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When and why randomized response techniques (fail to) elicit the truth

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  • John, Leslie K.
  • Loewenstein, George
  • Acquisti, Alessandro
  • Vosgerau, Joachim

Abstract

By adding random noise to individual responses, randomized response techniques (RRTs) are intended to enhance privacy protection and encourage honest disclosure of sensitive information. Empirical findings on their success in doing so are, however, mixed. In nine experiments, we show that the noise introduced by RRTs can make respondents concerned that innocuous responses will be interpreted as admissions, and as a result, yield prevalence estimates that are lower than direct questioning (Studies 1–4, 5A, & 6), less accurate than direct questioning (Studies 1, 3, 4B, & 5A), and even nonsensical (i.e., negative; Studies 3–6). Studies 2A and 2B show that the paradox is eliminated when the target behavior is socially desirable, even when it is merely framed as such. Study 3 shows the paradox is driven by respondents’ concerns over response misinterpretation. A simple modification designed to reduce concerns over response misinterpretation reduces the problem (Studies 4 & 5), particularly when such concerns are heightened (Studies 5 & 6).

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jobhdp:v:148:y:2018:i:c:p:101-123
    DOI: 10.1016/j.obhdp.2018.07.004
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    2. Andy C. Y. Chong & Amanda M. Y. Chu & Mike K. P. So & Ray S. W. Chung, 2019. "Asking Sensitive Questions Using the Randomized Response Approach in Public Health Research: An Empirical Study on the Factors of Illegal Waste Disposal," IJERPH, MDPI, vol. 16(6), pages 1-15, March.
    3. Burgstaller, Lilith & Feld, Lars P. & Pfeil, Katharina, 2022. "Working in the shadow: Survey techniques for measuring and explaining undeclared work," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 661-671.
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    5. Ivar Krumpal & Thomas Voss, 2020. "Sensitive Questions and Trust: Explaining Respondents’ Behavior in Randomized Response Surveys," SAGE Open, , vol. 10(3), pages 21582440209, July.
    6. Heng Xu & Nan Zhang, 2022. "Implications of Data Anonymization on the Statistical Evidence of Disparity," Management Science, INFORMS, vol. 68(4), pages 2600-2618, April.
    7. Sylvain Chassang & Christian Zehnder, 2019. "Secure Survey Design in Organizations: Theory and Experiments," NBER Working Papers 25918, National Bureau of Economic Research, Inc.
    8. Julia Meisters & Adrian Hoffmann & Jochen Musch, 2020. "Controlling social desirability bias: An experimental investigation of the extended crosswise model," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-13, December.

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