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More Is Not Always Better: An Experimental Individual-Level Validation of the Randomized Response Technique and the Crosswise Model

Author

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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 therefore be subject to considerable misreporting. To mitigate such misreporting, various indirect techniques for asking sensitive questions, such as the randomized response technique (RRT), have been proposed in the literature. In our study, we evaluate the viability of several 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 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 (that is, an increase in the proportion of cheaters who admit having cheated). At the same time, however, there is an increase in false positives (that is, an increase in non-cheaters who falsely admit having cheated). Overall, our findings suggest that none of the implemented sensitive questions techniques substantially outperforms direct questioning. Furthermore, our study demonstrates the importance of distinguishing false negatives and false positives when evaluating the validity of sensitive question techniques.

Suggested Citation

  • Marc Höglinger & Ben Jann, 2016. "More Is Not Always Better: An Experimental Individual-Level Validation of the Randomized Response Technique and the Crosswise Model," University of Bern Social Sciences Working Papers 18, University of Bern, Department of Social Sciences.
    • Handle: RePEc:bss:wpaper:18
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    References listed on IDEAS

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    9. 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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    12. 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.
    13. 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.
    14. Graeme Blair & Kosuke Imai & Yang-Yang Zhou, 2015. "Design and Analysis of the Randomized Response Technique," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1304-1319, September.
    15. Kundt, Thorben C. & Misch, Florian & Nerré, Birger, 2013. "Re-assessing the merits of measuring tax evasions through surveys: Evidence from Serbian firms," ZEW Discussion Papers 13-047, ZEW - Leibniz Centre for European Economic Research.
    16. 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.
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    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Ivar Krumpal & Thomas Voss, 2020. "Sensitive Questions and Trust: Explaining Respondents’ Behavior in Randomized Response Surveys," SAGE Open, , vol. 10(3), pages 21582440209, July.
    14. 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.
    15. 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).
    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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    More about this item

    Keywords

    Sensitive Questions; Online Survey; Amazon Mechanical Turk; Randomized Response Technique; Crosswise Model; Dice Game; Validation;
    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

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