AI Article Synopsis

  • Many survey responses can be unreliable, especially for sensitive topics, which makes direct questioning less effective.
  • The randomized response technique offers a way to protect respondents' privacy and ensure more honest answers.
  • This paper presents a new three-stage randomized response method validated through simulations, suggesting that using auxiliary information can improve estimation accuracy when studying sensitive behaviors like cheating among students.

Article Abstract

In the majority of sample surveys, the variable of interest might have sensitive characteristics. Since we frequently get unrealistic responses from respondents during interviews as employing the direct method is not seen logical in these situations. To tackle this difficulty, the randomized response technique is good substitute for the direct method. The respondent is reassured that there would not be any issues will your response when using the randomized response technique as it protects privacy and secrets. In this paper, we propose a novel quantitative three-stage randomized response technique. Using simulation with R software, we prove that the results obtained from the proposed method are ideal. We suggest ratio and product type estimators by using the auxiliary information to enhance the efficiency of estimators. It is demonstrated that these estimators are superior for the conventional ones in special situations and in applied task, the use of auxiliary variable improves the estimation of the average cheating of Shahid Chamran university of Ahvaz students. The main objective is to increase the average sensitive reaction by utilizing the auxiliary variable.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11616499PMC
http://dx.doi.org/10.1016/j.heliyon.2024.e40367DOI Listing

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