A Note on a Simple and Practical Randomized Response Framework for Eliciting Sensitive Dichotomous & Quantitative Information

TL;DR

This paper introduces a simple, unified randomized response framework for collecting sensitive binary and quantitative data, emphasizing its practicality and potential for computer-assisted implementation to improve data collection in social science research.

Contribution

It proposes a new, unified randomized response method for both binary and quantitative sensitive data, enhancing practicality and implementation in computer-assisted surveys.

Findings

The framework effectively elicits sensitive information with reduced bias.

It is simple to implement and adaptable to computer-assisted surveys.

Potential for improved data accuracy in sensitive surveys.

Abstract

Many issues of interest to social scientists and policymakers are of a sensitive nature in the sense that they are intrusive, stigmatizing or incriminating to the respondent. This results in refusals to cooperate or evasive cooperation in studies using self-reports. In a seminal article Warner proposed to curb this problem by generating an artificial variability in responses to inoculate the individual meaning of answers to sensitive questions. This procedure was further developed and extended, and came to be known as the randomized response (RR) technique. Here, we propose a unified treatment for eliciting sensitive binary as well as quantitative information with RR based on a model where the inoculating elements are provided for by the randomization device. The procedure is simple and we will argue that its implementation in a computer-assisted setting may have superior practical capabilities.