SUPPORT ENVIRONMENT FOR GRADE OF MEMBERSHIP (GOM) MODEL

隶属度（GoM）是一种多元分析技术， 分析高维离散响应数据， 分布假设。 GoM是作为一种研究工具开发的， 杜克大学在过去的10年里， 并评估蒙古国政府的统计基础。 几乎没有任何努力 旨在制作一份完整的墨西哥政府文件。 因此， 存在具有不同能力的GoM版本。 第一个项目任务 将确定一个适当的墨西哥政府研究版本， 增强并记录，以便分发给NIA和NIH研究人员。 GoM版本将能够处理大小在 10，000个观测值和30个分析变量， 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。 本文还提出了一个GoM模型的智能支持环境， 协助研究人员应用GoM。 支持环境将包括 三个模块：问答（Q & A）、数据准备和报告 分析. 开发GoM的原型环境和DOS版本 拟用于第一阶段。

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.