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

顺序混合模式调查混合使用多种模式或数据收集方法，如邮件、电话、面对面和网络，以增加对调查做出答复的人数。在顺序设计中，通常不控制将受访者的子组分配到模式。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，因为通常只有有限的资金来调查人们的反应。虽然使用混合模式的目标是明确的，但一个引人注目的研究问题是，模式的非随机混合如何影响调查数据，以及在估计调查人群特征(如平均收入和医疗保险覆盖范围)时应如何处理这些影响。到目前为止，由于模式的非随机混合给模式效应的评估带来了挑战，现有的推断方法假设模式效应在顺序混合模式调查中可以被忽略，尽管它们对调查估计的质量有未知的影响。本研究发展并评估计入非随机模式效应的统计推断方法，以检验不同模式调查估计的可比性。同时，这个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，同时考虑了无响应和非随机模式效应。1973年公共使用的当前人口调查(CPS)和社会保障记录完全匹配，以及非公共使用的美国社区调查(ACS)数据将被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断方法，在顺序混合模式调查的背景下，这些方法可以针对无回答和非随机模式影响进行调整。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性质是未知的，现有的评估和推断方法不能控制非随机模式效应。这项研究提出了序贯混合模式评估方法，将检验可能威胁调查数据质量的模式影响的可忽略程度。同时，这项研究还提出了在存在不可忽视的模式效应的情况下产生更高质量调查估计的推理方法。

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.