GRADE OF MEMBERSHIP MODELS IN AGING STUDIES

本申请寻求对统计调查的支持， 隶属度等级（GoM）模型的性质现在广泛使用， 用于分析健康状况和残疾调查数据， 老人使用这些模型对分析结果进行解释 以及更广泛的老年病学家用户社区采用这些模型， 人口统计学家和统计学家一直受到缺乏 可视化的，充分分析的例子。 该研究项目重点 在所谓的"条件" GoM模型上， 个体调查受访者--这些是GoM模型的形式 通常用于应用程序中。该项目集中在GoM 提供数据集的二维或三维表示的模型 从3到8个二分变量开始。这些低维情况 允许严格的分析和几何表示，提供了一个 的不同可能行为的例子库 模型 该项目将继续研究其稳健性， GoM模型对违反假设的敏感性， 从人工构建的数据集的基础上的变量， AHEAD调查和其他关于老年人口的全国性调查。

This application seeks support for an investigation of the statistical properties of the Grade of Membership (GoM) models now in widespread use for the analysis of survey data on health status and disability for the elderly. Interpretation of results from analyses using these models and adoption of the models by a wider user community of gerontologists, demographers, and statisticians have been hindered by a lack of visualizable, fully-analyzed examples. This research project focuses on so-called "conditional" GoM models which generate "GoM" scores" for individual survey respondents -- these are the form of GoM models commonly employed in applications. The project concentrates on GoM models which supply 2 or 3-dimensional representations of data sets starting with 3 to 8 dichotomous variables. These low-dimensional cases permit both rigorous analysis and geometric presentation, supplying a repertory of examples of the different possible behaviors of the models. The project will proceed to a study of the robustness and sensitivity of GoM models to violations of the assumptions, working from artificially-constructed data sets based on variables from the AHEAD Survey and other national surveys on the elderly population.