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Does Weighting for Nonresponse Increase the Variance of Survey Means?

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Author Info

  • Rod Little

    (University of Michigan)

  • Sonya Vartivarian

    (University of Michigan)

Abstract

Nonresponse weighting is a common method for handling unit nonresponse in surveys. A widespread view is that the weighting method is aimed at reducing nonresponse bias, at the expense of an increase in variance. Hence, the efficacy of weighting adjustments becomes a bias-variance trade-off. This note suggests that this view is an oversimplification -- nonresponse weighting can in fact lead to a reduction in variance as well as bias. A covariate for a weighting adjustment must have two characteristics to reduce nonresponse bias - it needs to be related to the probability of response, and it needs to be related to the survey outcome. If the latter is true, then weighting can reduce, not increase, sampling variance. A detailed analysis of bias and variance is provided in the setting of weighting for an estimate of a survey mean based on adjustment cells. The analysis suggests that the most important feature of variables for inclusion in weighting adjustments is that they are predictive of survey outcomes; prediction of the propensity to respond is a secondary, though useful, goal. Empirical estimates of root mean squared error for assessing when weighting is effective are proposed and evaluated in a simulation study.

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File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1034&context=umichbiostat
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Bibliographic Info

Paper provided by Berkeley Electronic Press in its series The University of Michigan Department of Biostatistics Working Paper Series with number 1034.

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Date of creation: 11 Jul 2004
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Handle: RePEc:bep:mchbio:1034

Note: oai:bepress.com:umichbiostat-1034
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Web page: http://www.bepress.com

Related research

Keywords: missing data; nonresponse adjustment; sampling weights; survey nonresponse;

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Cited by:
  1. Gastón Chaumont & Miguel Fuentes & Felipe Labbé & Alberto Naudón, 2011. "A Reassesment of Flexible Price Evidence Using Scanner Data: Evidence from an Emerging Economy," Working Papers Central Bank of Chile 632, Central Bank of Chile.
  2. Heinemann, Friedrich & Janeba, Eckhard & Moessinger, Marc-Daniel & Schröder, Christoph, 2013. "Revenue Autonomy Preference in German State Parliaments," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79736, Verein für Socialpolitik / German Economic Association.
  3. Hindsley, Paul & Landry, Craig E. & Gentner, Brad, 2011. "Addressing onsite sampling in recreation site choice models," Journal of Environmental Economics and Management, Elsevier, vol. 62(1), pages 95-110, July.
  4. repec:ese:iserwp:2009-21 is not listed on IDEAS
  5. Streif, Frank & Heinemann, Friedrich & Janeba, Eckhard & Schröder, Christoph, 2013. "Will the German Debt Brake Succeed? Survey Evidence from State Politicians," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 80044, Verein für Socialpolitik / German Economic Association.
  6. Ivan Faiella, 2010. "The use of survey weights in regression analysis," Temi di discussione (Economic working papers) 739, Bank of Italy, Economic Research and International Relations Area.

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