Speaker: Seho Park 

Abstract:

Survey data integration using mass imputation

Survey data integration combining information from multiple sources is an important prac-
tical problem in survey sampling. Data integration can be viewed as a missing data problem
and we propose mass imputation approach for data integration. By lling in the missing
values for the study variable in one sample with imputed values incorporating information
from the other sample, we obtain an improved estimator integrating information from two
samples.
Three specic setups are considered in this presentation. The rst setup is the classical
two-phase sampling where the second-phase sample is an outcome-dependent probability
sample from the rst-phase sample. The second one is a non-nested two-phase sampling
where the second-phase sample is not necessarily a probability sample. The third one is
combining two independent samples with different measurements from the same target pop-
ulation. Since the measurements are different for the same concepts, measurement errors
can exist.
For the rst two setups, we propose a regression imputation estimator for mass imputation
where the regression coeffcients are estimated from the second-phase sample. For the third
setup, we propose a survey data integration method using measurement error models. An
application of the technique to the Food and Nutrition Technical Assistance III Project is
presented.