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https://doi.org/10.18148/srm/2012.v6i2.5094
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Assessing the magnitude of non-consent biases in linked survey and administrative data
Die Beurteilung der Größe von nicht übereinstimmenden Tendenzen in verbundenen Erhebungs- und Verwaltungsdaten
[journal article]
Abstract "Administrative records are increasingly being linked to survey records to highten the utility of the survey data. Respondent consent is usually needed to perform exact record linkage; however, not all respondents agree to this request and several studies have found significant differences between c... view more
"Administrative records are increasingly being linked to survey records to highten the utility of the survey data. Respondent consent is usually needed to perform exact record linkage; however, not all respondents agree to this request and several studies have found significant differences between consenting and non-consenting respondents on the survey variables. To the extent that these survey variables are related to variables in the administrative data, the resulting administrative estimates can be biased due to non-consent. Estimating non-consent biases for linked administrative estimates is complicated by the fact that administrative records are typically not available for the non-consenting respondents. The present study can overcome this limitation by utilizing a unique data source, the German Panel Study 'Labor Market and Social Security' (PASS), and linking the consent indicator to the administrative records (available for the entire sample). This situation permits the estimation of non-consent biases for administrative variables and avoids the need to link the survey responses. The impact of non-consent bias can be assessed relative to other sources of bias (nonresponse, measurement) for several administrative estimates. The results show that non-consent biases are present for few estimates, but are generally small relative to other sources of bias." (author's abstract)... view less
Keywords
data; data documentation; data acquisition; data capture; data organization; data quality; data processing; data collection method; quality; Federal Republic of Germany; panel
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Document language
English
Publication Year
2012
Page/Pages
p. 113-122
Journal
Survey Research Methods, 6 (2012) 2
ISSN
1864-3361
Status
Published Version; peer reviewed
Licence
Deposit Licence - No Redistribution, No Modifications