Several researchers have begun this effort already. The post-survey adjustment methods applied to non-probability sampling have largely mirrored efforts in probability samples. Although this may be appropriate and effective to some extent, further consideration of selection bias mechanisms may be needed. We believe an agenda for advancing a method must include these attributes.

Missing data occur in survey research because an element in the target population is not included on the survey's sampling frame (noncoverage), because a sampled element does not participate in the survey (total nonresponse) and because a responding sampled element fails to provide acceptable responses to one or more of the survey items (item nonresponse). A variety of methods have been developed to attempt to compensate for missing survey data in a general purpose way that enables the survey's data file to be analysed without regard for the missing data. Weighting adjustments are often used to compensate for noncoverage and total nonresponse. Imputation methods that assign values for missing responses are used to compensate for item nonresponses. This paper describes the various weighting and imputation methods that have been developed, and discusses their benefits and limitations.

This review of nonresponse in cross-sectional household surveys in the United States shows trends in nonresponse rates, the main reasons for nonresponse, and changes in the components of nonresponse. It shows that nonresponse is increasing but that existing methods for modeling response mechanisms do not adequately explain these changes.

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