  Often in surveys, key items are subject to measurement errors. Given just the
data, it can be difficult to determine the distribution of this error process,
and hence to obtain accurate inferences that involve the error-prone variables.
In some settings, however, analysts have access to a data source on different
individuals with high quality measurements of the error-prone survey items. We
present a data fusion framework for leveraging this information to improve
inferences in the error-prone survey. The basic idea is to posit models about
the rates at which individuals make errors, coupled with models for the values
reported when errors are made. This can avoid the unrealistic assumption of
conditional independence typically used in data fusion. We apply the approach
on the reported values of educational attainments in the American Community
Survey, using the National Survey of College Graduates as the high quality data
source. In doing so, we account for the informative sampling design used to
select the National Survey of College Graduates. We also present a process for
assessing the sensitivity of various analyses to different choices for the
measurement error models. Supplemental material is available online.
