SUPPORT ENVIRONMENT FOR GRADE OF MEMBERSHIP (GOM) MODEL

隶属度(GOM)是一种多元分析技术，用于 用非常一般的方法分析高维离散响应数据 分布假设。GOM作为一种研究工具在 杜克大学在过去的10年里致力于发展 以及评估全球监测系统的统计基础。几乎没有付出什么努力 旨在制作GOM的完整文档版本。因此，各种 不同版本的GoM具有不同的功能。第一个项目任务 将确定一个适当的GOM研究版本，它将 得到增强并记录下来，分发给NIA和NIH的研究人员。 GoM版本将能够处理大小为 10,000个观测值和30个分析变量的排序，并将至少 可在工作站和基于486的PC上运行。这样一个版本的GoM将是 能够分析许多国家调查数据集，包括 国家长期护理调查。 还提出了GOM模型的智能支持环境，以 协助研究人员应用GOM。支持环境将包括 三个模块：问答、数据准备和报告 分析。GOM原型环境和DOS版本的开发 建议用于第一阶段。

Grade of Membership (GoM) is a multivariate analytic technique for analyzing high dimensional discrete response data with very general distributional assumptions. GoM has been developed as a research tool at Duke University over the last 10 years with efforts focused on developing and assessing the statistical foundations of GoM. Little effort has been directed to producing a fully documented version of GoM. Thus, various versions of GoM exist with different capabilities. The first project task will be to identify an appropriate research version of the GoM which will be enhanced and documented for distribution to NIA and NIH researchers. The GoM version will be capable of handling datasets with sizes on the order of 10,000 observations and 30 analytic variables and will minimally operate on workstations and 486-based PCs. Such a version of GoM would be capable of analyzing many of the national survey datasets including the National Long Term Care Surveys. An intelligent support environment for the GoM model is also proposed to assist researchers in applying GoM. The support environment will include three modules: Question and Answers (Q&A), Data Preparation, and Report Analysis. Development of a prototype environment and a DOS version of GoM is proposed for Phase I.