Reliability of Estimates With Alternative Cluster Sizes in the Health InterviewSurvey

The practice of grouping the elements of the universe of study into clusters and sampling the clusters is a common feature of sample designs. Dealing with clusters of elements ahnost invariably increases the sampling error of the statistics estimated from the sample. However, clustering usually reduces the per-unit costs of the sampling and data collection. As the cluster size is increased, the costs of conducting a survey are usually reduced but the sampling errors of the statistics generally increase for a given sample size. The increase in variance due to clustering may be considered as a "loss" in the reliability of a statistic. The purpose of this study is to obtain measurements of the loss in reliability due to clustering of households using various cluster sizes for data collected by the Health Interview Survey (HIS).