Abstract

Often the reliability of survey data is examined only in relationship to sampling variances, excluding many other potential sources of error. If the sampling variance dominates the mean-square error, then few mistakes result by considering sampling variance only; however, if sampling variance is only a small part of the mean-square error, serious mistakes in inference could be made. The Bureau of the Census has developed a model describing the joint effect of sampling and nonsampling errors on census statistics. This article shows how a study of the components of error may lead to methods of improving the accuracy and reliability of survey data.

The text of this article is only available as a PDF.
You do not currently have access to this content.