Modeling Evasive Response Bias in Randomized Response: Cheater Detection Versus Self-protective No-Saying.

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Psychometrika Pub Date : 2024-12-01 Epub Date: 2024-08-30 DOI:10.1007/s11336-024-10000-x
Khadiga H A Sayed, Maarten J L F Cruyff, Peter G M van der Heijden
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Abstract

Randomized response is an interview technique for sensitive questions designed to eliminate evasive response bias. Since this elimination is only partially successful, two models have been proposed for modeling evasive response bias: the cheater detection model for a design with two sub-samples with different randomization probabilities and the self-protective no sayers model for a design with multiple sensitive questions. This paper shows the correspondence between these models, and introduces models for the new, hybrid "ever/last year" design that account for self-protective no saying and cheating. The model for one set of ever/last year questions has a degree of freedom that can be used for the inclusion of a response bias parameter. Models with multiple degrees of freedom are introduced for extensions of the design with a third randomized response question and a second set of ever/last year questions. The models are illustrated with two surveys on doping use. We conclude with a discussion of the pros and cons of the ever/last year design and its potential for future research.

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随机应答中的回避应答偏差建模:作弊者检测与自我保护性 "不说话"。
随机回答是一种针对敏感问题的访谈技术,旨在消除回避回答偏差。由于这种消除方法只取得了部分成功,因此提出了两个模型来模拟回避回答偏差:针对具有不同随机化概率的两个子样本的设计的作弊者检测模型,以及针对具有多个敏感问题的设计的自我保护不说模型。本文展示了这些模型之间的对应关系,并介绍了新的、混合的 "曾经/最后一年 "设计模型,这些模型考虑到了自我保护性不说模型和作弊模型。一组 "曾经/最后一年 "问题的模型有一个自由度,可用于加入一个反应偏差参数。多自由度模型适用于该设计的扩展,包括第三个随机回答问题和第二组曾经/最后一年问题。我们用两个关于兴奋剂使用情况的调查来说明这些模型。最后,我们讨论了 "曾经/最后一年 "设计的利弊及其在未来研究中的潜力。
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来源期刊
Psychometrika
Psychometrika 数学-数学跨学科应用
CiteScore
4.40
自引率
10.00%
发文量
72
审稿时长
>12 weeks
期刊介绍: The journal Psychometrika is devoted to the advancement of theory and methodology for behavioral data in psychology, education and the social and behavioral sciences generally. Its coverage is offered in two sections: Theory and Methods (T& M), and Application Reviews and Case Studies (ARCS). T&M articles present original research and reviews on the development of quantitative models, statistical methods, and mathematical techniques for evaluating data from psychology, the social and behavioral sciences and related fields. Application Reviews can be integrative, drawing together disparate methodologies for applications, or comparative and evaluative, discussing advantages and disadvantages of one or more methodologies in applications. Case Studies highlight methodology that deepens understanding of substantive phenomena through more informative data analysis, or more elegant data description.
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