Multiple frame surveys in modern data integration

Thomas M. Menino Convention & Exhibition Center 

Multiple frame surveys provide effective ways to integrate multiple data sets from heterogeneous
sources. Well-motivated by traditional survey sampling, the scope of this design is unfortunately
too limited to address modern applications in data integration. The first part of the talk develops
methods for hypothesis testing often overlooked by survey sampling when data sets are obtained
from independent surveys. Because parameter of interest is not a finite population parameter,
we adopt the super population framework. This additional randomness introduces multitude
of dependence within and across multiple data sets through potential duplication and finite
population sampling so that quantifying uncertainty is much harder and challenging than in the
finite population framework. With this complication, the distributions of the inverse probability
weighted version of pivotal quantities in the i.i.d. setting becomes no longer parameter-free.
Our proposed methodology first develops asymptotic theory for multiple frame surveys with a
super population and then estimates complex parameter-dependent null distributions through
simulation and/or bootstrap. Our methods are illustrated with data analysis of the Wilms tumor
study. The second part is our attempt to apply the framework of multiple frame surveys to non-
probability samples. In the non-probability sample research, one combines a reference survey
and a non-probability sample whose missingness mechanism is unknown. We extend our
asymptotic theory to combining data from sample surveys and missing data. After reviewing
methodology in non-probability samples, we discuss limitations of this approach and potential
solutions using techniques from survey sampling such as record linkage

Multiple Frame Surveys

Data Integration