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Sensitive Questions in Online Surveys: Experimental Results for the Randomized Response Technique (RRT) and the Unmatched Count Technique (UCT)

Author

Listed:
  • Elisabeth Coutts

    (ETH, Zurich, Switzerland)

  • Ben Jann

    (University of Bern, Switzerland, jann@soz.unibe.ch)

Abstract

Gaining valid answers to so-called sensitive questions is an age-old problem in survey research. Various techniques have been developed to guarantee anonymity and minimize the respondent’s feelings of jeopardy. Two such techniques are the randomized response technique (RRT) and the unmatched count technique (UCT). In this study the authors evaluate the effectiveness of different implementations of the RRT (using a forced-response design) in a computer-assisted setting and also compare the use of the RRT to that of the UCT. The techniques are evaluated according to various quality criteria, such as the prevalence estimates they provide, the ease of their use, and respondent trust in the techniques. The results indicate that the RRTs are problematic with respect to several domains, such as the limited trust they inspire and nonresponse, and that the RRT estimates are unreliable due to a strong false no bias, especially for the more sensitive questions. The UCT, however, performed well compared to the RRTs on all the evaluated measures. The authors conclude that the UCT is a promising alternative to RRT in self-administered surveys and that future research should be directed toward evaluating and improving the technique.

Suggested Citation

  • Elisabeth Coutts & Ben Jann, 2011. "Sensitive Questions in Online Surveys: Experimental Results for the Randomized Response Technique (RRT) and the Unmatched Count Technique (UCT)," Sociological Methods & Research, , vol. 40(1), pages 169-193, February.
  • Handle: RePEc:sae:somere:v:40:y:2011:i:1:p:169-193
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    Citations

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    Cited by:

    1. Thorben C. Kundt & Florian Misch & Birger Nerré, 2017. "Re-assessing the merits of measuring tax evasion through business surveys: an application of the crosswise model," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(1), pages 112-133, February.
    2. Liu, Yin & Tian, Guo-Liang, 2013. "A variant of the parallel model for sample surveys with sensitive characteristics," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 115-135.
    3. Katherine B. Coffman & Lucas C. Coffman & Keith M. Marzilli Ericson, 2013. "The Size of the LGBT Population and the Magnitude of Anti-Gay Sentiment are Substantially Underestimated," NBER Working Papers 19508, National Bureau of Economic Research, Inc.
    4. Gueorguiev, Dimitar & Malesky, Edmund, 2012. "Foreign investment and bribery: A firm-level analysis of corruption in Vietnam," Journal of Asian Economics, Elsevier, vol. 23(2), pages 111-129.
    5. Kuha, Jouni & Jackson, Jonathan, 2014. "The item count method for sensitive survey questions: modelling criminal behaviour," LSE Research Online Documents on Economics 48069, London School of Economics and Political Science, LSE Library.
    6. 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.
    7. Anatol-Fiete Näher & Ivar Krumpal, 2012. "Asking sensitive questions: the impact of forgiving wording and question context on social desirability bias," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(5), pages 1601-1616, August.
    8. Ivar Krumpal, 2013. "Determinants of social desirability bias in sensitive surveys: a literature review," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2025-2047, June.
    9. Artem Shcherbina & Rostyslav Maiboroda, 2011. "Finite mixtures model approach to sensitive questions in surveys," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 12(2), pages 331-344, October.
    10. Antonio Arcos & María del Rueda & Sarjinder Singh, 2015. "A generalized approach to randomised response for quantitative variables," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1239-1256, May.
    11. 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.
    12. Klaus Friesenbichler & George Clarke & Michael Wong, 2014. "Price competition and market transparency: evidence from a random response technique," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 5-21, February.
    13. 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.

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