An unbalanced ranked-set sampling method to get more than one sample from each set

Abstract

In ranked-set sampling, the restriction of selecting just one individual from each set may require too many sets. We propose a new version of ranked-set sampling that relaxes this restriction. Our new design uses stratified sampling in which ranked-set sampling is used to form the strata. Simulations, and a real case study on medicinal flowers, show that this design can be more precise and less costly than previous designs. © The Author 2017. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved.