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Synthetic data for open and reproducible methodological research in social sciences and official statistics

Author

Listed:
  • Jan Pablo Burgard

    (Universität Trier)

  • Jan-Philipp Kolb

    (GESIS)

  • Hariolf Merkle

    (Universität Trier)

  • Ralf Münnich

    (Universität Trier)

Abstract

Open and reproducible research receives more and more attention in the research community. Whereas empirical research may benefit from research data centres or scientific use files that foster using data in a safe environment or with remote access, methodological research suffers from the availability of adequate data sources. In economic and social sciences, an additional drawback results from the presence of complex survey designs in the data generating process, that has to be considered when developing and applying estimators. In the present paper, we present a synthetic but realistic dataset based on social science data, that fosters evaluating and developing estimators in social sciences. The focus is on supporting comparable and reproducible research in a realistic framework providing individual and household data. The outcome is provided as an open research data resource.

Suggested Citation

  • Jan Pablo Burgard & Jan-Philipp Kolb & Hariolf Merkle & Ralf Münnich, 2017. "Synthetic data for open and reproducible methodological research in social sciences and official statistics," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 233-244, December.
    • Handle: RePEc:spr:astaws:v:11:y:2017:i:3:d:10.1007_s11943-017-0214-8
    DOI: 10.1007/s11943-017-0214-8
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    References listed on IDEAS

    as
    1. Eurosystem Household Finance and Consumption Network, 2013. "The Eurosystem Household Finance and Consumption Survey - Methodological report," Statistics Paper Series 1, European Central Bank.
    2. Reiter, Jerome P. & Drechsler, Jörg, 2007. "Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality," IAB-Discussion Paper 200720, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. 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].
    4. Eurosystem Household Finance and Consumption Network, 2013. "The Eurosystem Household Finance and Consumption Survey - Results from the first wave," Statistics Paper Series 2, European Central Bank.
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    Citations

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    Cited by:

    1. Angelo Moretti, 2023. "Estimation of small area proportions under a bivariate logistic mixed model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3663-3684, August.
    2. Jan Pablo Burgard & Ralf Münnich & Martin Rupp, 2019. "A Generalized Calibration Approach Ensuring Coherent Estimates with Small Area Constraints," Research Papers in Economics 2019-10, University of Trier, Department of Economics.
    3. Jan Pablo Burgard & Joscha Krause & Ralf Münnich, 2020. "A Study of Discontinuity Effects in Regression Inference based on Web-Augmented Mixed Mode Surveys," Research Papers in Economics 2020-03, University of Trier, Department of Economics.
    4. Saeideh Kamgar & Florian Meinfelder & Ralf Münnich & Hamidreza Navvabpour, 2020. "Estimation within the new integrated system of household surveys in Germany," Statistical Papers, Springer, vol. 61(5), pages 2091-2117, October.
    5. Christian Bruch, 2022. "Applying the rescaling bootstrap under imputation for a multistage sampling design," Computational Statistics, Springer, vol. 37(3), pages 1461-1494, July.
    6. Anne Konrad & Jan Pablo Burgard & Ralf Münnich, 2021. "A Two‐level GREG Estimator for Consistent Estimation in Household Surveys," International Statistical Review, International Statistical Institute, vol. 89(3), pages 635-656, December.
    7. Jan Pablo Burgard & Patricia Dörr & Ralf Münnich, 2020. "Monte-Carlo Simulation Studies in Survey Statistics – An Appraisal," Research Papers in Economics 2020-04, University of Trier, Department of Economics.
    8. Timo Schmid & Markus Zwick, 2017. "Vorwort der Herausgeber," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 143-146, December.

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