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Does Weighting for Nonresponse Increase the Variance of Survey Means?

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  • Roderick J.A. Little
  • Sonya Vartivarian

Abstract

Nonresponse weighting is a common method for handling unit nonresponse in surveys and is aimed at reducing nonresponse bias. Because the method can be accompanied by an increase in variance, the efficacy of weighting adjustments is often seen as a bias-variance trade-off. This view is an oversimplification, because weighting can reduce variance as well as bias. The authors provide a detailed analysis of bias and variance in setting weights to estimate a survey mean based on adjustment cells and suggest that the most important feature of variables for inclusion is that they are predictive of survey outcomes. Prediction of the propensity to respond is a secondary, though useful, goal. The authors also evaluate empirical estimates of root mean squared error for assessing when weighting is effective.

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Bibliographic Info

Paper provided by Mathematica Policy Research in its series Mathematica Policy Research Reports with number 4937.

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Date of creation: 30 Dec 2005
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Handle: RePEc:mpr:mprres:4937

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Postal: Mathematica Policy Research P.O. Box 2393 Princeton, NJ 08543-2393 Attn: Communications
Fax: (609) 799-0005
Web page: http://www.mathematica-mpr.com/
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Keywords: analysis of variance; estimation methods; models; nonresponse rate;

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Cited by:
  1. 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.
  2. 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.
  3. Heinemann, Friedrich & Janeba, Eckhard & Moessinger, Marc-Daniel & Schröder, Christoph, 2013. "Revenue autonomy preference in German state parliaments," ZEW Discussion Papers 13-090, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  4. Gastón Chaumont & Miguel Fuentes & Felipe Labbé & Alberto Naudon, 2011. "A Reassessment of Flexible Price Evidence Using Scanner Data: Evidence from an Emerging Economy," Working Papers Central Bank of Chile 641, Central Bank of Chile.
  5. Blom, Annelies G., 2009. "Nonresponse bias adjustments: what can process data contribute?," ISER Working Paper Series 2009-21, Institute for Social and Economic Research.

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