Meulders Michel, Ip Edward H, De Boeck Paul
Department of Psychology, University of Leuven, Belgium.
Br J Math Stat Psychol. 2005 May;58(Pt 1):117-43. doi: 10.1348/000711005X38555.
A general framework is presented for the analysis of partially ordered set (poset) data. The work is motivated by the need to analyse poset data such as multi-componential responses in psychological measurement and partially accomplished cognitive tasks in educational measurement. It is shown how the generalized loglinear model can be used to represent poset data that form a lattice and how latent-variable models can be constructed by further specifying the canonical parameters of the loglinear representation. The approach generalizes a class of latent-variable models for completely ordered data. We apply the methods to analyse data on the frequency and intensity of anger-related feelings. Furthermore, we propose a trajectory analysis to gain insight into the response function of partially ordered emotional states.
本文提出了一个用于分析偏序集（poset）数据的通用框架。开展这项工作的动机是需要分析偏序集数据，例如心理测量中的多成分反应以及教育测量中部分完成的认知任务。文中展示了广义对数线性模型如何用于表示形成格的偏序集数据，以及如何通过进一步指定对数线性表示的规范参数来构建潜在变量模型。该方法推广了一类用于完全有序数据的潜在变量模型。我们应用这些方法来分析与愤怒相关情绪的频率和强度数据。此外，我们提出了一种轨迹分析方法，以深入了解部分有序情绪状态的反应函数。
