Improving Estimation Accuracy in Nonrandomized Response Questioning Methods by Multiple Answers

Abstract

When private or stigmatizing characteristics are included in sample surveys, direct questions result in low cooperation of the respondents. To increase cooperation, indirect questioning procedures have been established in the literature. Nonrandomized response methods are one group of such procedures and have attracted much attention in recent years. In this article, we consider four popular nonrandomized response schemes and present a possibility to improve the estimation precision of these schemes. The basic idea is to require multiple indirect answers from each respondent. We develop a Fisher scoring algorithm for the maximum likelihood estimation in the presented new schemes and show the better efficiency of the new schemes compared with the original designs.

Bayesian Analysis of a Sensitive Proportion for a Small Area

“…There are similar difficulties for estimation and multiple answers are proposed (e.g. Groenitz, ); the key gain is a random mechanism is not needed.…”