MIXED EFFECTS MULTIDIMENSIONAL SCALING

混合效应多维尺度

• 批准号: 2776571
• 负责人: DOUGLAS B CLARKSON
• 金额: $ 25.07万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-01 至 2000-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2776571
• 关键词: artificial intelligence; behavioral /social science research tag; computer graphics /printing; computer program /software; computer system design /evaluation; mathematical model; model design /development; psychometrics; statistics /biometry

Multidimensional scaling (MDS) is a psychometric method with wide application in behavioral science research. We propose to develop software for a new class of MDS models. In these new models parameters associated with individuals are modeled as random effects rather than as fixed parameters. For the diagonal metric (or 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 better able to estimate subject weights even when only one dissimilarity is observed on an individual, and we can make model-based inferences about the sampled population of subject weights. We propose to develop a comprehensive module of computational algorithms for computing estimates in this new class of MDS models. Included in this module will be software for model fitting, inference, diagnostics, and other appropriate statistical techniques, a graphical user interface, a users manual, and online documentation. The software will also contain procedures for robust estimation. PROPOSED COMMERCIAL APPLICATIONS: Multidimensional scaling is a psychometric method widely used in market research, psychology, sociology, political science, 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.

多维量表(MDS)是一种具有广泛应用前景的心理测量方法 在行为科学研究中的应用。我们建议开发 用于新一类MDS型号的软件。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。对于对角度量(或INDSCAL)模型，这些 参数是主题权重。由此产生的随机效果MDS 与经典的对应模型相比，模型具有许多优势。例如, 我们能够更好地估计受试者的权重，即使只有一个 在个体上观察到不同之处，我们可以基于模型 关于抽样总体的受试者权重的推断。 我们建议开发一个全面的计算算法模块 用于计算这类新的MDS模型的估计值。包括在此 模块将是用于模型拟合、推理、诊断和 其他适当的统计技术、图形用户界面、 用户手册和在线文档。该软件还将包含 稳健估计的程序。 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，社会学，政治学，遗传学， 化学计量学，以及其他行为和科学研究领域。这个 与现有技术相比，所提出的模型具有显著的优势。一个 用于实施这些模式的精心设计和全面的模块将 找到一个现成的市场。