Estimating a finite population mean of a sensitive quantitative variable from a single probability sample by the Item Count Technique

Abstract

Item Count Technique was added as a procedure of estimating a finite population proportion of any sensitive characteristic so as to increase the level of protection of privacy of the respondents as compared to the Randomized Response Techniques (RRT's) by Warner [14] and the follow-ups. Raghavarao and Federer [13], Miller [11], Miller, Cisin and Harrel [12] introduced the Item Count Technique also known as List Experiments or the Block Total Response or the Unmatched Count Technique. Chaudhuri and Christofides [5] have extended the Item Count Technique for estimating finite population proportion of any sensitive attribute as well as finite mean or total of any sensitive quantitative variable from a sample drawn using a general sampling design whereas the original method was needlessly restricted to simple random sampling. The Chaudhuri and Christofides [5] version of Item Count Technique for estimating a finite population mean or total of any sensitive quantitative characteristic has an advantage over those by Eichhorn and Hayre [7], Gupta, Gupta and Singh [8] and Huang [10] in the sense that the population means of the innocuous quantitative characteristics used need not be known. A drawback of the method by Chaudhuri and Christofides [5] is that it requires the selection of two independent samples costing more time and money. Hence a revised method is presented in this paper.