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

RUI -合作项目：社会和行为科学数据分析的统计建模方法的发展：Mortaza Jamshidian和Ke-Hai YuanNSF建议SES - 0407258和SES-0437167摘要本项目开发了对社会和行为科学（SBS）中出现的不完整数据进行建模的方法。主要关注的是分析数据与非可解释的无响应，使用结构方程模型和方法，考虑到丢失数据的机制。研究人员研究和开发（1）选择和模式混合模型方法，以联合建模缺失数据机制和观察数据中的变化，（2）通过开发均值和协方差齐性的统计检验，利用聚类方法以及潜变量回归模型，将数据分割为具有相同缺失数据机制的组的方法，（3）多重插补方法，使用不可解释的无应答数据的预测模型来插补缺失数据，以及（4）应用各种类型的考虑缺失的自助方法。调查人员开发理论上健全的统计方法，理论进行了广泛的模拟研究评估，并通过应用到真实的数据，特别是从圣母大学青少年养育项目，一个正在进行的纵向研究青少年养育的数据方法进行检查。研究人员开发统计方法来分析不完整的数据。在社会和行为科学中，数据通常通过纵向研究和问卷调查收集。受试者不依从（例如，辍学和/或不完整的反应），导致不完整的数据是司空见惯的。这个项目的重点是分析的数据是失踪的非随机（MNAR）。MNAR发生在由于变量的值是非典型的而没有观察到该变量的情况下;例如，受试者不接受她的抑郁水平的测量，因为她异常抑郁。迄今为止，SBS尚未探索分析MNAR数据的适当统计方法。研究人员制定新的模型，开发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这些方法。在后一方面，研究人员集中在应用的方法来分析一组数据收集的圣母大学，研究青少年父母。这些分析是在结构方程模型的背景下进行的，结构方程模型已广泛应用于各种学科，包括教育学，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.