IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v34y2018i4p863-888n4.html
   My bibliography  Save this article

Detecting Reporting Errors in Data from Decentralised Autonomous Administrations with an Application to Hospital Data

Author

Listed:
  • van Delden Arnout
  • van der Laan Jan

    (Statistics Netherlands, Department of Process Development and Methodology, Henri Faasdreef 312, P.O. Box 24500, 2490 HA The Hague, The Netherlands.)

  • Prins Annemarie

    (Netherlands Institute for Health Services Research (Nivel), P.O.Box 1568, 3500 BN Utrecht, The Netherlands.)

Abstract

Administrative data sources are increasingly used by National Statistical Institutes to compile statistics. These sources may be based on decentralised autonomous administrations, for instance municipalities that deliver data on their inhabitants. One issue that may arise when using these decentralised administrative data is that categorical variables are underreported by some of the data suppliers, for instance to avoid administrative burden. Under certain conditions overreporting may also occur.

Suggested Citation

  • van Delden Arnout & van der Laan Jan & Prins Annemarie, 2018. "Detecting Reporting Errors in Data from Decentralised Autonomous Administrations with an Application to Hospital Data," Journal of Official Statistics, Sciendo, vol. 34(4), pages 863-888, December.
  • Handle: RePEc:vrs:offsta:v:34:y:2018:i:4:p:863-888:n:4
    DOI: 10.2478/jos-2018-0043
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/jos-2018-0043
    Download Restriction: no

    File URL: https://libkey.io/10.2478/jos-2018-0043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. D. L. Oberski & A. Kirchner & S. Eckman & F. Kreuter, 2017. "Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1477-1489, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    2. Ogbonnaya, Ijeoma Nwabuzor & Keeney, Annie J., 2018. "A systematic review of the effectiveness of interagency and cross-system collaborations in the United States to improve child welfare outcomes," Children and Youth Services Review, Elsevier, vol. 94(C), pages 225-245.
    3. Stüber, Heiko & Grabka, Markus M. & Schnitzlein, Daniel D., 2023. "A tale of two data sets: comparing German administrative and survey data using wage inequality as an example," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 57, pages 1-8.
    4. Pina-Sánchez, Jose & Buil-Gil, David & brunton-smith, ian & Cernat, Alexandru, 2021. "The impact of measurement error in models using police recorded crime rates," SocArXiv ydf4b, Center for Open Science.
    5. Bosch Jover, Oriol & Revilla, Melanie, 2022. "When survey science met web tracking: presenting an error framework for metered data," LSE Research Online Documents on Economics 116431, London School of Economics and Political Science, LSE Library.
    6. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," IZA Discussion Papers 10943, Institute of Labor Economics (IZA).
    7. Bruce D. Meyer & Nikolas Mittag, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," NBER Working Papers 25738, National Bureau of Economic Research, Inc.
    8. Oriol J. Bosch & Melanie Revilla, 2022. "When survey science met web tracking: Presenting an error framework for metered data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 408-436, December.
    9. Gessendorfer Jonathan & Beste Jonas & Drechsler Jörg & Sakshaug Joseph W., 2018. "Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?," Journal of Official Statistics, Sciendo, vol. 34(4), pages 909-933, December.
    10. Luis Ayala & Ana Pérez & Mercedes Prieto-Alaiz, 2022. "The impact of different data sources on the level and structure of income inequality," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(3), pages 583-611, September.
    11. David J. Hand, 2018. "Statistical challenges of administrative and transaction data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 555-605, June.
    12. Bruce Meyer & Nikolas Mittag, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Working Papers 2017-075, Human Capital and Economic Opportunity Working Group.
    13. Alexandru Cernat & Daniel L. Oberski, 2022. "Estimating stochastic survey response errors using the multitrait‐multierror model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 134-155, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:offsta:v:34:y:2018:i:4:p:863-888:n:4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.