Collaborative Project: RUI - Development of Statistical Modeling Methods for Analysis of Social and Behavioral Science Data with Nonignorable Nonresponse

RUI-合作项目：开发不可忽视的无响应的社会和行为科学数据分析的统计建模方法PIS：Mortaza Jamshidian和Ke-Hai袁NSF建议SES-0407258和SES-0437167摘要该项目开发了对社会和行为科学中出现的不完整数据进行建模的方法。主要的重点是分析具有不可忽略的无响应的数据，使用结构方程模型和考虑数据机制的方法。研究人员研究和开发了(1)选择和模式混合模型方法来联合建模缺失数据机制和观测数据的变化，(2)通过发展均值和协方差的同质性统计检验，利用聚类法和潜变量回归模型，将数据分成具有相同数据机制的组的方法，(3)使用不可忽略的无响应数据的预测模型来估计缺失数据的多重补偿方法，以及(4)应用考虑缺失性的各种Bootstrap方法。研究人员开发了理论上可靠的统计方法，理论通过广泛的模拟研究进行评估，方法通过对真实数据的应用进行检验，特别是来自圣母大学青少年育儿项目的数据，这是一项正在进行的青少年育儿纵向研究。研究人员开发统计方法来分析不完整的数据。在社会科学和行为科学中，数据通常是通过纵向研究和问卷调查收集的。受试者缺乏合规性(例如，退出研究和/或回答不完整)，导致数据不完整是很常见的。本项目重点分析非随机丢失的数据(MANAR)。当由于变量的值不典型而没有观察到变量的情况时，就会发生Mnar；例如，受试者不服从她的抑郁程度的测量，因为她异常抑郁。到目前为止，SBS还没有探索出足够的统计方法来分析Mnar数据。研究人员制定了新的模型，开发了针对Mnar数据的推理和计算方法，并用社会和行为科学数据集说明了这些方法。在后一方面，研究人员专注于应用方法论来分析圣母大学收集的一组研究青少年育儿的数据。这些分析是在结构方程模型的背景下进行的，该模型已广泛应用于教育、医学、心理学、社会学和其他与人类行为相关的领域。

RUI - Collaborative Project: Development of Statistical Modeling Methods for Analysis of Social and Behavioral Science Data with Nonignorable NonresponsePIs: Mortaza Jamshidian and Ke-Hai YuanNSF proposals SES - 0407258 and SES-0437167AbstractThis project develops methods for modeling incomplete data thatarise in social and behavioral sciences (SBS). The main focus isanalysis of data with nonignorable nonresponse, usingstructural equation models and methods that take into account themissing data mechanism. The investigators study and develop (1)selection and pattern mixture model approaches to jointly modelthe missing data mechanism and the variation in the observed data,(2) methods to segment the data into groups having the samemissing data mechanism via development of statistical tests of homogeneity of mean and covariances, utilization of clustering methods as well as latent variable regression models, (3) multiple imputation methods that use predictive models for nonignorable nonresponse data to impute missing data, and (4) application of various types of bootstrap methods that take into account missing-ness. The investigators develop theoretically sound statistical methods, theories are assessed by extensive simulation studies, and methods are examined by application to real data, specifically the data from theUniversity of Notre-Dame Adolescent Parenting Project, an on-going longitudinal study of teen parenting. The investigators develop statistical methodology for analysis of data that are not complete. In social and behavioral sciences, data are often collected in longitudinal studies and through questionnaires. Lack of compliance of subjects (e.g., dropping out of studies and/or incomplete responses) that leads to incomplete data is commonplace.This project focuses on analysis of data that are missing not at random (MNAR). MNAR occurs when a case of a variable is not observed due to the value of that variable being atypical; for example, a subject does not submit to a measure of the level of her depression because she is unusually depressed. To-date, adequate statistical methodology to analyze MNAR data has not been explored in SBS. The investigators formulate new models, develop inferential and computational methods for MNAR data, and illustrate the methods with social and behavioral science data sets. In the latter respect, the investigators concentrate in applying the methodology to analyze a set of data collected by University of Notre Dame which studies teen parenting. The analyses are carried out in the context of structural equation modeling which has been widely used in a variety of disciplines including education, medicine, psychology, sociology, and other areas related to human behavior.