Weighting and Prediction in Sample Surveys

Abstract

A fundamental technique in survey sampling is to weight included units by the inverse of their probability of inclusion, which may be known (as in the case sampling weights) or estimated (as in the case of nonresponse weights or post-stratification) . The technique is closely associated with the design-based approach to survey inference, with the idea that units in the sample are representing a certain number of units in the population. I discuss weighting from a modelling perspective. Some common misconceptions of weighting will be addressed, including the idea that modelers can ignore the sampling weights, or that weighting necessarily reduces bias at the expense of increased variance, or that units entering the calculation of nonresponse weights should be weighted by their sampling weights. A robust model-based perspective suggests that selection weights cannot be ignored , but there may be better ways of incorporating them in the inference than via the standard Horvitz-Thompson estimator and its variants.