<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Record linkage has become an important tool for increasing research opportunities in the social sciences. Surveys that perform record linkage to administrative records are often required to obtain informed consent from respondents prior to linkage. A major concern is that nonconsent could introduce biases in analyses based on the linked data. One straightforward strategy to overcome the missing data problem created by nonconsent is to match nonconsenters with statistically similar units in the target administrative database. To assess the effectiveness of statistical matching in this context, we use data from two German panel surveys that have been linked to an administrative database of the German Federal Employment Agency. We evaluate the statistical matching procedure under various artificial nonconsent scenarios and show that the method can be effective in reducing nonconsent biases in marginal distributions, but that biases in multivariate estimates can sometimes be worsened. We discuss the implications of these findings for survey practice and elaborate on some of the practical challenges of implementing the statistical matching procedure in the context of linkage nonconsent. The developed simulation design can act as a roadmap for other statistical agencies considering the proposed approach for their data.</p></abstract>

Record linkage has become an important tool for increasing research opportunities in the social sciences. Surveys that perform record linkage to administrative records are often required to obtain informed consent from respondents prior to linkage. A major concern is that nonconsent could introduce biases in analyses based on the linked data. One straightforward strategy to overcome the missing data problem created by nonconsent is to match nonconsenters with statistically similar units in the target administrative database. To assess the effectiveness of statistical matching in this context, we use data from two German panel surveys that have been linked to an administrative database of the German Federal Employment Agency. We evaluate the statistical matching procedure under various artificial nonconsent scenarios and show that the method can be effective in reducing nonconsent biases in marginal distributions, but that biases in multivariate estimates can sometimes be worsened. We discuss the implications of these findings for survey practice and elaborate on some of the practical challenges of implementing the statistical matching procedure in the context of linkage nonconsent. The developed simulation design can act as a roadmap for other statistical agencies considering the proposed approach for their data.

Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?

Journal of Official Statistics

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

Record linkage has become an important tool for increasing research opportunities in the social sciences. Surveys that perform record linkage to administrative...

Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?

Pubblicato online: 14 dic 2018

Pagine: 909 - 933

Ricevuto: 01 giu 2017

Accettato: 01 giu 2018

DOI: https://doi.org/10.2478/jos-2018-0045

Parole chiave
Data fusion, survey data, administrative data, linkage nonconsent

© 2018 Jonathan Gessendorfer et al., published by Sciendo

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

Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?

Pubblicato online: 14 dic 2018

Pagine: 909 - 933

Ricevuto: 01 giu 2017

Accettato: 01 giu 2018

DOI: https://doi.org/10.2478/jos-2018-0045

Parole chiaveData fusion, survey data, administrative data, linkage nonconsent

© 2018 Jonathan Gessendorfer et al., published by Sciendo

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

Parole chiave
Data fusion, survey data, administrative data, linkage nonconsent