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Weighting is one of the major components in survey sampling. For a given sample survey,
to each unit of the selected sample is attached a weight that is used to obtain estimates of
population parameters of interest (e.g., means or totals). The weighting process usually
involves three steps: (i) obtain the design weights, which account for sample selection; (ii)
adjust these weights to compensate for nonresponse; (iii) adjust the weights so that the
estimates coincide to some known totals of the population, which is called calibration.
Unfortunately, weighting is often considered as a process restricted to survey sampling and
for the production of statistics related to finite populations. This should not be the case
because, when using survey data, statistical analyses, modeling and index estimation
should use weights in their calculation.
This paper tries to describe why weights are useful when dealing with survey data. First,
some context is given about weighting in sample surveys. Second, we present the use of
weights in statistical analysis, and we give the impact of not using the weights through an
illustrative example. Third, the above three weighting steps are formally described.