A Multi-Proportions Randomized Response Model
多类别比例的随机化回答模型AI 解读 ↓
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多类别比例的随机化回答模型
- 研究问题
- 调查涉及多个类别且部分特征可能带来污名时，如何通过随机化回答估计类别比例，并权衡方差与如实回答程度？
- 方法与思路
- 作者将二分类随机化回答推广到多类别情形，详细研究三类别估计，并在两种访谈都可能出现说谎的假设下比较表现。
- 材料陈述的贡献
- 摘要称，三类别估计无偏但方差较常规估计大，合作改善可能补偿该损失，并讨论条件性优势与实地实施经验。
- 阅读关注点
- 应核对随机化机制、无偏性所依赖的回答行为、说谎模型与比较准则，以及调查员能力对实地实施的影响。
- 阅读边界
- 仅据所给 OpenAlex 索引摘要，稿件版本未注明；行为假设和实地结果细节未提供，不能保证随机化必然提高真实性或精度。
原文摘要
In surveys of human populations respondents are not likely to participate or tell the truth when the reply may tend to stigmatize them in the eyes of the surveyor. Warner developed a technique for estimating the proportion of persons with such a sensitive characteristic in a binomial situation by having the respondent answer only “Yes” or “No” to one of two questions selected at random. This technique is hereby extended to the case where the population can be divided into t categories in which at least one and not more than (t − 1) of them are stigmatizing. The case when t = 3 is investigated in detail and the estimates are shown to be unbiased. The variances are larger than those for regular trinomial estimators but this loss may be compensated for by greater cooperation. Under the assumption that respondents may tend to lie under either interviewing method, comparisons are made to determine where the randomizing technique still has an advantage. An example of a field trial is cited where the method was shown to work only with highly skilled interviewers and on more intelligent respondents. Improvements in field technique are also suggested.