Assessing the Potential of Paradata and Other Auxiliary Data for Nonresponse Adjustments

Bibliographic Data

ID7363425
AuthorsBrian S Krueger, Brady T West (0000-0003-0160-1388)
Year2014
Volume78
Issue4
Pages795-831
Publication date2014-10-18
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenuePublic Opinion Quarterly (JOURNAL)
Journal identifiersISSN: 0033-362X • E-ISSN: 1537-5331
PublisherOxford University Press (OUP) (PUBLISHER)
DOI10.1093/poq/nfu040
OpenAlexW2143026981
LanguageEN
Citations received5
References cited21

Given the theoretical promise of these auxiliary data for overcoming the challenge of nonresponse, survey researchers have shown an increased interest in collecting paradata, data from screening interviews, and other contextual information. However, very few studies have systematically assessed the use of these data for post-survey nonresponse adjustments. Those studies that do exist generally do not identify auxiliary variables that correlate with response propensity and key survey variables, a necessary condition for auxiliary data to be effective tools for reducing nonresponse bias. Using the National Survey of Family Growth (NSFG), this paper leverages a large set of auxiliary variables available for the full NSFG sample to assess their potential as independent tools for post-survey nonresponse adjustments. We begin by using this auxiliary information to predict response propensity (RP) for each person in the full sample. We then display descriptive estimates for a variety of attitudes and behaviors measured in the NSFG, using post-stratification weighting adjustments as well as RP adjustments followed by post-stratification. The results show that accounting for RP in the weighting adjustment often produces noteworthy differences in the estimates, thus supporting the collection of these types of auxiliary variables in practice. These results also suggest that standard post-stratification adjustments may not be entirely effective at removing nonresponse bias from all survey estimates, and that some subgroup analyses may be especially subject to bias when adjusting survey estimates using post-stratification alone

Econometrics · Non-response bias · Sample (material · Statistics · Survey data collection · Weighting · Computer Science · Food Security and Health in Diverse Populations · Health disparities and outcomes · Mathematics · Medicine · Psychology · Survey Methodology and Nonresponse

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