effects," EconStor Preprints 249353, ZBW - Leibniz Information Centre for Economics.
    • Handle: RePEc:zbw:esprep:249353
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    File URL: https://www.econstor.eu/bitstream/10419/249353/1/walzenbach-hinz-puzzling-answers-to-crosswise-questions.pdf
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    References listed on IDEAS

    as
    1. Marc Höglinger & Ben Jann, 2018. "More is not always better: An experimental individual-level validation of the randomized response technique and the crosswise model," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-22, August.
    2. Korndörfer, Martin & Krumpal, Ivar & Schmukle, Stefan C., 2014. "Measuring and explaining tax evasion: Improving self-reports using the crosswise model," Journal of Economic Psychology, Elsevier, vol. 45(C), pages 18-32.
    3. Julia Meisters & Adrian Hoffmann & Jochen Musch, 2020. "Can detailed instructions and comprehension checks increase the validity of crosswise model estimates?," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    4. Coutts Elisabethen & Jann Ben & Krumpal Ivar & Näher Anatol-Fiete, 2011. "Plagiarism in Student Papers: Prevalence Estimates Using Special Techniques for Sensitive Questions," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(5-6), pages 749-760, October.
    5. Jun-Wu Yu & Guo-Liang Tian & Man-Lai Tang, 2008. "Two new models for survey sampling with sensitive characteristic: design and analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 67(3), pages 251-263, April.
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    More about this item

    Keywords

    crosswise model; randomized response; social desirability bias; primacy effects; learning effects; panel conditioning; privacy concerns;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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