Summary

Sample selection in business statistics can be challenging because of several reasons. The population is often skewed, new businesses are created or they go out of business, and businesses may be related to each other in different ways. The use of a stratified simple random sampling design can enable researchers to draw inferences about specific subgroups that may be lost in a more generalised random sample, but this requires the selection of relevant stratification variables. An important option here, which is commonly used for business surveys whenever element size varies greatly, is probability proportional to size (pps) sampling, often in combination with cut-off sampling. This method can improve accuracy for a given sample size by concentrating the sample on large elements that have the greatest impact on population estimates. An alternative to stratified simple random sampling is systematic sampling. Cluster or multistage sampling is motivated by the need for practical, economical and sometimes administrative efficiency. The use of fixed panels will produce very efficient estimates of periodic change. In most periodic surveys sample rotation is used in order to reduce response burden.

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