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Diagnose von Fehlerquellen und methodische Qualität in der sozialwissenschaftlichen Forschung [Sources of Bias and Quality of Data in Social Science Research]

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  • Andreas Diekmann

Abstract

Surveys supply us with the statistical data for economic and administrative decisions.Errors in survey research may lead to wrong and costly decisions. For a better control of the quality of research, methodological standards and validity criteria should be communicated more clearly. In this paper, sources of bias in interviews, sampling, and data analysis are discussed. An often neglected sorce of bias is the fraudulent manipulation of data. How can faked data be detected? In a final section new tests using the Benford distribution are explored. However, more research is needed to ascertain the validity of Benford tests. -- Umfragen liefern heute das Zahlenmaterial für wirtschaftliche und administrative Entscheidungen sowie sozialwissenschaftliche Untersuchungen. Fehler können dabei teuer zu stehen kommen. Der per Befragung erhobene IFO-Geschäftsklima-Index zum Beispiel hat nach Bekanntgabe unmittelbaren Einfluss auf den Kurs des Euro. Media-Analysen entscheiden über die Verteilung umfangreicher Werbebudgets. Und Irrtümer bei der Ermittlung des Preisindex wirken sich auf zahlreiche Verträge aus, die direkt oder indirekt mit dem Index verkoppelt sind. Die Sicherung methodischer Qualität und Transparenz bezüglich der Gütekriterien von Erhebungen ist daher eine nachdrückliche Forderung. In dem Vortrag werden Fehlerquellen bei der Befragung, der Stichprobenziehung und der Datenanalyse aufgezeigt sowie Möglichkeiten zur Behebung zumindest einiger typischer Fehlerquellen vorgeschlagen. Dabei wird auch das Thema betrügerischer Datenmanipulationen angesprochen. Zur Diagnose von Datenfälschungen wurden in jüngster Zeit Testverfahren entwickelt, die sich allerdings noch in der Erprobungsphase befinden. Diese Verfahren und ihre Anwendungsmöglichkeiten werden abschließend vorgestellt.

Suggested Citation

  • Andreas Diekmann, 2002. "Diagnose von Fehlerquellen und methodische Qualität in der sozialwissenschaftlichen Forschung [Sources of Bias and Quality of Data in Social Science Research]," ITA manu:scripts 02_04, Institute of Technology Assessment (ITA).
    • Handle: RePEc:ita:itaman:02_04
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    References listed on IDEAS

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    1. Georg Aichholzer, 2002. "Das ExpertInnen-Delphi: methodische Grundlagen und Anwendungsfeld ‘Technology Foresight‘ (The Expert Delphi: Methodology and Application in 'Technology Foresight')," ITA manu:scripts 02_01, Institute of Technology Assessment (ITA).
    2. Georg Aichholzer, 2001. "Delphi Austria - An Example of Tailoring Foresight to the Needs of a Small Country," ITA manu:scripts 01_02, Institute of Technology Assessment (ITA).
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    Cited by:

    1. 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.
    2. Andreas Diekmann & Ben Jann, 2010. "Benford's Law and Fraud Detection: Facts and Legends," German Economic Review, Verein für Socialpolitik, vol. 11(3), pages 397-401, August.

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    Keywords

    Methodenprobleme; Gütekriterien; Fehlerquellen; Ausschöpfungsquote; Datenfälschung; Benford-Gesetz;
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