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Methodenreport: Synthetische Scientific-Use-Files der Welle 2007 des IAB-Betriebspanels


  • Drechsler, Jörg

    () (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])


"Providing scientific use files for business surveys is a difficult task. Due to smaller populations, higher sampling rates, and skewed distributions disclosure risks are much higher than for household surveys. Simple measures like coarsening are not sufficient to protect the data. The aim of generating synthetic datasets is to release data that provide a high level of data utility while guaranteeing the confidentiality of the survey respondent. To achieve this, sensitive variables and variables that could be used for re-identification purposes are replaced with multiple imputations. This report gives a short introduction to the topic and discusses some aspects that analysts should keep in mind when using the synthetic datasets. Furthermore, the report describes how valid inferences can be obtained based on the synthetic datasets and provides some first data utility evaluations that indicate the potentials but also the limits of the generated datasets." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Drechsler, Jörg, 2011. "Methodenreport: Synthetische Scientific-Use-Files der Welle 2007 des IAB-Betriebspanels," FDZ Methodenreport 201101_de, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  • Handle: RePEc:iab:iabfme:201101_de

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    References listed on IDEAS

    1. Reiter, Jerome P. & Raghunathan, Trivellore E., 2007. "The Multiple Adaptations of Multiple Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1462-1471, December.
    2. Drechsler, Jörg & Dundler, Agnes & Bender, Stefan & Rässler, Susanne & Zwick, Thomas, 2007. "A new approach for disclosure control in the IAB Establishment Panel : multiple imputation for a better data access," IAB Discussion Paper 200711, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Jacobebbinghaus, Peter & Müller, Dana & Orban, Agnes, 2010. "How to use data swapping to create useful dummy data for panel datasets," FDZ Methodenreport 201003_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
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    Cited by:

    1. Peter Ellguth & Susanne Kohaut & Iris Möller, 2014. "The IAB Establishment Panel—methodological essentials and data quality [Das IAB-Betriebspanel: Methodische Grundlagen und Datenqualität]," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 47(1), pages 27-41, March.
    2. d'Andria, Diego & Uebelmesser, Silke, 2016. "The relationship between R&D intensity and profit-sharing schemes: evidence from Germany and the United Kingdom," VfS Annual Conference 2016 (Augsburg): Demographic Change 145622, Verein für Socialpolitik / German Economic Association.

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    IAB-Betriebspanel; Imputationsverfahren; Datenanonymisierung; Datenschutz; Betriebsdatenerfassung; Non Response;

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