Masking data: a solution to social desirability bias in paired comparison experiments

被引：7

作者：

Shah, Said Farooq ^{[1
]}

Cheema, Salman Arif ^{[2
]}

Hussain, Zawar ^{[3
]}

Shah, Ejaz Ali ^{[4
]}

机构：

[1] Quaid I Azam Univ, Dept Stat, Islamabad, Pakistan

[2] Univ Newcastle, Sch Math & Phys Sci, Callaghan, NSW, Australia

[3] Cholistan Univ Vet & Anim Sci, Dept Social & Allied Sci, Bahawalpur, Pakistan

[4] Hazara Univ, Dept Math & Stat, Mansehra, Pakistan

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2022年 / 51卷 / 06期

关键词：

Masked data; MCMC; paired comparisons; preference probability; ranking; randomized response; social desirability bias; RANDOMIZED-RESPONSE TECHNIQUE; MODELS; VOTERS;

D O I：

10.1080/03610918.2019.1710191

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper deals with the reduction of evasive answering bias in paired comparisons studies by providing a masking mechanism to the judges. Specifically, the Warner (1965) masking design is applied in the application of Bradley and Terry (1952) paired comparison model. For estimating the worth parameters and preference probabilities, Bayesian method of estimation is applied. To study the behavior of Bayes estimates and the effect of masking parameter, simulation study is performed. It is observed that the preference ordering is not disturbed when the number of judges is moderate to large. Also, the preference ordering is observed to be robust with respect to the extent of masking, when the number of judges is large. These findings are also supported by a numerical study using real data.

引用

页码：3149 / 3167

页数：19

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