- Speaker: Dr. Philip S. Kott, RTI International
- Organizer/Chair: Dan Liao, WSS Methodology Program Chair/RTI International
- Location: Bureau of Labor Statistics Conference Center; 2 Massachusetts Avenue, NE, Washington, DC. (Metrorail Red Line to Union Station. Enter BLS from First Street, NE).
- Sponsor: WSS Methodology Program
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Abstract: When a random sample drawn from a complete list frame suffers from unit nonresponse, calibration weighting to population totals can be used to remove nonresponse bias under either an assumed response (selection) or an assumed prediction (outcome) model. Calibration weighting in this way can not only provide double protection against nonresponse bias, it can also decrease variance. By employing a simple trick one can estimate the variance under the assumed prediction model and the mean squared error under the combination of an assumed response model and the probability-sampling mechanism simultaneously.
Unfortunately, there is a practical limitation on what response model can be assumed when calibrating in a single step. In particular, the choice for the response function cannot always be logistic. That limitation does not hinder calibration weighting when performed in two steps: one to remove the response bias and one to decrease variance. There are potential efficiency advantages from using the two-step approach as well even when the calibration variables employed in both steps are the same or a subset of the calibration variables in the single step. Simultaneous mean-squared-error estimation using linearization is possible, but more complicated than when calibrating in a single step.
An empirical example demonstrates, 1, that double protection works unless both models fail badly, and, 2, that calibration weighting in two steps can be more efficient that in one, although may not be worth the effort.