Statistical agencies depend on responses to inquiries made to the public, and occasionally conduct experiments to improve contact procedures. Agencies may wish to assess whether there is significant change in response rates due to an operational refinement. This work considers the assessment of response rates when up to L attempts are made to contact each subject, and subjects receive one of J possible variations of the operation under experimentation. In particular, the continuation-ratio logit (CRL) model facilitates inference on the probability of success at each step of the sequence, given that failures occurred at previous attempts. The CRL model is investigated as a basis for sample size determination– one of the major decisions faced by an experimenter–to attain a desired power under a Wald test of a general linear hypothesis. An experiment that was conducted for nonresponse followup in the United States 2020 decennial census provides a motivating illustration.

中文翻译：

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使用顺序回归模型设计和确定无响应后续实验的样本量

统计机构依赖于对公众查询的回应，并偶尔进行实验以改进联系程序。机构可能希望评估响应率是否因操作改进而发生重大变化。这项工作考虑了响应率的评估大号尝试联系每个对象，并且对象收到以下内容之一杰实验中操作的可能变化。特别是，连续比率逻辑 (CRL) 模型有助于推断序列每个步骤的成功概率，前提是之前的尝试发生了失败。CRL 模型被研究作为样本量确定的基础——实验者面临的主要决定之一——在一般线性假设的 Wald 检验下获得所需的功效。在美国 2020 年十年一次的人口普查中针对无应答跟进进行的一项实验提供了一个激励性的例证。