  Permutation tests are a distribution free way of performing hypothesis tests.
These tests rely on the condition that the observed data are exchangeable among
the groups being tested under the null hypothesis. This assumption is easily
satisfied for data obtained from a simple random sample or a controlled study
after simple adjustments to the data, but there is no general method for
adjusting survey data collected using a complex sample design to allow for
permutation tests. In this article, we propose a general method for performing
a pseudo- permutation test that accounts for the complex sample design. The
proposed method is not a true permutation test in that the new values do not
come from the set of observed values in general, but of an expanded set of
values satisfying a random-effects model on the clustered residuals. Tests
using a simulated population comparing the performance of the proposed method
to permutation tests that ignore the sample design demonstrate that it is
necessary to account for certain design features in order to obtain reasonable
p-value estimates.
