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Sampling of subpopulations in two‐stage surveys
Author(s) -
Clark Robert Graham
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3723
Subject(s) - sampling frame , sampling (signal processing) , sampling design , sample (material) , statistics , population , selection (genetic algorithm) , survey sampling , cluster sampling , multistage sampling , census , sample size determination , systematic sampling , range (aeronautics) , econometrics , computer science , mathematics , demography , machine learning , engineering , sociology , chemistry , filter (signal processing) , chromatography , computer vision , aerospace engineering
Many health and other surveys aim to produce statistics on small subpopulations, such as specific ethnic groups or the indigenous population of a country. In most countries, there is no reliable sampling frame of the subpopulations of interest, hence it is necessary to sample from the general population, which can be very expensive. A range of issues and strategies for sampling rare subpopulations is reviewed. The most common approaches in practice are the use of a large screening sample, and disproportionate sampling by strata. Optimal sample designs have been derived for the case of one‐stage sampling, but most household interview surveys use two or more stages of selection. This paper develops optimal designs for two‐stage sampling, where there is auxiliary information on subpopulation numbers for each primary sampling unit. Various alternative designs are evaluated using a simulated population derived from the New Zealand Census. Copyright © 2009 John Wiley & Sons, Ltd.