IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v53y2024i1p328-368.html

A New Approach to Detecting Cheating in Sensitive Surveys: The Cheating Detection Triangular Model

Author

Listed:
  • Julia Meisters
  • Adrian Hoffmann
  • Jochen Musch

Abstract

Indirect questioning techniques such as the randomized response technique aim to control social desirability bias in surveys of sensitive topics. To improve upon previous indirect questioning techniques, we propose the new Cheating Detection Triangular Model. Similar to the Cheating Detection Model, it includes a mechanism for detecting instruction non-adherence, and similar to the Triangular Model, it uses simplified instructions to improve respondents’ understanding of the procedure. Based on a comparison with the known prevalence of a sensitive attribute serving as external criterion, we report the first individual-level validation of the Cheating Detection Model, the Triangular Model and the Cheating Detection Triangular Model. Moreover, the sensitivity and specificity of all models was assessed, as well as the respondents’ subjective evaluation of all questioning technique formats. Based on our results, the Cheating Detection Triangular Model appears to be the best choice among the investigated indirect questioning techniques.

Suggested Citation

  • Julia Meisters & Adrian Hoffmann & Jochen Musch, 2024. "A New Approach to Detecting Cheating in Sensitive Surveys: The Cheating Detection Triangular Model," Sociological Methods & Research, , vol. 53(1), pages 328-368, February.
    • Handle: RePEc:sae:somere:v:53:y:2024:i:1:p:328-368
    DOI: 10.1177/00491241211055764
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/00491241211055764
    Download Restriction: no
    File URL: https://libkey.io/10.1177/00491241211055764?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Arijit Chaudhuri & Tasos C. Christofides, 2013. "Indirect Questioning in Sample Surveys," Springer Books, Springer, edition 127, number 978-3-642-36276-7, March.
    2. 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.
    3. Xiangen Hu & William Batchelder, 1994. "The statistical analysis of general processing tree models with the EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 59(1), pages 21-47, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Julia Meisters & Adrian Hoffmann & Jochen Musch, 2020. "Can detailed instructions and comprehension checks increase the validity of crosswise model estimates?," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    2. 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.
    3. Fabiola Reiber & Harrison Pope & Rolf Ulrich, 2023. "Cheater Detection Using the Unrelated Question Model," Sociological Methods & Research, , vol. 52(1), pages 389-411, February.
    4. Yamen, Ahmed & Allam, Amir & Bani-Mustafa, Ahmed & Uyar, Ali, 2018. "Impact of institutional environment quality on tax evasion: A comparative investigation of old versus new EU members," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 32(C), pages 17-29.
    5. Marc Jekel & Andreas Glockner & Arndt Broder & Viktoriya Maydych, 2014. "Approximating rationality under incomplete information: Adaptive inferences for missing cue values based on cue-discrimination," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(2), pages 129-147, March.
    6. Mario Bossler & Christopher Osiander & Julia Schmidtke & Mark Trappmann, 2023. "Free riding on short‐time work allowances? Results from an experimental survey design," Kyklos, Wiley Blackwell, vol. 76(4), pages 882-901, November.
    7. Ning Ding & Xinnan Zhang & Yiming Zhai & Chenglong Li, 2021. "Risk assessment of VAT invoice crime levels of companies based on DFPSVM: a case study in China," Risk Management, Palgrave Macmillan, vol. 23(1), pages 75-96, June.
    8. Rubens Moura de Carvalho & Helena Coelho Inácio & Rui Pedro Marques, 2024. "An Empirical Analysis of Tax Evasion among Companies Engaged in Stablecoin Transactions," JRFM, MDPI, vol. 17(9), pages 1-18, September.
    9. 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.
    10. 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.
    11. Dora Matzke & Conor Dolan & William Batchelder & Eric-Jan Wagenmakers, 2015. "Bayesian Estimation of Multinomial Processing Tree Models with Heterogeneity in Participants and Items," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 205-235, March.
    12. Jonas Moss, 2023. "Measuring Agreement Using Guessing Models and Knowledge Coefficients," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1002-1025, September.
    13. María del Mar García Rueda & Pier Francesco Perri & Beatriz Rodríguez Cobo, 2018. "Advances in estimation by the item sum technique using auxiliary information in complex surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 455-478, July.
    14. 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.
    15. Florian Wickelmaier & Achim Zeileis, 2016. "Using Recursive Partitioning to Account for Parameter Heterogeneity in Multinomial Processing Tree Models," Working Papers 2016-26, Faculty of Economics and Statistics, Universität Innsbruck.
    16. Rainer Schnell & Kathrin Thomas, 2023. "A Meta-analysis of Studies on the Performance of the Crosswise Model," Sociological Methods & Research, , vol. 52(3), pages 1493-1518, August.
    17. Lukovszki, Lívia & Gorjanecz, György, 2025. "Két ország, két út: Minimálbérrel élők adóterhei és jó(l)léte Írországban és Magyarországon [Two countries, two paths. Tax burdens and well-being of minimum wage earners in Ireland and Hungary]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 488-515.
    18. Lívia Lukovszki & Norbert Sipos & András Rideg & Zsófia Vörös, 2025. "Personality aspects of tax compliance," European Journal of Law and Economics, Springer, vol. 59(3), pages 457-490, June.
    19. 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).
    20. repec:plo:pone00:0243384 is not listed on IDEAS
    21. Abaei, Mohammad Mahdi & Hekkenberg, Robert & BahooToroody, Ahmad, 2021. "A multinomial process tree for reliability assessment of machinery in autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 210(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:somere:v:53:y:2024:i:1:p:328-368. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.