How many federal employees are not satisfied? Using response times to estimate population proportions under the survey variable cause model
有多少联邦雇员不满意？利用调查回应时间估计总体比例AI 解读 ↓
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有多少联邦雇员不满意？利用调查回应时间估计总体比例
- 研究问题
- 当被调查特征本身直接影响参与意愿时，如何利用回应时间估计总体比例，并分析传统后分层难以消除的调查不回应偏差？
- 方法与思路
- 作者在调查变量致因模型下刻画受访者进入调查的时间，借助计数过程方法估计总体比例，并应用于联邦雇员工作满意度调查。
- 材料陈述的贡献
- 摘要提出依赖不同于后分层假设的偏差分析方法，并报告其模型提示：样本即使后分层后，仍可能高估总体不满意比例。
- 阅读关注点
- 应核对回应时间如何识别总体比例、未回应者的处理方式，以及计数过程模型假设、后分层比较和结论对模型设定的敏感性。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。不满意者选择参与是模型支持的解释，摘要未说明识别条件、真实总体比例或估计数值。
原文摘要
We propose a statistical model to estimate population proportions under the survey variable cause model (Groves 2006), the setting in which the characteristic measured by the survey has a direct causal effect on survey participation. For example, we estimate employee satisfaction from a survey in which the decision of an employee to participate depends on their satisfaction. We model the time at which a respondent 'arrives' to take the survey, leveraging results from the counting processes literature that has been developed to analyze similar problems with survival data. Our approach is particularly useful for nonresponse bias analysis because it relies on different assumptions than traditional adjustments such as poststratification, which assumes the common cause model, the setting in which external factors explain the characteristic measured by the survey and participation. Our motivation is the Federal Employee Viewpoint Survey, which asks federal employees whether they are satisfied with their work organization. Our model suggests that the sample proportion overestimates the proportion of federal employees that are not satisfied with their work organization even after adjustment by poststratification. Employees that are not satisfied likely select into the survey, and this selection cannot be explained by personal characteristics like race, gender, and occupation or work-place characteristics like agency, unit, and location.