Statistical Models for Categorical Dependent Variables in Social Research

社会研究中分类因变量的统计模型

• 批准号: 9310101
• 负责人: Bruce Lindsay
• 金额: $ 26.12万
• 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: 1993
• 资助国家: 美国
• 起止时间: 1993-07-01 至 1999-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9310101&HistoricalAwards=false
• 关键词: Statistical; Models; Categorical; Dependent; Variables

9310101 Clogg A five year program of research will be undertaken by the investigator to study seven interrelated statistical models for analyzing categorical dependent variables in social research. The objectives of the research are to: (1) Apply Rasch type models of the latent class variety to panel data so that unobserved heterogeneity can be characterized without extra assumptions about the form of the left out factors creating unmodeled variation; (2) Further developments in latent class analysis and measurement models; (3) Create new models and methods for regression with an ordinal dependent variable; (4) Generate models and methods for regression with a partially ordered Y; (5) Explore a new index of model fit based on mixture methods, with applications to social research; (6) Improve prediction methods associated with latent class models and apply these new prediction methods to graphic models; and (7) Model "total uncertainty" in statistical models used in social research. %%% This research program will generate and improve tools for a more rigorous empirical analysis in social research and provide results which will be of general interest to those in statistics and other areas. ***

小行星9310101 研究者将进行一项为期五年的研究计划，研究七个相互关联的统计模型，用于分析社会研究中的分类因变量。 本研究的主要目的是：（1）将潜在类别差异的Rasch型模型应用于面板数据，使未观察到的异质性可以被表征，而不需要对造成未建模差异的遗漏因素的形式进行额外的假设;（2）进一步发展潜在类别分析和测量模型;（3）建立新的有序因变量回归模型和方法;（4）生成具有偏序Y的回归模型和方法;（5）探索基于混合方法的模型拟合的新指数，并将其应用于社会研究;（6）改进与潜在类别模型相关的预测方法，并将这些新的预测方法应用于图形模型;以及（7）在社会研究中使用的统计模型中建模“总不确定性”。 该研究计划将产生和改进工具，以便在社会研究中进行更严格的实证分析，并提供统计和其他领域的普遍感兴趣的结果。***