Using Prediction-Oriented Software for Survey Estimation - Part III:
Full-Scale Study of Variance and Bias
by James R. Knaub, Jr
Applications of this method include small area estimation and imputation, accompanied by
estimates of standard errors. This method can be used for imputation with any kind of sample or
census survey. It has been tested and developed, and now enters full-scale testing and
implementation. Advantages include ease in revising models, flexible organization, storage and
usage of data, and the ability to maximize the effectiveness of collected data. For purposes of
estimation, collected data may be grouped such that each data set contains as many members as may
be well defined under a single model per group. That is, each group should be as large as it can be
and remain basically homogeneous. After the models are exercised, there will either be an observed
response or an 'imputed' value for each member of the population, for each data element, which can
be rearranged and published, with estimated standard errors, for any subtotals desired. Under
full-scale testing, more results have become available for a better study of variance estimation, and
bias is also studied with instructive results. Other areas illustrated are the degree to which use of this
technique may be appropriate under an extreme condition, and the application of this method across
Small Area Estimation, Imputation, Standard Error, Bias, Inference, Model-Based
James R. Knaub, Jr.
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