  Nonresponse arises frequently in surveys and follow-ups are routinely made to
increase the response rate. In order to monitor the follow-up process, callback
data have been used in social sciences and survey studies for decades. In
modern surveys, the availability of callback data is increasing because the
response rate is decreasing and follow-ups are essential to collect maximum
information. Although callback data are helpful to reduce the bias in surveys,
such data have not been widely used in statistical analysis until recently. We
propose a stableness of resistance assumption for nonresponse adjustment with
callback data. We establish the identification and the semiparametric
efficiency theory under this assumption, and propose a suite of semiparametric
estimation methods including a doubly robust one, which generalize existing
parametric approaches for callback data analysis. We apply the approach to a
Consumer Expenditure Survey dataset. The results suggest an association between
nonresponse and high housing expenditures.
