Using Geocoded Census Data for Nonresponse Bias Correction: An Assessment

Abstract

Response rates in household surveys have been declining, which increases the risks of bias in the survey results. Nonresponse weight adjustments can reduce these risks to some extent; however, for many studies—particularly in random digit dial (RDD) telephone surveys—data needed to perform the adjustments are scant. A source of data that is being used with increasing frequency by statisticians is census geocoded (CG) data where block group- or tract-level census data are appended to each sample case for use in the nonresponse adjustment process. Though this method may have some merit, its effectiveness for reducing bias remains unknown. This article reports results of an evaluation of its effectiveness for general household surveys and investigates the conditions that favor its use in the nonresponse adjustment process. Using a unique data set from a national household survey augmented by CG data, we empirically evaluate: (1) the effectiveness of CG data for removing bias in nonresponse adjustments, (2) the effect of using aggregate data and effect of using matching by telephone number on the ability to adjust for nonresponse bias, (3) any potentially adverse effects on the variances of survey estimates and trade-off with bias reduction, and (4) the conditions that enhance or limit its effectiveness. We conclude that aggregate census data have very limited utility in nonresponse adjustment unless (a) the census characteristics used in the adjustment are highly geographically clustered and (b) outcome variables exhibit good correlation with these census characteristics. We provide specific recommendations regarding (a) and (b).