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More is not always better: An experimental individual-level validation of the randomized response technique and the crosswise model

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  • Marc Höglinger
  • Ben Jann

Abstract

Social desirability and the fear of sanctions can deter survey respondents from responding truthfully to sensitive questions. Self-reports on norm breaking behavior such as shoplifting, non-voting, or tax evasion may thus be subject to considerable misreporting. To mitigate such response bias, various indirect question techniques, such as the randomized response technique (RRT), have been proposed. We evaluate the viability of several popular variants of the RRT, including the recently proposed crosswise-model RRT, by comparing respondents’ self-reports on cheating in dice games to actual cheating behavior, thereby distinguishing between false negatives (underreporting) and false positives (overreporting). The study has been implemented as an online survey on Amazon Mechanical Turk (N = 6, 505). Our results from two validation designs indicate that the forced-response RRT and the unrelated-question RRT, as implemented in our survey, fail to reduce the level of misreporting compared to conventional direct questioning. For the crosswise-model RRT we do observe a reduction of false negatives. At the same time, however, there is a non-ignorable increase in false positives; a flaw that previous evaluation studies relying on comparative or aggregate-level validation could not detect. Overall, none of the evaluated indirect techniques outperformed conventional direct questioning. Furthermore, our study demonstrates the importance of identifying false negatives as well as false positives to avoid false conclusions about the validity of indirect sensitive question techniques.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0201770
    DOI: 10.1371/journal.pone.0201770
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    9. Höglinger, Marc & Diekmann, Andreas, 2017. "Uncovering a Blind Spot in Sensitive Question Research: False Positives Undermine the Crosswise-Model RRT," Political Analysis, Cambridge University Press, vol. 25(1), pages 131-137, January.
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    12. Marc Höglinger & Ben Jann & Andreas Diekmann, 2014. "Sensitive Questions in Online Surveys: An Experimental Evaluation of the Randomized Response Technique and the Crosswise Model," University of Bern Social Sciences Working Papers 9, University of Bern, Department of Social Sciences, revised 24 Jun 2014.
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    Cited by:

    1. Höglinger, Marc & Diekmann, Andreas, 2017. "Uncovering a Blind Spot in Sensitive Question Research: False Positives Undermine the Crosswise-Model RRT," Political Analysis, Cambridge University Press, vol. 25(1), pages 131-137, January.
    2. David Sungho Park & Shilpa Aggarwal & Dahyeon Jeong & Naresh Kumar & Jonathan Robinson & Alan Spearot, 2021. "Private but Misunderstood? Evidence on Measuring Intimate Partner Violence via Self-Interviewing in Rural Liberia and Malawi," NBER Working Papers 29584, National Bureau of Economic Research, Inc.
    3. 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).
    4. 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.
    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. Marco Gregori & Martijn G. Jong & Rik Pieters, 2024. "The Crosswise Model for Surveys on Sensitive Topics: A General Framework for Item Selection and Statistical Analysis," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 1007-1033, September.
    7. Ulrich Thy Jensen, 2020. "Is self-reported social distancing susceptible to social desirability bias? Using the crosswise model to elicit sensitive behaviors," Journal of Behavioral Public Administration, Center for Experimental and Behavioral Public Administration, vol. 3(2).
    8. Ham, Andrés & Guarín, Ángela & Ruiz, Juanita, 2024. "How accurately are household surveys measuring the LGBT population in Colombia? Evidence from a list experiment," Labour Economics, Elsevier, vol. 87(C).
    9. Pier Francesco Perri & Eleni Manoli & Tasos C. Christofides, 2023. "Assessing the effectiveness of indirect questioning techniques by detecting liars," Statistical Papers, Springer, vol. 64(5), pages 1483-1506, October.
    10. 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.
    11. Adrian Hoffmann & Julia Meisters & Jochen Musch, 2021. "Nothing but the truth? Effects of faking on the validity of the crosswise model," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-20, October.
    12. Daoust, Jean-François & Bélanger, Éric & Dassonneville, Ruth & Lachapelle, Erick & Nadeau, Richard & Becher, Michael & Brouard, Sylvain & Foucault, Martial & Hönnige, Christoph & Stegmueller, Daniel, 2020. "Face-Saving Strategies Increase Self-Reported Non-Compliance with COVID-19 Preventive Measures: Experimental Evidence from 12 Countries," SocArXiv tkrs7, Center for Open Science.
    13. Assefa, Thomas W. & Kadam, Aditi & Magnan, Nicholas & McCullough, Ellen & McGavock, Tamara, 2022. "Who is asking and how? The effects of enumerator gender and survey method in measuring intimate partner violence," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322543, Agricultural and Applied Economics Association.
    14. Ó Ceallaigh, Diarmaid & Timmons, Shane & Robertson, Deirdre & Lunn, Pete, 2023. "Problem gambling: A narrative review of important policy-relevant issues," Research Series, Economic and Social Research Institute (ESRI), number SUSTAT119.
    15. S. Rinken & S. Pasadas-del-Amo & M. Rueda & B. Cobo, 2021. "No magic bullet: estimating anti-immigrant sentiment and social desirability bias with the item-count technique," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2139-2159, December.
    16. Adetola Adedamola Adediran & Femi Barnabas Adebola & Olusegun Sunday Ewemooje, 2020. "Unbiased estimator modeling in unrelated dichotomous randomized response," Statistics in Transition New Series, Polish Statistical Association, vol. 21(5), pages 119-132, December.

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    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

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