  We consider an empirical likelihood framework for inference for a statistical
model based on an informative sampling design. Covariate information is
incorporated both through the weights and the estimating equations. The
estimator is based on conditional weights. We show that under usual conditions,
with population size increasing unbounded, the estimates are strongly
consistent, asymptotically unbiased and normally distributed. Our framework
provides additional justification for inverse probability weighted score
estimators in terms of conditional empirical likelihood. In doing so, it
bridges the gap between design-based and model-based modes of inference in
survey sampling settings. We illustrate these ideas with an application to an
electoral survey.
