IDEAS home Printed from https://ideas.repec.org/p/zbw/zeudps/39.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/39808/1/593464877.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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].
    6. 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.
    7. 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).
    8. 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.
    9. Michael Spagat, 2010. "Estimating the Human Costs of War: The Sample Survey Approach," HiCN Research Design Notes 14, Households in Conflict Network.

    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:zbw:zeudps:39. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zegiede.html .

    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.