趣赢平台 @ 2021 Joint Statistical Meetings (JSM)

The Joint Statistical Meetings (JSM), to be held virtually 8月8日-12, 2021, is one of the largest statistical events in the world. More than 13 professional societies participate, and the conference brings together 6,500+ attendees from 52 countries, 80 +参展商, 1,超过1000名学生出席, and 75+ employers hiring for 200+ positions. Whether virtual or in person, 趣赢平台 continues to deliver leading-edge research for the event.

“每年, JSM为所有人提供了一个绝佳的机会，让我们与来自全国和全球的同事们聚在一起，建立联系,” notes 趣赢平台 Vice President Jeri Mulrow, Statistics and Evaluation Sciences Director. “With the unprecedented challenges of 2020 behind us, 韦斯特的统计学家和数据科学家正在分享创新的想法和方法，这些想法和方法帮助我们推动了这一领域的发展, address client research needs, and enhance statistical research for all.”

Learn more how we can help you meet your project challenges. Check out 专家 Guiding the Way (PDF), which illustrates our multimode capabilities and innovative ways to advance the data collection science. 

今年, in the spirit of the event’s theme of Statistics, Data, 以及他们讲述的故事, we share a selection of our statistics and data stories below. (Note: * indicates presenter.)

周日, 8月8日, 2021: 3:30-5:20 pm(美国东部时间):纵向调查微数据中重新识别风险的估计方法[j] .李建柱*, 林李, 汤姆Krenzke

Protecting survey respondents’ confidential data is paramount. Before releasing statistical data products, a risk assessment needs to take place to ensure that the disclosure risk is at an acceptably low level. 在纵向调查中, because the same respondents participate in more than one wave of a survey, the re-identification risk is usually higher than the risk in cross-sectional data. 不随时间变化或模式变化的公共变量可能允许用户将单个文件中的记录连接起来，形成纵向记录. 在这里, 我们分享一个调查示例来演示使用对数线性建模方法来测量重新识别风险，同时结合数据的纵向性质, which measures the increase of longitudinal risk relative to the cross-sectional risk.

周二, 8月10日, 2021: 10-11:50 am(美国东部时间):利用行政数据提高儿童乘客安全:Elizabeth Petraglia*

行政, 或“发现,数据(税务记录), 传感器数据, transaction receipts) have become common resources for research. 但是，其他数据来源可以帮助研究人员在提高统计效率和降低成本的同时，经常得出更详细的结论. 我们将介绍, 例如, the National Digital Car Seat Check Form (NDCF), 在一次典型的汽车座椅检查过程中，收集有关儿童乘客安全的详细和方便用户的行政数据. 在这里 we explore the data offerings of NDCF and some of the innovative methods, such as visualization-based and personalized dashboards, that are used to distribute NDCF data to users with varying levels of data literacy. We examine how the NDCF compares to selected CPS-related surveys and observational studies, assessing the strengths and limitations of each source in terms of coverage, 细节, 数据质量, 样本大小. We wind up this story by providing recommendations for practical applications in the field, including exploratory work on combining survey-based data with the NDCF.

周三, 8月11日, 2021:下午1:30-3:20(美国东部时间):当采样后合格框架信息可用时，为PPS样本创建基本权重和复制权重，并添加补充样本:陈建茹*, 伊斯梅尔·弗洛雷斯·塞万提斯, 迈克Kwanisai

当回复率低或不合格率高时，在调查中使用补充样本来增加样本量. 在选择了主样本之后，没有直接的方法来绘制与大小成比例的系统概率(PPS)样本设计的补充样本. 我们研究了一种情况，即在主样本选择之后，但在绘制补充样本之前，在已知的采样框架中存在大量不合格的情况. 通过随机偏移主样本区间的随机起点，绘制一个不重叠的补充样本. 我们探索和评估了几种创建基本和复制权重的方法，这些方法正确地反映了该设计的方差估计. Finally, we compare the empirical bias and variance for these methods using Monte Carlo simulations.

周三, 8月11日, 2021: 1:30-3:20 pm(美国东部时间):两种CHAID软件包在调查非响应建模中的比较:林天焕*, 卡洛斯Arieira, 伊斯梅尔·弗洛雷斯·塞万提斯, 迈克Kwanisai

When it comes to unit nonresponse, 通常的做法是通过建模反应倾向和调整权重来减少偏差，以解释不同的反应倾向. CHAID(卡方自动交互检测器)算法通常用于为此目的生成权重类, 这让我们分析了两个流行的实现CHAID算法的软件包:SI-CHAID和HPSPLIT. 我们将通过使用复杂调查样本设计的模拟来检查包装的互换性，根据加权估计的结果偏差和方差来描述两种包装的优点和缺点.

周三, 8月11日, 2021:下午1:30-3:20(美国东部时间):区域样本设计中从人口普查块中形成分段的方法的评估:詹妮弗·卡利*, 汤姆Krenzke, 陈应, 剑陈, 吉姆·格林

亲自调查通常采用多阶段抽样设计，在称为分段的地理区域内对家庭进行抽样, improving cost efficiency by restricting the geographic range that data collectors travel. Often, segments are formed by grouping neighboring census blocks. A simple method to combine adjacent census blocks is to sort the census block file by the census block ID, which often creates segments that are not contiguous, not complete (contain holes), 不紧凑. 在确定样本框架中包含哪些住房单元时，连续性和完整性问题给数据收集人员带来了挑战. Less compact segments increase interviewer travel costs. We will review alternative approaches to forming segments, evaluating the segments formed by each sorting method according to contiguity, 完整性, 密实度, and between-segment variance, and will present segment formation algorithm that uses all 4 sorting methods.

周三, 8月11日, 2021:下午1:30-3:20(美国东部时间):聚类样本设计下的调查无响应建模:分类和回归树方法比较:Michael Jones*, 威廉·埃弗雷特·塞西尔, Tien-Huan林, 詹妮弗·卡利, 伊斯梅尔·弗洛雷斯·塞万提斯

When computing survey weights for use in analyzing complex sample survey data, an adjustment for nonresponse is often performed to reduce the bias of the estimates. 您可以使用许多算法和方法对这些调整的调查非响应进行建模. 最好的方法是什么? We dig down deep and compare select algorithms when working with a complex cluster sample design. 我们还评估了基于分类树的方法在高响应和低响应设置中减少非响应偏差的效果, and investigate the performance of the methods when they are used to adjust survey weights. 在使用聚类样本的调查中，使用这些方法来估计反应倾向的好处和局限性是什么? 我们也会讨论它们.