Using proxy measures and other correlates of survey outcomes to adjust for non-response: examples from multiple surveys
dc.contributor.author | Kreuter, Frauke | en_US |
dc.contributor.author | Olson, K. | en_US |
dc.contributor.author | Wagner, James R. | en_US |
dc.contributor.author | Yan, Ting | en_US |
dc.contributor.author | Ezzati-Rice, T. M. | en_US |
dc.contributor.author | Casas-Cordero, C. | en_US |
dc.contributor.author | Lemay, M. | en_US |
dc.contributor.author | Peytchev, A. | en_US |
dc.contributor.author | Groves, R. M. | en_US |
dc.contributor.author | Raghunathan, Trivellore E. | en_US |
dc.date.accessioned | 2011-01-31T17:53:42Z | |
dc.date.available | 2011-06-09T15:09:40Z | en_US |
dc.date.issued | 2010-04 | en_US |
dc.identifier.citation | Kreuter, F.; Olson, K.; Wagner, J.; Yan, T.; Ezzati-Rice, T. M.; Casas-Cordero, C.; Lemay, M.; Peytchev, A.; Groves, R. M.; Raghunathan, T. E.; (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 (Statistics in Society) 173(2): 389-407. <http://hdl.handle.net/2027.42/79323> | en_US |
dc.identifier.issn | 0964-1998 | en_US |
dc.identifier.issn | 1467-985X | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/79323 | |
dc.description.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. | en_US |
dc.format.extent | 896784 bytes | |
dc.format.extent | 3106 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Blackwell Publishing Ltd | en_US |
dc.subject.other | Interviewer Observations | en_US |
dc.subject.other | Non-response Adjustment | en_US |
dc.subject.other | Non-response Bias | en_US |
dc.subject.other | Paradata | en_US |
dc.subject.other | Response Propensity Weights | en_US |
dc.title | Using proxy measures and other correlates of survey outcomes to adjust for non-response: examples from multiple surveys | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | University of Michigan, Ann Arbor, USA | en_US |
dc.contributor.affiliationum | University of Michigan, Ann Arbor, USA | en_US |
dc.contributor.affiliationother | University of Maryland, College Park, USA | en_US |
dc.contributor.affiliationother | University of Nebraska, Lincoln, USA | en_US |
dc.contributor.affiliationother | National Opinion Research Center, Chicago, USA | en_US |
dc.contributor.affiliationother | Agency for Healthcare Research and Quality, Rockville, USA | en_US |
dc.contributor.affiliationother | University of Maryland, College Park, USA | en_US |
dc.contributor.affiliationother | RTI International, Research Triangle Park, USA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/79323/1/j.1467-985X.2009.00621.x.pdf | |
dc.identifier.doi | 10.1111/j.1467-985X.2009.00621.x | en_US |
dc.identifier.source | Journal of the Royal Statistical Society: Series A (Statistics in Society) | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.