Collaborative Research: Nonparametric Models for Incomplete Clustered Data with Applications to the Social Sciences

协作研究：不完整聚类数据的非参数模型及其在社会科学中的应用

• 批准号: 9986592
• 负责人: Michael Akritas
• 金额: $ 7.05万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2002-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9986592&HistoricalAwards=false
• 关键词: Collaborative; Research; Nonparametric; Models; Incomplete

Clustered data are very common in social sciences research and other fields. For example, in a study involving school children, school districts form clusters and schools form sub-clusters within each cluster. In this context, researchers want to explain a certain variable of interest (the response variable) in terms of certain categorical variables (factors) while adjusting for the presence of other incidental variables (covariates) which might influence the response. This project aims at developing statistical methods for analyzing such data. Though the classical statistical methods accommodate the lack of independence that is inherent to data arising from cluster sampling, they are very often unsuitable for data arising from social science research. This is because they require a set of restrictive assumptions (such as normality and homogeneity of the residuals, linearity, scale dependence) which are rarely satisfied in the social sciences. In addition, data in social sciences research are often incomplete (censored or missing) in which case inference based on the classical statistical models cannot be implemented. Alternative approaches developed to deal with these issues also rely on assumptions which may or may not be satisfied for any given application. The research for this project will focus on the development of statistical models and methods that are free of restrictive assumptions. Central components of the project is the application of these methods to questions regarding routine activities and deviant behavior, and to the question of whether there has been a secular rise in job instability among young adults over the past three decades using two cohorts from the National Longitudinal Survey (NLS). Programs for formal hypothesis testing, graphical summaries of effects and exploratory data analysis plots, will be made available on the web for use by the social sciences community.Correct statistical analysis is very important as it often forms the basis for policy and other decisions. The nonparametric formulation in this project is especially apt for many kinds of social science data where we have weak theories about functional forms and weak measurement procedures (e.g., with attitudes) that produce ordinal or only somewhat stronger (but typically not interval) scales. The violation of assumptions underlying a statistical approach can result in misuse of scarce data resources and ultimately misguided policy decisions.

在社会科学研究和其他领域，数据挖掘是非常普遍的。 例如，在一项涉及学童的研究中，学区形成群组，学校在每个群组内形成子群组。 在这种情况下，研究人员希望用某些分类变量（因素）来解释某个感兴趣的变量（响应变量），同时调整可能影响响应的其他偶然变量（协变量）的存在。 该项目旨在开发分析此类数据的统计方法。 虽然经典的统计方法适应了缺乏独立性，这是固有的，从集群抽样产生的数据，他们往往不适合来自社会科学研究的数据。 这是因为它们需要一组限制性的假设（如残差的正态性和同质性，线性，尺度依赖性），这在社会科学中很少得到满足。 此外，社会科学研究中的数据往往是不完整的（审查或缺失），在这种情况下，无法实施基于经典统计模型的推断。 为处理这些问题而开发的替代方法也依赖于对任何特定应用可能满足或可能不满足的假设。 该项目的研究将侧重于开发不受限制性假设的统计模型和方法。 该项目的核心组成部分是应用这些方法的问题，关于日常活动和越轨行为，以及是否有一个长期上升的问题，在工作不稳定的年轻人在过去的三十年中，使用两个队列从国家纵向调查（NLS）。 正式的假设检验程序、效果的图形摘要和探索性数据分析图将在网上提供给社会科学界使用。正确的统计分析非常重要，因为它往往构成政策和其他决定的基础。 本项目中的非参数公式特别适用于许多类型的社会科学数据，其中我们对函数形式和弱测量程序的理论很弱（例如，与态度），产生顺序或只是稍微强（但通常不是间隔）规模。 违反统计方法所依据的假设可能导致滥用稀缺的数据资源，并最终导致错误的政策决定。