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

协作研究：多级数据结构的样本选择建模

• 批准号: 1729244
• 负责人: Douglas Gibler
• 金额: $ 15.82万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1729244&HistoricalAwards=false
• 关键词: Collaborative; Research; Modeling; Sample; Selection

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.

政治学研究的一个重要部分是对事件数据的分析，研究人员在分析中解释事件何时发生，发生的频率，并试图解释为什么会发生。 政治科学家感兴趣的例子包括战争，大规模抗议和政变等主题。 与这类研究相关的问题之一是，所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实际原因或一组因果因素对该结果的相对贡献。 由于对特定事件的分析对政治科学家如此感兴趣，因此重要的是要开发出使研究人员能够就这些事件如何发生得出正确结论的方法。 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类型事件如何发生的理解，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，社会科学中收集的大部分事件数据是结构化数据生成过程的产物，通常未进行分析。 因此，观察到的事件与许多协变量相关。 如果不适当处理，对这些数据的统计分析可能会导致有偏见的推论。 这些问题是特别关注的小n，或以区域为基础的研究。 PI建议开发一种理论上知情的统计方法，以确定如何从结构上选择数据。 该项目由三个部分组成。 首先，他们开发了一个估计程序，在更高层次的分析与结构数据的重复观测。第二，他们将产生的工作，桥梁不同时期的时间为基础的聚合水平。 第三，他们扩展了结构选择框架，超越了两级结构。 成功地开发程序将有助于改善数据的收集和分析，从而有助于减少集中在少数情况下的厚描述性方法和大n研究中变量的薄信息操作之间的权衡。 这项工作将有超越政治科学的应用，吸引一些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。