GRADE OF MEMBERSHIP MODELS IN AGING STUDIES

此应用程序寻求对统计数据的调查提供支持 目前广泛使用的隶属度(GOM)模型的性质 用于分析#年健康状况和残疾调查数据 老年人。使用这些模型对分析结果的解释 以及更广泛的老年学家用户社区对模型的采用， 人口学家和统计学家一直受到缺乏 可视的、经过充分分析的例子。这项研究项目的重点是 关于所谓的“有条件的”GOM模型 个人调查受访者--这些是GOM模型的形式 通常在应用程序中使用。该项目的重点是GOM 提供数据集的2维或3维表示的模型 从3到8个二分变量开始。这些低维的案例 允许进行严格的分析和几何表示，提供了 的不同可能行为的示例 模特们。该项目将继续进行稳健性研究和 GOM模型对违反假设的敏感性，工作 来自人工构建的数据集，基于 提前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.