Collaborative Research: Modeling Sample Selection for Multi-Level Data Structures

摘要政治科学研究的一个重要组成部分是对事件数据的分析，研究人员在分析中说明事件发生的时间、频率，并试图解释事件发生的原因。政治学家感兴趣的例子包括战争、大规模抗议和政变等话题。与这类研究相关的一个问题是，被观察到的事件是许多其他因素或条件的产物。因此，研究人员可能无法确定导致该结果的实际原因或一系列因果因素的相对贡献。由于对特定事件的分析是政治科学家的兴趣所在，因此开发方法使研究人员能够得出有关这些事件如何发生的正确结论是很重要的。研究小组建议开发一种统计方法，以克服与事件数据分析相关的问题。该方法有可能提高我们对重要事件类型如何发生的理解，使其成为一系列社会科学学者的极大兴趣。该研究项目还将提供一个重要的教育组成部分，因为该团队建议让本科生参与研究经验。技术摘要PI注意到，在社会科学中收集的许多事件数据是结构数据生成过程的产物，通常仍未被分析。结果，观察到的事件与许多协变量相关。如果处理不当，对这些数据的统计分析可能导致有偏见的推论。这些问题是小n或基于区域的研究特别关注的问题。PI的建议是发展一种有理论依据的、统计的方法来研究如何从结构上选择数据。该项目由三个部分组成。首先，他们开发了一种估计程序，用于在更高层次的结构数据分析中重复观察。其次，他们将产生桥梁不同时期的基于时间的跨级别聚合的工作。第三，扩展结构选择框架，超越两层结构。成功地开发该程序将有助于改进数据的收集和分析，从而有助于减少侧重于少数案例的厚重描述性方法与大n研究中变量的稀疏信息操作之间的权衡。这项工作将应用于政治学以外的领域，吸引许多社会科学学科的学者。该项目将提供重要的教学效益，因为PI将让本科生参与研究。

General AbstractAn important part of political science research entails the analysis of events data in which the researcher accounts for when events occur, how often they occur, and tries to explain why they occur. Examples of interest to political scientists include topics like wars, mass protests, and coups, for instance. One of the problems associated with this type of research is that the event being observed is the product of a number of other factors or conditions. As a consequence, the researcher may not be able to identify the actual causes or the relative contributions of a set of causal factors for that outcome. Since the analysis of particular events is of such interest to political scientists, it is important to develop methods that allow researchers to draw the correct conclusions about how such events came to occur. The research team proposes to develop a statistical method to overcome the problems associated with events data analysis. The method has the potential to improve our understanding of how important types of events come to occur, making it of great interest to scholars across a range of social sciences. The research project will also provide a significant educational component as the team proposes to involve undergraduate students in the research experience. Technical AbstractThe PI's note that much of the events data collected in the social sciences is the product of a structural data generation process that generally remains unanalyzed. As a result the observed events are correlated with a number of covariates. Statistical analyses of such data may lead to biased inferences if not appropriately addressed. These problems are of particular concern for small-n, or area-based research. The PI's propose to develop a theoretically-informed, statistical approach to how data is selected structurally. The project consists of three components. First, they develop an estimation procedure for repeated observations at higher levels of analysis with structural data. Second, they will produce work that bridges different periods of time-based aggregation across levels. Third, they extend the structural selection frame to move beyond two-level structures. Successfully developing the procedure will help to improve the collection and analysis of data, thereby helping to reduce the trade-off between thick descriptive approaches focusing on a few cases and thinly-informative operationalizations of variables in large-n studies. The work will have applications beyond political science, appealing to scholars in a number of social scientific disciplines. The project will provide important pedagogical benefits as the PI's will involve undergraduate students in the research.