Calibration can be used to adjust for unit nonresponse when the model variables on which the response/nonresponse mechanism depends do not coincide with the benchmark variables in the calibration equation. As a result, model-variable values need only known for the respondents. This allows the treatment of what is usually considered nonignorable nonresponse. Although one can invoke either quasirandomization or prediction-model-based theory to justify the calibration, both frameworks rely on unverifiable model assumptions, and both require large samples to produce nearly unbiased estimators even when those assumptions hold. We will explore these issues theoretically and with an empirical study.


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