IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v40y2011i2p311-332.html
   My bibliography  Save this article

Multiple Auxiliary Variables in Nonresponse Adjustment

Author

Listed:
  • Frauke Kreuter

    (University of Maryland, College Park, USA and Institute for Employment Research, Nuremberg, Germany, fkreuter@survey.umd.edu)

  • Kristen Olson

    (University of Nebraska-Lincoln, USA)

Abstract

Prior work has shown that effective survey nonresponse adjustment variables should be highly correlated with both the propensity to respond to a survey and the survey variables of interest. In practice, propensity models are often used for nonresponse adjustment with multiple auxiliary variables as predictors. These auxiliary variables may be positively or negatively associated with survey participation, they may be correlated with each other, and can have positive or negative relationships with the survey variables. Yet the consequences for nonresponse adjustment of these conditions are not known to survey practitioners. Simulations are used here to examine the effects of multiple auxiliary variables with opposite relationships with survey participation and the survey variables. The results show that bias and mean square error of adjusted respondent means are substantially different when the predictors have relationships of the same directions compared to when they have opposite directions with either propensity or the survey variables. Implications for nonresponse adjustment and responsive designs will be discussed.

Suggested Citation

  • Frauke Kreuter & Kristen Olson, 2011. "Multiple Auxiliary Variables in Nonresponse Adjustment," Sociological Methods & Research, , vol. 40(2), pages 311-332, May.
    • Handle: RePEc:sae:somere:v:40:y:2011:i:2:p:311-332
    DOI: 10.1177/0049124111400042
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124111400042
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124111400042?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. Katharine G. Abraham & Aaron Maitland & Suzanne M. Bianchi, 2006. "Non-response in the American Time Use Survey: Who Is Missing from the Data and How Much Does It Matter?," NBER Technical Working Papers 0328, National Bureau of Economic Research, Inc.
    2. repec:mpr:mprres:4937 is not listed on IDEAS
    3. repec:mpr:mprres:4780 is not listed on IDEAS
    4. 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.
    5. Robert M. Groves & Steven G. Heeringa, 2006. "Responsive design for household surveys: tools for actively controlling survey errors and costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 439-457, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Raphael Nishimura & James Wagner & Michael Elliott, 2016. "Alternative Indicators for the Risk of Non-response Bias: A Simulation Study," International Statistical Review, International Statistical Institute, vol. 84(1), pages 43-62, April.

    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. Kristen Olson, 2013. "Paradata for Nonresponse Adjustment," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 142-170, January.
    2. 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.
    3. 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.
    4. Tobias Gummer, 2019. "Assessing Trends and Decomposing Change in Nonresponse Bias: The Case of Bias in Cohort Distributions," Sociological Methods & Research, , vol. 48(1), pages 92-115, February.
    5. Andy Peytchev, 2013. "Consequences of Survey Nonresponse," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 88-111, January.
    6. Frauke Kreuter, 2013. "Facing the Nonresponse Challenge," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 23-35, January.
    7. Brady T. West & Dan Li, 2019. "Sources of Variance in the Accuracy of Interviewer Observations," Sociological Methods & Research, , vol. 48(3), pages 485-533, August.
    8. Brick J. Michael, 2013. "Unit Nonresponse and Weighting Adjustments: A Critical Review," Journal of Official Statistics, Sciendo, vol. 29(3), pages 329-353, June.
    9. Eltinge John L. & Biemer Paul P. & Holmberg Anders, 2013. "A Potential Framework for Integration of Architecture and Methodology to Improve Statistical Production Systems," Journal of Official Statistics, Sciendo, vol. 29(1), pages 125-145, March.
    10. Ashmead Robert & Slud Eric & Hughes Todd, 2017. "Adaptive Intervention Methodology for Reduction of Respondent Contact Burden in the American Community Survey," Journal of Official Statistics, Sciendo, vol. 33(4), pages 901-919, December.
    11. 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.
    12. Michael Osei Mireku & Alina Rodriguez, 2021. "Sleep Duration and Waking Activities in Relation to the National Sleep Foundation’s Recommendations: An Analysis of US Population Sleep Patterns from 2015 to 2017," IJERPH, MDPI, vol. 18(11), pages 1-15, June.
    13. Reza C. Daniels, 2012. "A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys," SALDRU Working Papers 90, Southern Africa Labour and Development Research Unit, University of Cape Town.
    14. Reist, Benjamin M. & Rodhouse, Joseph B. & Ball, Shane T. & Young, Linda J., 2019. "Subsampling of Nonrespondents in the 2017 Census of Agriculture," NASS Research Reports 322826, United States Department of Agriculture, National Agricultural Statistics Service.
    15. Jiayun Jin & Caroline Vandenplas & Geert Loosveldt, 2019. "The Evaluation of Statistical Process Control Methods to Monitor Interview Duration During Survey Data Collection," SAGE Open, , vol. 9(2), pages 21582440198, June.
    16. Roger Tourangeau & J. Michael Brick & Sharon Lohr & Jane Li, 2017. "Adaptive and responsive survey designs: a review and assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 203-223, January.
    17. repec:iab:iabfda:201307(en is not listed on IDEAS
    18. 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.
    19. Böhme, Marcus & Stöhr, Tobias, 2012. "Guidelines for the use of household interview duration analysis in CAPI survey management," Kiel Working Papers 1779, Kiel Institute for the World Economy (IfW Kiel).
    20. Jens Bonke & Mette Deding & Mette Lausten & Leslie S. Stratton, 2008. "Intra‐Household Specialization in Housework in the United States and Denmark," Social Science Quarterly, Southwestern Social Science Association, vol. 89(4), pages 1023-1043, December.
    21. Arenas-Arroyo, Esther & Schmidpeter, Bernhard, 2022. "Spillover effects of immigration policies on children's human capital," Ruhr Economic Papers 974, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

    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:sae:somere:v:40:y:2011:i:2:p:311-332. 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: SAGE Publications (email available below). General contact details of provider: .

    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.