Using Proxy Measures and Other Correlates of Survey Outcomes to Adjust for Non-Response: Examples from Multiple Surveys

Non-response weighting is a commonly used method to adjust for bias due to unit nonresponse 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