IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v34y2018i2p463-481n9.html
   My bibliography  Save this article

Assessing Nonresponse in a Longitudinal Establishment Survey Using Regression Trees

Author

Listed:
  • Earp Morgan

    (Bureau of Labor Statistics, 2 Massachusetts Avenue NE, Washington DC, 20212-0001, U.S.A.)

  • Toth Daniell

    (Bureau of Labor Statistics, 2 Massachusetts Avenue NE, Washington DC, 20212-0001, U.S.A.)

  • Phipps Polly

    (Bureau of Labor Statistics, 2 Massachusetts Avenue NE, Washington DC, 20212-0001, U.S.A.)

  • Oslund Charlotte

    (Bureau of Labor Statistics, 2 Massachusetts Avenue NE, Washington DC, 20212-0001, U.S.A.)

Abstract

This article introduces and discusses a method for conducting an analysis of nonresponse for a longitudinal establishment survey using regression trees. The methodology consists of three parts: analysis during the frame refinement and enrollment phases, common in longitudinal surveys; analysis of the effect of time on response rates during data collection; and analysis of the potential for nonresponse bias. For all three analyses, regression tree models are used to identify establishment characteristics and subgroups of establishments that represent vulnerabilities during the data collection process. This information could be used to direct additional resources to collecting data from identified establishments in order to improve the response rate.

Suggested Citation

  • Earp Morgan & Toth Daniell & Phipps Polly & Oslund Charlotte, 2018. "Assessing Nonresponse in a Longitudinal Establishment Survey Using Regression Trees," Journal of Official Statistics, Sciendo, vol. 34(2), pages 463-481, June.
    • Handle: RePEc:vrs:offsta:v:34:y:2018:i:2:p:463-481:n:9
    DOI: 10.2478/jos-2018-0021
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/jos-2018-0021
    Download Restriction: no
    File URL: https://libkey.io/10.2478/jos-2018-0021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. F. Kreuter & K. Olson & J. Wagner & T. Yan & T. M. Ezzati‐Rice & C. Casas‐Cordero & M. Lemay & A. Peytchev & R. M. Groves & T. E. Raghunathan, 2010. "Using proxy measures and other correlates of survey outcomes to adjust for non‐response: examples from multiple surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 389-407, April.
    2. repec:mpr:mprres:4937 is not listed on IDEAS
    3. Christian Seiler, 2010. "Dynamic Modelling of Nonresponse in Business Surveys," ifo Working Paper Series 93, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    4. Schouten, Barry & Shlomo, Natalie & Skinner, Chris J., 2011. "Indicators for monitoring and improving representativeness of response," LSE Research Online Documents on Economics 39121, London School of Economics and Political Science, LSE Library.
    5. repec:mpr:mprres:4780 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Plewis Ian & Shlomo Natalie, 2017. "Using Response Propensity Models to Improve the Quality of Response Data in Longitudinal Studies," Journal of Official Statistics, Sciendo, vol. 33(3), pages 753-779, September.
    2. Brick J. Michael, 2013. "Unit Nonresponse and Weighting Adjustments: A Critical Review," Journal of Official Statistics, Sciendo, vol. 29(3), pages 329-353, June.
    3. David Cutler & Kaushik Ghosh & Irina Bondarenko & Kassandra Messer & Trivellore Raghunathan & Susan Stewart & Allison B. Rosen, 2018. "Attributing Medical Spending to Conditions: A Comparison of Methods," NBER Working Papers 25233, National Bureau of Economic Research, Inc.
    4. Alireza Rezaee & Mojtaba Ganjali & Ehsan Bahrami Samani, 2022. "Sample selection bias with multiple dependent selection rules: an application to survey data analysis with multilevel nonresponse," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-15, December.
    5. Roberts Caroline & Vandenplas Caroline & Herzing Jessica M.E., 2020. "A Validation of R-Indicators as a Measure of the Risk of Bias using Data from a Nonresponse Follow-Up Survey," Journal of Official Statistics, Sciendo, vol. 36(3), pages 675-701, September.
    6. Patrick Gleiser & Joseph W. Sakshaug & Marieke Volkert & Peter Ellguth & Susanne Kohaut & Iris Möller, 2022. "Introducing Web in a mixed‐mode establishment survey: Effects on nonresponse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 891-915, July.
    7. Durrant Gabriele B. & Maslovskaya Olga & Smith Peter W. F., 2017. "Using Prior Wave Information and Paradata: Can They Help to Predict Response Outcomes and Call Sequence Length in a Longitudinal Study?," Journal of Official Statistics, Sciendo, vol. 33(3), pages 801-833, September.
    8. Stephanie Coffey, PhD. & Jaya Damineni & John Eltinge, PhD. & Anup Mathur, PhD. & Kayla Varela & Allison Zotti, 2023. "Some Open Questions on Multiple-Source Extensions of Adaptive-Survey Design Concepts and Methods," Working Papers 23-03, Center for Economic Studies, U.S. Census Bureau.
    9. Seiler, Christian & Heumann, Christian, 2013. "Microdata imputations and macrodata implications: Evidence from the Ifo Business Survey," Economic Modelling, Elsevier, vol. 35(C), pages 722-733.
    10. Felderer Barbara & Kirchner Antje & Kreuter Frauke, 2019. "The Effect of Survey Mode on Data Quality: Disentangling Nonresponse and Measurement Error Bias," Journal of Official Statistics, Sciendo, vol. 35(1), pages 93-115, March.
    11. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    12. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    13. Lundquist Peter & Särndal Carl-Erik, 2013. "Aspects of Responsive Design with Applications to the Swedish Living Conditions Survey," Journal of Official Statistics, Sciendo, vol. 29(4), pages 557-582, December.
    14. Aneta Chmielewska & Małgorzata Renigier-Biłozor & Artur Janowski, 2022. "Representative Residential Property Model—Soft Computing Solution," IJERPH, MDPI, vol. 19(22), pages 1-24, November.
    15. Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
    16. Steele, Fiona & Durrant, Gabriele B., 2011. "Alternative approaches to multilevel modelling of survey non-contact and refusal," LSE Research Online Documents on Economics 50113, London School of Economics and Political Science, LSE Library.
    17. Torres van Grinsven Vanessa & Bolko Irena & Bavdaž Mojca, 2014. "In Search of Motivation for the Business Survey Response Task," Journal of Official Statistics, Sciendo, vol. 30(4), pages 579-606, December.
    18. Schnepf, Sylke V. & Durrant, Gabriele B. & Micklewright, John, 2014. "Which Schools and Pupils Respond to Educational Achievement Surveys? A Focus on the English PISA Sample," IZA Discussion Papers 8411, Institute of Labor Economics (IZA).
    19. Särndal Carl-Erik & Traat Imbi & Lumiste Kaur, 2018. "Interaction Between Data Collection And Estimation Phases In Surveys With Nonresponse," Statistics in Transition New Series, Statistics Poland, vol. 19(2), pages 183-200, June.
    20. James R. Hines & Niklas Potrafke & Marina Riem & Christoph Schinke, 2019. "Inter vivos transfers of ownership in family firms," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(2), pages 225-256, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:offsta:v:34:y:2018:i:2:p:463-481:n:9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.