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Using proxy measures and other correlates of survey outcomes to adjust for non-response: examples from multiple surveys

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Author Info

  • 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

Abstract

Non-response weighting is a commonly used method to adjust for bias due to unit non-response in surveys. Theory and simulations show that, to reduce bias effectively without increasing variance, a covariate that is used for non-response weighting adjustment needs to be highly associated with both the response indicator and the survey outcome variable. In practice, these requirements pose a challenge that is often overlooked, because those covariates are often not observed or may not exist. Surveys have recently begun to collect supplementary data, such as interviewer observations and other proxy measures of key survey outcome variables. To the extent that these auxiliary variables are highly correlated with the actual outcomes, these variables are promising candidates for non-response adjustment. In the present study, we examine traditional covariates and new auxiliary variables for the National Survey of Family Growth, the Medical Expenditure Panel Survey, the American National Election Survey, the European Social Surveys and the University of Michigan Transportation Research Institute survey. We provide empirical estimates of the association between proxy measures and response to the survey request as well as the actual survey outcome variables. We also compare unweighted and weighted estimates under various non-response models. Our results from multiple surveys with multiple recruitment protocols from multiple organizations on multiple topics show the difficulty of finding suitable covariates for non-response adjustment and the need to improve the quality of auxiliary data. Copyright (c) 2009 Royal Statistical Society.

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Bibliographic Info

Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).

Volume (Year): 173 (2010)
Issue (Month): 2 ()
Pages: 389-407

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Handle: RePEc:bla:jorssa:v:173:y:2010:i:2:p:389-407

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Cited by:
  1. Fiona Steele & Gabriele B. Durrant, 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.
  2. Jörg-Peter Schräpler & Jürgen Schupp & Gert G. Wagner, 2010. "Individual and Neighborhood Determinants of Survey Nonresponse: An Analysis Based on a New Subsample of the German Socio-Economic Panel (SOEP), Microgeographic Characteristics and Survey-Based Intervi," SOEPpapers on Multidisciplinary Panel Data Research 288, DIW Berlin, The German Socio-Economic Panel (SOEP).
  3. Natalie Shlomo & Chris J. Skinner & Barry Schouten, 2012. "Estimation of an indicator of the representativeness of survey response," LSE Research Online Documents on Economics 39124, London School of Economics and Political Science, LSE Library.

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