  In data fusion analysts seek to combine information from two databases
comprised of disjoint sets of individuals, in which some variables appear in
both databases and other variables appear in only one database. Most data
fusion techniques rely on variants of conditional independence assumptions.
When inappropriate, these assumptions can result in unreliable inferences. We
propose a data fusion technique that allows analysts to easily incorporate
auxiliary information on the dependence structure of variables not observed
jointly; we refer to this auxiliary information as glue. With this technique,
we fuse two marketing surveys from the book publisher HarperCollins using glue
from the online, rapid-response polling company CivicScience. The fused data
enable estimation of associations between people's preferences for authors and
for learning about new books. The analysis also serves as a case study on the
potential for using online surveys to aid data fusion.
