The calibration approach in survey theory and practice - ARCHIVED

Calibration is the principal theme in many recent articles on estimation in survey sampling. Words such as "calibration approach" and "calibration estimators" are frequently used. As article authors like to point out, calibration provides a systematic way to incorporate auxiliary information in the procedure.

Calibration has established itself as an important methodological instrument in large-scale production of statistics. Several national statistical agencies have developed software designed to compute weights, usually calibrated to auxiliary information available in administrative registers and other accurate sources.

This paper presents a review of the calibration approach, with an emphasis on progress achieved in the past decade or so. The literature on calibration is growing rapidly; selected issues are discussed in this paper.

The paper starts with a definition of the calibration approach. Its important features are reviewed. The calibration approach is contrasted with (generalized) regression estimation, which is an alternative but different way to take auxiliary information into account. The computational aspects of calibration are discussed, including methods for avoiding extreme weights. In the early sections of the paper, simple applications of calibration are examined: Estimation of a population total in direct, single phase sampling. Generalization to more complex parameters and more complex sampling designs are then considered. A common feature of more complex designs (sampling in two or more phases or stages) is that the available auxiliary information may consist of several components or layers. The uses of calibration in such cases of composite information are reviewed. In later sections of the paper, some examples are given to illustrate how the results of the calibration thinking may contrast with answers given by earlier established approaches. Finally, applications of calibration in the presence of nonsampling error are discussed, in particular methods for nonresponse bias adjustment.