Level-of-Effort Paradata and Nonresponse Adjustment Models for a National Face-to-Face Survey

Survey samples are designed to produce unbiased estimates. Unfortunately, nonresponse may lead to bias if the responders and nonresponders are different with respect to the survey variables. One common approach to addressing nonresponse after data collection has been completed is to differentially weight responding cases such that the respondents match the full sample on the selected characteristics. The selection of the characteristics is a modeling step that assumes that conditional upon the selected characteristics, responders and nonresponders are equivalent. This method is known as nonresponse weighting. The method relies upon having data available for the entire sample that predicts both response and the survey variables themselves. These data can come from either the sampling frame or from paradata (Couper, 1998; Couper and Lyberg, 2005), that is, from process data created during data collection. If the available data are only useful for predicting response and not for predicting the survey variables, then adjustments based upon these data can only add noise to estimates. This is true even when the true probability of responding is known. In practice, the true probability is never known and estimates of it have associated sampling error and, possibly, misspecification error which may also add noise to estimates.