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A statistical approach to detect cheating interviewers

Author

Listed:
  • Bredl, Sebastian
  • Winker, Peter
  • Kötschau, Kerstin

Abstract

Survey data are potentially affected by cheating interviewers. Even a small number of fabricated interviews might seriously impair the results of further empirical analysis. Besides reinterviews some statistical approaches have been proposed for identifying fabrication of interviews. As a novel toolin this context, cluster and discriminant analysis are used. Several indicators are combined to classify 'at risk' interviewers based solely on the collected data. An application to a dataset with known cases of cheating interviewers demonstrates that the methods are able to identify the cheating interviewers with a high probability. The multivariate classiffication is superior to the application of a singleindicator such as Benford's law.

Suggested Citation

  • Bredl, Sebastian & Winker, Peter & Kötschau, Kerstin, 2008. "A statistical approach to detect cheating interviewers," Discussion Papers 39, Justus Liebig University Giessen, Center for international Development and Environmental Research (ZEU).
    • Handle: RePEc:zbw:zeudps:39
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    Citations

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    Cited by:

    1. Hatice Uenal & David Hampel, 2017. "Economic Aspects of the Missing Data Problem - the Case of the Patient Registry," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(5), pages 1779-1791.
    2. De Haas Samuel & Winker Peter, 2016. "Detecting Fraudulent Interviewers by Improved Clustering Methods – The Case of Falsifications of Answers to Parts of a Questionnaire," Journal of Official Statistics, Sciendo, vol. 32(3), pages 643-660, September.
    3. Schräpler Jörg-Peter, 2011. "Benford’s Law as an Instrument for Fraud Detection in Surveys Using the Data of the Socio-Economic Panel (SOEP)," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(5-6), pages 685-718, October.
    4. Finn, Arden & Ranchhod, Vimal, 2013. "Genuine Fakes: The prevalence and implications of fieldworker fraud in a large South African survey," SALDRU Working Papers 115, Southern Africa Labour and Development Research Unit, University of Cape Town.
    5. Storfinger, Nina & Winker, Peter, 2011. "Robustness of clustering methods for identification of potential falsifications in survey data," Discussion Papers 57, Justus Liebig University Giessen, Center for international Development and Environmental Research (ZEU).
    6. Josten Michael & Trappmann Mark, 2016. "Interviewer Effects on a Network-Size Filter Question," Journal of Official Statistics, Sciendo, vol. 32(2), pages 349-373, June.
    7. Mario Gyori & Tatiana Martínez Zavala & Jessica Baier & Maria Hernandez & Sofie Olsson & Alexis Lefevre, 2017. "Social and Behaviour Change Communication (SBCC) project in Manica, Mozambique: baseline survey report," Working Papers 162, International Policy Centre for Inclusive Growth.
    8. Michael Spagat, 2010. "Estimating the Human Costs of War: The Sample Survey Approach," HiCN Research Design Notes 14, Households in Conflict Network.
    9. Kosyakova, Yuliya & Olbrich, Lukas & Sakshaug, Joseph & Schwanhäuser, Silvia, 2019. "Identification of interviewer falsification in the IAB-BAMF-SOEP Survey of Refugees in Germany," FDZ Methodenreport 201902_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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