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Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models

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  • D. L. Oberski
  • A. Kirchner
  • S. Eckman
  • F. Kreuter
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. Supplementary materials for this article are available online.

Suggested Citation

  • D. L. Oberski & A. Kirchner & S. Eckman & F. Kreuter, 2017. "Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1477-1489, October.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:520:p:1477-1489
    DOI: 10.1080/01621459.2017.1302338
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    Citations

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    Cited by:

    1. van Delden Arnout & van der Laan Jan & Prins Annemarie, 2018. "Detecting Reporting Errors in Data from Decentralised Autonomous Administrations with an Application to Hospital Data," Journal of Official Statistics, Sciendo, vol. 34(4), pages 863-888, December.
    2. Oriol J. Bosch & Melanie Revilla, 2022. "When survey science met web tracking: Presenting an error framework for metered data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 408-436, December.
    3. Gessendorfer Jonathan & Beste Jonas & Drechsler Jörg & Sakshaug Joseph W., 2018. "Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?," Journal of Official Statistics, Sciendo, vol. 34(4), pages 909-933, December.
    4. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," IZA Discussion Papers 10943, Institute of Labor Economics (IZA).
    5. Pina-Sánchez, Jose & Buil-Gil, David & brunton-smith, ian & Cernat, Alexandru, 2021. "The impact of measurement error in models using police recorded crime rates," SocArXiv ydf4b, Center for Open Science.
    6. Bosch Jover, Oriol & Revilla, Melanie, 2022. "When survey science met web tracking: presenting an error framework for metered data," LSE Research Online Documents on Economics 116431, London School of Economics and Political Science, LSE Library.
    7. David J. Hand, 2018. "Statistical challenges of administrative and transaction data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 555-605, June.
    8. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    9. Bruce Meyer & Nikolas Mittag, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Working Papers 2017-075, Human Capital and Economic Opportunity Working Group.
    10. Heiko Stüber & Markus M. Grabka & Daniel D. Schnitzlein, 2023. "A tale of two data sets: comparing German administrative and survey data using wage inequality as an example," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-18, December.
    11. Alexandru Cernat & Daniel L. Oberski, 2022. "Estimating stochastic survey response errors using the multitrait‐multierror model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 134-155, January.
    12. Luis Ayala & Ana Pérez & Mercedes Prieto-Alaiz, 2022. "The impact of different data sources on the level and structure of income inequality," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(3), pages 583-611, September.
    13. Ogbonnaya, Ijeoma Nwabuzor & Keeney, Annie J., 2018. "A systematic review of the effectiveness of interagency and cross-system collaborations in the United States to improve child welfare outcomes," Children and Youth Services Review, Elsevier, vol. 94(C), pages 225-245.
    14. Bruce D. Meyer & Nikolas Mittag, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," NBER Working Papers 25738, National Bureau of Economic Research, Inc.

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