The nonresponse affecting most sample surveys today continues to pose methodological challenges because of the bias caused in estimates of population parameters. Nonresponse adjustment weighting is commonly used in the estimation. The broad possibilities that this technique offers, especially for calibration weighting, are explored in this talk.

We need to distinguish different levels of availability of variable values: The population level, the sample level, the response level. The sample is drawn by probability sampling from the population. The response is the subset of the sample for which the study variables values (the y-variable values) are individually observed.

Auxiliary variables are essential. To qualify as auxiliary, a variable must contain information at a higher level than the response, and its value must be known individually for all units in the response. The use of auxiliary variables contributes to two important objectives in estimation: A reduction of variance and a reduction of nonresponse bias.

As an introduction, the talk will start by introducing calibration in a survey sampling context where we do not have the nonresponse issue to deal with.