SOFTWARE FOR A RANDOM EFFECTS INDSCAL MODEL

在此，我们提出了多维尺度的方法论研究 针对计划说明中的研究主题108.c的方法 为了NIMH。多维尺度(MDS)是一种心理测量学方法， 在一般行为科学研究中的广泛应用。我们建议 为新一代个人研究和开发软件 差异MDS(INDSCAL)模型。在这些新模型中，参数 与个体相关联的模型被建模为随机效果而不是 固定参数。对于INDSCAL模型，这些参数是 受试者权重。由此产生的随机效应MDS模型有许多 与经典的同类产品相比，具有更大的优势。例如，我们能够 计算所有受试者权重的单独估计，即使只有一个 不同的个体被观察到，我们能够直接 将我们的结果用于对受试者抽样总体的推断 重量。我们建议研究用于计算的计算算法 估计在这类新的MDS模型中，并开发模型构建 以及适用于该方法的其他统计技术。也是 重要的是在第一阶段展示了这些新产品的优势 通过使用第一阶段开发的原型软件建立模型 分析真实数据集。 建议的商业应用： 多维测量是市场上广泛使用的一种心理测量方法。 研究、心理学、社会科学、遗传学、化学计量学和 行为和科学研究的其他领域。建议的模型 与现有技术相比具有显著的优势。一个设计精良的 和用于实施这些模型的综合模块将找到一个现成的 市场。

Here we propose methodological research on multidimsional scaling methods in response to research topic 108.c in the program description for NIMH. Multidimensional scaling (MDS) is a psychometric method with wide application in general behavioral science research. We propose to investigate and develop software for a new class of individual differences MDS (INDSCAL) models. In these new models parameters associated with individuals are modeled as random effects rather than as fixed parameters. For the INDSCAL models, these parameters are the subject weights. The resulting random effects MDS model has many advantages over its classical counterpart. For example, we are able to compute individual estimates of all subject weights even when only one dissimilarity is observed on the individual, and we are able to directly use our results in inferences about the sampled population of subject weights. We propose to study computational algorithms for computing estimates in this new class of MDS models, and to develop model building and other statistical techniques appropriate for the method. Also important during Phase I is demonstrating the advantages of these new models by using the prototypical software developed in Phase I to analyze real data sets. PROPOSED COMMERCIAL APPLICATION: Multidimensional scaling is a psychometric method widely used in market research, psychology, the social sciences, genetics, chemometrics, and other areas of behavioral and scientific research. The proposed models have significant advantages over existing techniques. A well designed and comprehensive module for implementing these models will find a ready market.