Binary Randomized Response Technique (RRT) models under measurement error

Abstract

In real-world surveys, measurement error is usually inevitable. It is the difference between the actual value of the variable being measured and its recorded value. Many authors in the field of Randomized Response Technique (RRT) have studied the impact of measurement error on quantitative RRT models, but there are no such studies for binary RRT models. In this article, we propose a binary RRT model under measurement error based on the previous work of Warner (1965). A simulation study is presented to validate the theoretical findings. Simulations show that the measurement error factor cannot be ignored when using binary RRT models, and the proposed estimator from the binary RRT model under measurement error performs well.