Handling Missing Data in Ranked Set Sampling The existence of missing observations is a very important aspect to be considered in the application of survey sampling for example. In human populations they may be caused by a refusal of some interviewees to give the true value for the variable of interest. Traditionally simple random sampling is used to select samples. Most statistical models are supported by the use of samples selected by means of this design. In recent decades an alternative design has started being used which in many cases shows an improvement in terms of accuracy compared with traditional sampling. It is called Ranked Set Sampling (RSS). A random selection is made with the replacement of samples which are ordered (ranked). The literature on the subject is increasing due to the potentialities of RSS for deriving more effective alternatives to well established statistical models. In this work the use of RSS sub sampling for obtaining information among the non respondents and different imputation procedures are considered. RSS models are developed as counterparts of well known simple random sampling (SRS) models. SRS and RSS models for estimating the population using missing data are presented and compared both theoretically and using numerical experiments. Preface Missing Observations and Data Quality Improvement Sampling Using Ranked Sets: Basic Concepts The Non Response Problem: Sub sampling among the Non Respondents Imputation of the Missing Data Some Numerical Studies of the Behavior of RSS.