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

Frauke Kreuter (), K. Olson, J. Wagner, T. Yan, T. M. Ezzati‐Rice, C. Casas‐Cordero, M. Lemay, A. Peytchev, R. M. Groves and T. E. Raghunathan

Journal of the Royal Statistical Society Series A, 2010, vol. 173, issue 2, 389-407

Abstract: Summary. 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.

Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

Downloads: (external link)
https://doi.org/10.1111/j.1467-985X.2009.00621.x

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:173:y:2010:i:2:p:389-407

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X

Access Statistics for this article

Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples

More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().