Sample weighting is carried out to accomplish a number of objectives, including adjustments for nonresponse. The purpose of weighting the survey data is to permit analysts to produce estimates of statistics for the total target population. For example, state surveys usually involve the selection of a random sample of low-income persons from an existing administrative data file. Sampling weights produced for such surveys can be considered as estimated measures of the number of persons in the target population that the particular sampled low-income individual represents. Weighting takes into account several features of the survey: the specific probabilities of selection of individuals in the sample (as described in the following subsection), as well as nonresponse and differences between the sample of low-income persons and the total low-income population. Differences between the sample and the population may arise because of sampling variability, differential noncoverage in the survey among population subgroups, and possibly other types of response errors, such as differential response rates or misclassification errors.
In summary, sample weighting in surveys is carried out to accomplish the following objectives:
- To enable the production of tabulations that provide estimates of the number of persons (or families) in the population for the various categories tabulated;
- To compensate for disproportionate sampling of various subgroups in the sample;
- To reduce biases arising from the fact that nonrespondents may be different from those who participate;
- To compensate, to the extent possible, for noncoverage in the sample due to inadequacies in the sampling frame or other reasons for noncoverage; and
- To reduce variances in the estimation procedure by using auxiliary information that is known with a high degree of accuracy if the auxiliary variables are highly correlated with the study variables.
We start with a description of base weights because the adjustments are applied to these weights.
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