Evaluating the quality of survey and administrative data with generalized multitrait-multimethod models


Oberski, Daniel L. ; Kirchner, Antje ; Eckman, Stephanie ; Kreuter, Frauke


DOI: https://doi.org/10.1080/01621459.2017.1302338
URL: https://amstat.tandfonline.com/doi/abs/10.1080/016...
Additional URL: https://arxiv.org/abs/1508.05502
Document Type: Article
Year of publication: 2017
The title of a journal, publication series: Journal of the American Statistical Association : JASA
Volume: 112
Issue number: 520
Page range: 1477-1489
Place of publication: Abingdon [u.a.]
Publishing house: Taylor & Francis Group
ISSN: 0162-1459 , 0003-1291 , 1537-274X
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department A
School of Social Sciences > Statistik u. Sozialwissenschaftliche Methodenlehre (Kreuter 2014-)
Subject: 300 Social sciences, sociology, anthropology
Abstract: Administrative data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect. We introduce the “generalized multitrait-multimethod” (GMTMM) model, which can be seen as a general framework for evaluating the quality of administrative and survey data simultaneously. This framework allows both survey and administrative data to contain random and systematic measurement errors. Moreover, it accommodates common features of administrative data such as discreteness, nonlinearity, and nonnormality, improving similar existing models. The use of the GMTMM model is demonstrated by application to linked survey-administrative data from the German Federal Employment Agency on income from of employment, and a simulation study evaluates the estimates obtained and their robustness to model misspecification.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Oberski, Daniel L. ; Kirchner, Antje ; Eckman, Stephanie ; Kreuter, Frauke ORCID: 0000-0002-7339-2645 (2017) Evaluating the quality of survey and administrative data with generalized multitrait-multimethod models. Journal of the American Statistical Association : JASA Abingdon [u.a.] 112 520 1477-1489 [Article]


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