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Keywords = social desirability bias (SDB)

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23 pages, 2631 KiB  
Article
Impact of Auxiliary Information and Measurement Errors on Mean Estimation with Mixture Optional Enhanced Trust (MOET) Randomized Response Model
by Michael Parker, Sat Gupta and Sadia Khalil
Axioms 2025, 14(3), 183; https://doi.org/10.3390/axioms14030183 - 28 Feb 2025
Viewed by 484
Abstract
Randomized response technique (RRT) surveys are designed to secure honest answers to sensitive questions. In this study, we consider the important issue of measurement error (ME). While non-response, a common culprit for survey inaccuracy, is a lesser issue in RRT studies because they [...] Read more.
Randomized response technique (RRT) surveys are designed to secure honest answers to sensitive questions. In this study, we consider the important issue of measurement error (ME). While non-response, a common culprit for survey inaccuracy, is a lesser issue in RRT studies because they are conducted through face-to-face interviews, measurement error is of particular significance. RRT models are generally more complex than other survey methods, sometimes requiring that respondents follow ordered instructions, draw cards from decks, and/or perform simple mathematical calculations. All of these steps can result in measurement errors, and when such error is high, estimation efficiency will suffer. In this study, we consider the impact of measurement error on a Mixture Optional Enhanced Trust (MOET) RRT model proposed in 2024, and we propose new estimators for this model that take measurement error into account. We also study the extent to which measurement error can be tolerated before it is so large that it overwhelms and undermines the benefit that RRT was implemented to yield in the first place (the reduction in or elimination of social desirability bias-related untruthfulness). We also draw attention to a surprising finding—that the presence of measurement error inadvertently serves to provide additional scrambling, thereby leading to an increase in privacy. Full article
(This article belongs to the Special Issue New Perspectives in Mathematical Statistics)
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17 pages, 2171 KiB  
Article
A Ratio Estimator for the Mean Using a Mixture Optional Enhance Trust (MOET) Randomized Response Model
by Sat Gupta, Michael Parker and Sadia Khalil
Mathematics 2024, 12(22), 3617; https://doi.org/10.3390/math12223617 - 20 Nov 2024
Cited by 2 | Viewed by 818
Abstract
When researchers conduct surveys seeking sensitive, socially stigmatized information, respondents, on average, modify their answers to represent themselves favorably. To overcome this issue, researchers may use Randomized Response Technique (RRT) models. Recently, Parker et al. proposed a model that incorporates some of the [...] Read more.
When researchers conduct surveys seeking sensitive, socially stigmatized information, respondents, on average, modify their answers to represent themselves favorably. To overcome this issue, researchers may use Randomized Response Technique (RRT) models. Recently, Parker et al. proposed a model that incorporates some of the most critical recent quantitative RRT advancements—mixture, optionality, and enhanced trust—into a single model, which they called a Mixture Optional Enhanced (MOET) model. We now improve upon the MOET model by incorporating auxiliary information into the analysis. Positively correlated auxiliary information can improve the mean response estimation through use of a ratio estimator. In this study, we propose just such an estimator for the MOET model. Further, we investigate the conditions under which the ratio estimator outperforms the basic MOET estimator proposed by Parker et al. in 2024. We also consider the possibility that the collection of auxiliary information may compromise privacy; and we study the impact of privacy reduction on the overall model performance as assessed by the unified measure (UM) proposed by Gupta et al. in 2018. Full article
(This article belongs to the Special Issue Statistical Simulation and Computation: 3rd Edition)
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13 pages, 1300 KiB  
Article
A Mixture Quantitative Randomized Response Model That Improves Trust in RRT Methodology
by Michael Parker, Sat Gupta and Sadia Khalil
Axioms 2024, 13(1), 11; https://doi.org/10.3390/axioms13010011 - 22 Dec 2023
Cited by 3 | Viewed by 1770
Abstract
The Quantitative Randomized Response Technique (RRT) can be used by researchers to obtain honest answers to questions that, due to their sensitive (socially undesirable, dangerous, or even illegal) nature, might otherwise invoke partially or completely falsified responses. Over the years, Quantitative RRT models, [...] Read more.
The Quantitative Randomized Response Technique (RRT) can be used by researchers to obtain honest answers to questions that, due to their sensitive (socially undesirable, dangerous, or even illegal) nature, might otherwise invoke partially or completely falsified responses. Over the years, Quantitative RRT models, sometimes called Scrambling models, have been developed to incorporate such advancements as mixture, optionality and enhanced trust, each of which has important benefits. However, no single model incorporates all of these features. In this study, we propose just such a unified model, which we call the Mixture Optional Enhanced Trust (MOET) model. After developing methodologies to assess MOET based on standard approaches and using them to explore the key characteristics of the new model, we show that MOET has superior efficiency compared to the Quantitative Optional Enhanced Trust (OET) model. We also show that use of the model’s mixture capability allows practitioners to optimally balance the model’s efficiency with its privacy, making the model adaptable to a wide variety of research scenarios. Full article
(This article belongs to the Special Issue Computational Statistics and Its Applications)
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