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

Arnout van Delden (), Jan van der Laan () and Prins Annemarie ()
Additional contact information
Arnout van Delden: Statistics Netherlands, Department of Process Development and Methodology, Henri Faasdreef 312, P.O. Box 24500, 2490 HA The Hague, The Netherlands.
Jan van der Laan: 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.

Journal of Official Statistics, 2018, vol. 34, issue 4, 863-888

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.When statistical output on changes is estimated from decentralised administrative data, the question may arise whether those changes are affected by shifts in reporting frequencies. For instance, in a case study on hospital data, the values from certain data suppliers may have been affected by changes in reporting frequencies. We present an automatic procedure to detect suspicious data suppliers in decentralised administrative data in which shifts in reporting behaviour are likely to have affected the estimated output. The procedure is based on a predictive mean matching approach, where part of the original data values are replaced by imputed values obtained from a selected reference group. The method is successfully applied to a case study with administrative hospital data.

Keywords: Administrative data; measurement errors; predictive mean matching; reporting errors; selective editing (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/jos-2018-0043 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:34:y:2018:i:4:p:863-888:n:4

DOI: 10.2478/jos-2018-0043

Access Statistics for this article

Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson

More articles in Journal of Official Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().