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A statistical approach to detect cheating interviewers

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Author Info

  • 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. --

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Bibliographic Info

Paper provided by Justus Liebig University Giessen, Center for international Development and Environmental Research (ZEU) in its series Discussion Papers with number 39.

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Date of creation: 2008
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Handle: RePEc:zbw:zeudps:39

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Web page: http://www.uni-giessen.de/cms/faculties/research-centers/zeu-en/view
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Related research

Keywords: cheating interviewers; Benford's law; cluster analysis; data fabrication;

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Cited by:
  1. Finn, Arden & Ranchhod, Vimal, 2013. "Genuine Fakes: The prevalence and implications of fieldworker fraud in a large South African survey," SALDRU Working Papers, Southern Africa Labour and Development Research Unit, University of Cape Town 115, Southern Africa Labour and Development Research Unit, University of Cape Town.
  2. Michael Spagat, 2010. "Estimating the Human Costs of War: The Sample Survey Approach," HiCN Research Design Notes, Households in Conflict Network 14, Households in Conflict Network.
  3. Jörg-Peter Schräpler, 2010. "Benford's Law As an Instrument for Fraud Detection in Surveys Using the Data of the Socio-Economic Panel (SOEP)," SOEPpapers on Multidisciplinary Panel Data Research, DIW Berlin, The German Socio-Economic Panel (SOEP) 273, DIW Berlin, The German Socio-Economic Panel (SOEP).
  4. Storfinger, Nina & Winker, Peter, 2011. "Robustness of clustering methods for identification of potential falsifications in survey data," Discussion Papers, Justus Liebig University Giessen, Center for international Development and Environmental Research (ZEU) 57, Justus Liebig University Giessen, Center for international Development and Environmental Research (ZEU).

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