Calibration weighting is an easy-to-implement yet powerful tool for reducing the standard errors of many population estimates derived from a sample survey by forcing the weighted sums of certain “calibration” variables to equal their known (or better-estimated) population totals. Although originally developed to reduce standard errors, calibration weighting can also be used to reduce or remove selection biases resulting from unit nonresponse. To this end, nonrespondents are usually assumed to be “missing at random,” that is, the response mechanism is assumed to be a function of calibration variables with either known values in the entire sample or known population totals. It is possible, however, to use calibration-weighting to compensate for unit nonresponse when response is a function of model variables that need not be calibration variables; in fact, some model variables can have values known only for respondents. We will explore some recent findings connected with this methodology.
Calibration Weighting When Model and Calibration Variables Can Differ