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How valid can data fusion be?

Author

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  • Kiesl, Hans
  • Rässler, Susanne

Abstract

"Data fusion techniques typically aim to achieve a complete data file from different sources which do not contain the same units. Traditionally, this is done on the basis of variables common to all files. It is well known that those approaches establish conditional independence of the specific variables given the common variables, although they may be conditionally dependent in reality. We discuss the objectives of data fusion in the light of their feasibility and distinguish four levels of validity that a fusion technique may achieve. For a rather general situation, we derive the feasible set of correlation matrices for the variables not jointly observed and suggest a new quality index for data fusion. Finally, we present a suitable and effcient multiple imputation procedure to make use of auxiliary information and to overcome the conditional independence assumption." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Kiesl, Hans & Rässler, Susanne, 2006. "How valid can data fusion be?," IAB-Discussion Paper 200615, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:200615
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    File URL: https://doku.iab.de/discussionpapers/2006/dp1506.pdf
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    References listed on IDEAS

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    1. Rodgers, Willard L, 1984. "An Evaluation of Statistical Matching," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 91-102, January.
    2. Moriarity, Chris & Scheuren, Fritz, 2003. "A Note on Rubin's Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 65-73, January.
    3. Donald Rubin & Dorothy Thayer, 1978. "Relating tests given to different samples," Psychometrika, Springer;The Psychometric Society, vol. 43(1), pages 3-10, March.
    4. Ridder, Geert & Moffitt, Robert, 2007. "The Econometrics of Data Combination," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 75, Elsevier.
    5. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Hohendanner, Christian, 2007. "Verdrängen Ein-Euro-Jobs sozialversicherungspflichtige Beschäftigung in den Betrieben?," IAB-Discussion Paper 200708, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Kruppe, Thomas, 2006. "Die Förderung beruflicher Weiterbildung : eine mikroökonometrische Evaluation der Ergänzung durch das ESF-BA-Programm," IAB-Discussion Paper 200621, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Blien, Uwe & Kirchhof, Kai & Ludewig, Oliver, 2006. "Agglomeration effects on labour demand," IAB-Discussion Paper 200628, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. Andrea Cutillo & Mauro Scanu, 2020. "A Mixed Approach for Data Fusion of HBS and SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 150(2), pages 411-437, July.
    5. Zvi Gilula & Robert McCulloch, 2013. "Multi level categorical data fusion using partially fused data," Quantitative Marketing and Economics (QME), Springer, vol. 11(3), pages 353-377, September.
    6. Frethey-Bentham, Catherine, 2011. "Pseudo panels as an alternative study design," Australasian marketing journal, Elsevier, vol. 19(4), pages 281-292.
    7. Eckey, Hans-Friedrich & Schwengler, Barbara & Türck, Matthias, 2007. "Vergleich von deutschen Arbeitsmarktregionen," IAB-Discussion Paper 200703, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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    More about this item

    Keywords

    Datenaufbereitung ; Datenqualität ; Imputationsverfahren ; Datenfusion ; Korrelation ; mathematische Statistik ; angewandte Statistik ; Validität;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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