Doctoral Dissertation Research: Investigating the Bias of Alternative Statistical Inference Methods in Sequential Mixed-Mode Surveys

博士论文研究：调查序贯混合模式调查中替代统计推断方法的偏差

• 批准号: 1238612
• 负责人: Richard Valliant
• 金额: $ 1.52万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1238612&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Investigating; Bias

Sequential mixed-mode surveys use a mix of modes or data collection methods such as mail, telephone, in-person, and web to increase the number of people who respond to a survey. In sequential designs, there is usually no control in assigning subgroups of respondents to modes. As a result, nonrandom assignment of modes is an inherent characteristic of sequential mixed-mode surveys. This design is important since there are usually limited funds to probe people to respond. While the goal of using mixed modes is clear, one compelling research question is how the nonrandom mix of mode impacts survey data and how these effects should be handled in estimating survey population characteristics such as mean income, and health insurance coverage. To date, since the nonrandom mix of modes poses a challenge in evaluating the mode effects, the existing inference methods assume that mode effects can be ignored in sequential mixed-mode surveys despite their unknown impact on the quality of the survey estimates. This research develops and evaluates the statistical inference methods accounting for nonrandom mode effects to test the comparability of the survey estimates from the different modes. In parallel, this project also develops statistical inference methods accounting for both nonresponse and nonrandom mode effects in the presence of nonignorable mode effects. The public-use Current Population Survey (CPS), 1973, and Social Security Records Exact Match, and the nonpublic-use American Community Survey (ACS) data will be used to conduct empirical and simulation evaluations. This research provides federal agencies, survey organizations, research centers, and other data producers assessment and inferential methods that adjust for both nonresponse and nonrandom mode effects in the context of sequential mixed-mode surveys. Some large surveys have employed some variation of mixed-mode surveys in order to meet budget constraints. On the other hand, in the presence of nonignorable mode effects, the bias properties for the survey population characteristics are not known and the existing assessment and inferential methods do not control for the nonrandom mode effects. This research produces sequential mixed-mode assessment methods which will test the ignorability of the mode effects which can be a threat for the quality of survey data. In parallel, this research also produces methods of inference which will yield higher quality survey estimates in the presence of nonignorable mode effects.

顺序混合模式调查使用多种模式或数据收集方法（例如邮件、电话、面对面和网络）来增加对调查做出回应的人数。在顺序设计中，通常无法控制将受访者子组分配给模式。因此，模式的非随机分配是顺序混合模式调查的固有特征。这种设计很重要，因为通常用于调查人们做出反应的资金有限。虽然使用混合模式的目标很明确，但一个引人注目的研究问题是模式的非随机组合如何影响调查数据，以及在估计平均收入和健康保险覆盖率等调查人口特征时应如何处理这些影响。迄今为止，由于模式的非随机混合对评估模式效应提出了挑战，现有的推理方法假设模式效应在顺序混合模式调查中可以忽略不计，尽管它们对调查估计质量的影响未知。本研究开发并评估了考虑非随机模式效应的统计推断方法，以测试不同模式的调查估计的可比性。与此同时，该项目还开发了统计推断方法，在存在不可忽略的模式效应的情况下解释无响应和非随机模式效应。公共使用的1973年人口调查（CPS）和社会保障记录精确匹配数据以及非公共使用的美国社区调查（ACS）数据将用于进行实证和模拟评估。这项研究为联邦机构、调查组织、研究中心和其他数据生产者提供了评估和推理方法，这些方法可以在顺序混合模式调查的背景下调整无响应和非随机模式效应。一些大型调查采用了混合模式调查的一些变体，以满足预算限制。另一方面，在存在不可忽略的模式效应的情况下，调查总体特征的偏差属性是未知的，并且现有的评估和推断方法不能控制非随机模式效应。这项研究产生了顺序混合模式评估方法，该方法将测试可能对调查数据质量构成威胁的模式效应的可忽略性。与此同时，这项研究还产生了推理方法，在存在不可忽略的模式效应的情况下，将产生更高质量的调查估计。