Changepoint Tests Designed for the Analysis of Hiring Data Arising in Employment Discrimination Cases
面向就业歧视案件招聘数据分析的变点检验AI 解读 ↓
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面向就业歧视案件招聘数据分析的变点检验
- 研究问题
- 如何检验就业歧视案件中招聘行为是否随时间发生变化，并区分投诉前后时期，以分析与法律责任认定更相关的招聘实践？
- 方法与思路
- 作者扩展基于累积和的变点程序以处理超几何模型及分层数据；因年度观测的大样本近似不够准确，以模拟计算统计量的p值。
- 材料陈述的贡献
- 摘要称将既有二项招聘数据程序扩展至超几何与分层数据，分析多组提交法院的数据，并利用改进的Bonferroni不等式给出新的上界。
- 阅读关注点
- 核对二项与超几何抽样机制的差别、分层处理和变点定位方式，以及模拟p值与Bonferroni上界的计算对象及误差。
- 阅读边界
- 摘要未说明模拟规模、检验功效和各案件的具体结论；检出的招聘变化如何支持法律责任认定，仍需结合全文与案件事实。
原文摘要
When a complaint of discrimination is made, an employer may respond by hiring more minorities. From a legal viewpoint, the practices in effect during the time period prior to the complaint are more relevant for determining liability than those of the postcharge period. In Gay vs. Waiters, the trial judge observed that the data suggested that a change occurred after the charge was filed. Because the data had not been subject to a formal statistical analysis, the court was reluctant to base its decision on this observation. Gastwirth and Freidlin and Gastwirth proposed cumulative-sum-based procedures for the analysis of hiring data following the binomial model. In this article, the procedures are extended to data following the hypergeometric model and to analysis of stratified data. Several datasets that were submitted to the courts in the United States are analyzed by the proposed methods. Because the data are usually reported by year, the ordinary large-sample theory is not sufficiently accurate. Therefore, we obtain the p values of the statistics by simulation. For binomial data, recent improvements in the Bonferroni inequality are used to derive a new upper bound.