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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
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    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. 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.
    3. repec:mpr:mprres:4937 is not listed on IDEAS
    4. 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.
    5. repec:mpr:mprres:4780 is not listed on IDEAS
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    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.

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