A statistical approach to detect cheating interviewers
AbstractSurvey 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. --
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Justus Liebig University Giessen, Center for international Development and Environmental Research (ZEU) in its series Discussion Papers with number 39.
Date of creation: 2008
Date of revision:
Contact details of provider:
Web page: http://www.uni-giessen.de/cms/faculties/research-centers/zeu-en/view
More information through EDIRC
cheating interviewers; Benford's law; cluster analysis; data fabrication;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- 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.
- 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.
- 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).
- Joerg-Peter Schraepler, 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), Justus-Liebig University Giessen, Department of Statistics and Economics, Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(5-6), pages 685-718, November.
- 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).
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics).
If references are entirely missing, you can add them using this form.