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Uncovering a Blind Spot in Sensitive Question Research: False Positives Undermine the Crosswise-Model RRT

Author

Listed:
  • Marc Höglinger
  • Andreas Diekmann

Abstract

Validly measuring sensitive issues such as norm violations or stigmatizing traits through self-reports in surveys is often problematic. Special techniques for sensitive questions like the Randomized Response Technique (RRT) and, among its variants, the recent crosswise model should generate more honest answers by providing full response privacy. Different types of validation studies have examined whether these techniques actually improve data validity, with varying results. Yet, most of these studies did not consider the possibility of false positives, i.e. that respondents are misclassified as having a sensitive trait even though they actually do not. Assuming that respondents only falsely deny but never falsely admit possessing a sensitive trait, higher prevalence estimates have typically been interpreted as more valid estimates. If false positives occur, however, conclusions drawn under this assumption might be misleading. We present a comparative validation design that is able to detect false positives without the need for an individual-level validation criterion – which is often unavailable. Results show that the most widely used crosswise-model implementation produced false positives to a non-ignorable extent. This defect was not revealed by several previous validation studies that did not consider false positives - apparently a blind spot in past sensitive question research.

Suggested Citation

  • Marc Höglinger & Andreas Diekmann, 2016. "Uncovering a Blind Spot in Sensitive Question Research: False Positives Undermine the Crosswise-Model RRT," University of Bern Social Sciences Working Papers 24, University of Bern, Department of Social Sciences.
  • Handle: RePEc:bss:wpaper:24
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    References listed on IDEAS

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    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. Ben Jann, 2007. "Making regression tables simplified," Stata Journal, StataCorp LP, vol. 7(2), pages 227-244, June.
    3. Kirchner Antje, 2015. "Validating Sensitive Questions: A Comparison of Survey and Register Data," Journal of Official Statistics, Sciendo, vol. 31(1), pages 31-59, March.
    4. John, Leslie K. & Loewenstein, George & Acquisti, Alessandro & Vosgerau, Joachim, 2018. "When and why randomized response techniques (fail to) elicit the truth," Organizational Behavior and Human Decision Processes, Elsevier, vol. 148(C), pages 101-123.
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    Cited by:

    1. Chuang, Erica & Dupas, Pascaline & Huillery, Elise & Seban, Juliette, 2021. "Sex, lies, and measurement: Consistency tests for indirect response survey methods," Journal of Development Economics, Elsevier, vol. 148(C).

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    More about this item

    Keywords

    Sensitive Questions; Sensitive Survey Techniques; Randomized Response Technique; Crosswise Model; Item Count Technique; Data Validity; Social Desirability; Measurement Error; Survey Design;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods

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