Statistical methods for survey data in the social sciences

我将在与复杂的调查方法相关的三个领域进行研究，共同的主题是潜在变量和联系。 第一个领域是复杂调查背景下的潜变量建模。潜变量是假设的但不可观测的变量，赋予统计模型结构和意义。例如，混合或多水平模型中的随机效应通常被认为是潜在的；结构方程模型使用潜在结构来解释观察到的心理变量之间的关系；在教育测试中使用的项目反应理论(IRT)模型，项目难度和考生能力通常被认为是潜在的。我将扩展以前关于多水平模型的工作，并将估计函数方法应用于潜在变量模型参数的稳健估计。 第二个领域是对社交网络抽样数据的分析。网络是节点的集合，其中一些节点通过链路连接。我和我的合作者致力于“拼凑抽样”，即从节点的概率样本中跟踪链接到一定距离，并记录多个包含项。应用包括学者合作网络；相互呼叫的电信客户网络；或具有疾病传播链接的联系网络。我计划将我们的初步工作扩展到连通性和复杂依赖项的估计。 第三个领域是多帧方法和数据链接。框架是一个群体成员的数据库或列表，可以从中提取样本。成员可能是可联系的，也可能不是可联系的，并且框架在提供关于成员的有用信息的意义上可能是或可能不是“丰富的”。通常情况下，数据库或样本有相当大的重叠，记录的适当链接可以使分析更有效。如果标识符不是唯一的，或者如果错误的或丢失的标识符使得链接不确定，则期望具有用于自动估计链接的技术。我将探索传统的多框架方法、记录链接方法和使用链接信息进行抽样之间的关系。 潜变量模型的工作解决了社会科学研究人员面临的两个问题：当抽样设计是结构信息的和/或模型是非正态时，需要准确的多水平分析；以及需要稳健估计的潜变量模型。提案的其余部分涉及调查方法的趋势，这是因为越来越多地可获得关于个人和机构的大量丰富和可联系的数据，以及由于没有答复，进行传统抽样调查等设计性研究的难度越来越大。潜在应用领域清单不仅包括官方统计和社会研究方法，还包括法医用途，如检测通信流量中的异常活动。 每个领域都将包括对研究生的监督。

I will pursue research in three areas related to complex survey methodology, with common themes of latent variables and linkages. The first area is latent variable modeling in the complex survey context. Latent variables are hypothesized but unobservable variables, giving structure and meaning to a statistical model. For example, a random effect in a mixed or multilevel model is often regarded as latent; structural equation models use latent constructs to explain the relationships among observed psychometric variables; in item response theory (IRT) models used in educational testing, item difficulty and test taker ability are often taken to be latent. I will extend previous work on multilevel models, and apply estimating function approaches to robust estimation of latent variable model parameters. The second area is the analysis of data from sampling on social networks. A network is a collection of nodes, some of which are joined by links. My collaborators and I have worked on “patchwork sampling”, wherein links from a probability sample of nodes are followed to a certain distance, with multiple inclusions recorded. Applications include a collaboration network of scholars; a network of telecom customers who call each other; or a contact network with links for disease transmission. I plan to extend our preliminary work to the estimation of connectivity and complex dependences. The third area is multi-frame methods and data linkage. A frame is a database or list of members of a population, from which samples can be drawn. The members may or may not be contactable, and the frame may or may not be “rich” in the sense of providing useful information about the members. Typically the databases or samples overlap considerably, and proper linkage of the records can make analysis more efficient. If the identifiers are not unique, or if erroneous or missing identifiers make linkage uncertain, it is desirable to have techniques for automatic estimation of links. I will explore relationships among traditional multi-frame methods, record linkage methods, and sampling using linkage information. The work on latent variable models addresses two problems facing social science researchers: the need for accurate multi-level analysis when the sampling design is structurally informative and/or the models are non-normal; and the need for robustly estimable latent variable models. The rest of the proposal concerns trends in survey methods arising from the increasing availability of vast amounts of rich and linkable data on people and establishments, and the increasing difficulty of carrying out designed studies such as traditional sample surveys due to non-response. The list of potential application areas includes not only official statistics and social research methods but also forensic uses such as detection of unusual activity in communications traffic. Supervision of graduate students will be included in each area.