New randomized response technique for estimating the population total of a quantitative variable

TL;DR

This paper introduces a new randomized response method for estimating population totals of quantitative variables that enhances privacy, encourages cooperation, and can be adapted to various sampling schemes.

Contribution

It proposes a novel randomized response technique that protects privacy while estimating population totals, with modifications for known random numbers and respondent embarrassment.

Findings

Method provides high privacy protection and increased respondent cooperation.

Simulation results demonstrate the effectiveness of the proposed technique.

Applicable to various sampling schemes beyond simple random sampling.

Abstract

In this paper, a new randomized response technique aimed at protecting respondents' privacy is proposed. It is designed for estimating the population total, or the population mean, of a quantitative characteristic. It provides a~high degree of protection to the interviewed individuals, hence it may be favorably perceived by them and increase their willingness to cooperate. Instead of revealing the true value of the characteristic under investigation, a respondent only states whether the value is greater (or smaller) than a~number which is selected by him/her at random, and is unknown to the interviewer. For each respondent this number, a sort of individual threshold, is generated as a pseudorandom number from the uniform distribution. Further, two modifications of the proposed technique are presented. The first modification assumes that the interviewer also knows the generated random number. The second modification deals with the issue that, for certain variables, such as income, it may be embarrassing for the respondents to report either high or low values. Thus, depending on the value of the pseudorandom lower bound, the respondent is asked different questions to avoid being embarrassed. The suggested approach is applied in detail to the simple random sampling without replacement, but it can also be applied to many currently used sampling schemes, including cluster sampling, two-stage sampling, etc. Results of simulations illustrate the behavior of the proposed procedure.