The relative impacts of design effects and multiple imputation on variance estimates
A case study with the 2008 National Ambulatory Medical Care Survey
Lewis, T. H., Goldberg, E., Schenker, N., Beresovsky, V., Schappert, S., Decker, S., Sonnenfeld, N., & Shimizu, I. (2014). The relative impacts of design effects and multiple imputation on variance estimates: A case study with the 2008 National Ambulatory Medical Care Survey. Journal of Official Statistics, 30(1), 147–161. https://doi.org/10.2478/jos-2014-0008
The National Ambulatory Medical Care Survey collects data on office-based physician care from a nationally representative, multistage sampling scheme where the ultimate unit of analysis is a patient-doctor encounter. Patient race, a commonly analyzed demographic, has been subject to a steadily increasing item nonresponse rate. In 1999, race was missing for 17 percent of cases; by 2008, that figure had risen to 33 percent. Over this entire period, single imputation has been the compensation method employed. Recent research at the National Center for Health Statistics evaluated multiply imputing race to better represent the missing-data uncertainty. Given item nonresponse rates of 30 percent or greater, we were surprised to find many estimates’ ratios of multiple-imputation to single-imputation estimated standard errors close to 1. A likely explanation is that the design effects attributable to the complex sample design largely outweigh any increase in variance attributable to missing-data uncertainty.