Ranked Set Sampling for Estimating a Proportion:   How to Improve the Ranking  by Haiying Chen, Elizabeth A. Stasny, and DOUGLAS A. WOLFE Ohio State University Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgment or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. The objective of this study is to use a National Health and Nutrition Examination Survey III data set to investigate the application of RSS to estimation of a population proportion. We use logistic regression to aid in the ranking of the variable of interest.  Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a population proportion.