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Plagiarism in Student Papers: Prevalence Estimates Using Special Techniques for Sensitive Questions

Author

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  • Coutts Elisabethen

    (Zurich)

  • Jann Ben

    (University of Bern, Institute of Sociology, Lerchenweg 36, 3012 Bern, Switzerland)

  • Krumpal Ivar
  • Näher Anatol-Fiete

    (University of Leipzig, Institute of Sociology, Beethovenstrasse 15, 04107 Leipzig, Germany)

Abstract

This article evaluates three different questioning techniques for measuring the prevalence of plagiarism in student papers: the randomized response technique (RRT), the item count technique (ICT), and the crosswise model (CM). In three independent experimental surveys with Swiss and German university students as subjects (two web surveys and a survey using paper and- pencil questionnaires in a classroom setting), each of the three techniques is compared to direct questioning and evaluated based on the “more-is-better” assumption. According to our results the RRT and the ICT failed to reduce social desirability bias in self-reports of plagiarism. In contrast, the CM was more successful in eliciting a significantly higher rate of reported sensitive behavior than direct questioning. One reason for the success of the CM, we believe, is that it overcomes the “self-protective no” bias known from the RRT (and which may also be a potential problem in the ICT).We find rates of up to 22 percent of students who declared that they ever intentionally adopted a passage from someone else’s work without citing it. Severe plagiarism such as handing in someone else’s paper as one’s own, however, seems to be less frequent with rates of about 1 to 2 percent.

Suggested Citation

  • 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.
    • Handle: RePEc:jns:jbstat:v:231:y:2011:i:5-6:p:749-760
    DOI: 10.1515/jbnst-2011-5-612
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    References listed on IDEAS

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    1. 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.
    2. Johannes Landsheer & Peter Van Der Heijden & Ger Van Gils, 1999. "Trust and Understanding, Two Psychological Aspects of Randomized Response," Quality & Quantity: International Journal of Methodology, Springer, vol. 33(1), pages 1-12, February.
    3. Buchman, Ta & Tracy, Ja, 1982. "Obtaining Responses To Sensitive Questions - Conventional Questionnaire Versus Randomized-Response Technique," Journal of Accounting Research, Wiley Blackwell, vol. 20(1), pages 263-271.
    4. James Abernathy & Bernard Greenberg & Daniel Horvitz, 1970. "Estimates of induced abortion in urban North Carolina," Demography, Springer;Population Association of America (PAA), vol. 7(1), pages 19-29, February.
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    Cited by:

    1. Ivar Krumpal & Thomas Voss, 2020. "Sensitive Questions and Trust: Explaining Respondents’ Behavior in Randomized Response Surveys," SAGE Open, , vol. 10(3), pages 21582440209, July.
    2. 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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    4. 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.
    5. McGinnity, Frances & Creighton, Mathew & Fahey, Éamonn, 2020. "Hidden versus revealed attitudes: a list experiment on support for minorities in Ireland," Research Series, Economic and Social Research Institute (ESRI), number BKMNEXT372, June.
    6. 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.
    7. 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.
    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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