Generating randomized response by inverse mechanism

Abstract

Following the pioneering work of Singh and Grewal [19] we examine how their inverse approach of revising Warner's [22] and Kuk's [8] techniques of directly eliciting a randomized response to unbiasedly estimate a finite population proportion bearing a stigmatizing feature, fares as a viable competitor. We consider sampling by general schemes admitting positive inclusion probabilities for single and paired persons facilitating estimation. Our live-data based numerical presentations suggest Singh and Grewal's [19] approach as quite promising.