To gain insight into how characteristics of an establishment are associated
with nonresponse, a recursive partitioning algorithm is applied to the
Occupational Employment Statistics May 2006 survey data to build a regression
tree. The tree models an establishment's propensity to respond to the survey
given certain establishment characteristics. It provides mutually exclusive
cells based on the characteristics with homogeneous response propensities. This
makes it easy to identify interpretable associations between the characteristic
variables and an establishment's propensity to respond, something not easily
done using a logistic regression propensity model. We test the model obtained
using the May data against data from the November 2006 Occupational Employment
Statistics survey. Testing the model on a disjoint set of establishment data
with a very large sample size (n=179,360) offers evidence that the regression
tree model accurately describes the association between the establishment
characteristics and the response propensity for the OES survey. The accuracy of
this modeling approach is compared to that of logistic regression through
simulation. This representation is then used along with frame-level
administrative wage data linked to sample data to investigate the possibility
of nonresponse bias. We show that without proper adjustments the nonresponse
does pose a risk of bias and is possibly nonignorable.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS521 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org