<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p><p>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.</p></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.

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

Statistics Netherlands, Department of Process Development and Methodology

Netherlands Institute for Health Services Research (Nivel)

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Administrative data sources are increasingly used by National Statistical Institutes to compile statistics. These sources may be based on decentralised...

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

Published Online: Dec 14, 2018

Page range: 863 - 888

Received: Jun 01, 2017

Accepted: May 01, 2018

DOI: https://doi.org/10.2478/jos-2018-0043

Keywords
Administrative data, measurement errors, predictive mean matching, reporting errors, selective editing

© 2018 Arnout van Delden et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Dec 14, 2018

Page range: 863 - 888

Received: Jun 01, 2017

Accepted: May 01, 2018

DOI: https://doi.org/10.2478/jos-2018-0043

KeywordsAdministrative data, measurement errors, predictive mean matching, reporting errors, selective editing

© 2018 Arnout van Delden et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Administrative data, measurement errors, predictive mean matching, reporting errors, selective editing