Using Prediction-Oriented Software for Survey Estimation
by James R. Knaub, Jr
Survey sampling and inference may be accomplished by solely design-based
procedures, or solely model-based procedures, or by model-assisted, design-based procedures.
Depending upon circumstances, there are advantages to each of these methods. There are times,
particularly in (highly skewed) establishment surveys, when, either in terms of resources, and/or
nonsampling error, it may not be practical to sample from among the 'smallest' members of the
population, and solely model-based procedures may then be advantageous. Further, imputation
for either a sample or a census is accomplished in the same manner. That is, whether data are
'missing' because of nonresponse, or because a sample is being used, a model can be used to
predict values for the 'missing' data. It is important, therefore, to apply models to relatively
homogeneous sets of data (i.e., such that the model parameters apply reasonably well). When
predictions are made available for all 'missing' data, an estimate is available for the total. This
article shows a general approach that may be used to organize such estimations for totals and their
estimated variances in a flexible manner. Readily available regression software may be used and
results may be easily reorganized to present various aggregations. This article explores what can
be done to decompose variances, and simplify the applications. Note that the concept of model
variance (Royall (1970)) as a measure of uncertainty, applies equally well to the uncertainty in a
reported total after imputation has been applied to a census, as it does to a sample.
survey sampling; estimation; imputation; variance estimation; establishment surveys
James R. Knaub, Jr.,
Jeffrey S. Simonoff
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