Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima

Cluster differences scaling is a method for partitioning a set of objects into classes and simultaneously finding a low-dimensional spatial representation ofK cluster points, to model a given square table of dissimilarities amongn stimuli or objects. The least squares loss function of cluster differences scaling, originally defined only on the residuals of pairs of objects that are allocated to different clusters, is extended with a loss component for pairs that are allocated to the same cluster. It is shown that this extension makes the method equivalent to multidimensional scaling with cluster constraints on the coordinates. A decomposition of the sum of squared dissimilarities into contributions from several sources of variation is described, including the appropriate degrees of freedom for each source. After developing a convergent algorithm for fitting the cluster differences model, it is argued that the individual objects and the cluster locations can be jointly displayed in a configuration obtained as a by-product of the optimization. Finally, the paper introduces a fuzzy version of the loss function, which can be used in a successive approximation strategy for avoiding local minima. A simulation study demonstrates that this strategy significantly outperforms two other well-known initialization strategies, and that it has a success rate of 92 out of 100 in attaining the global minimum.     

华人人际关系的概念化——针对中国香港大学生的实证研究

以杨中芳所构建的华人人际关系概念化理论模型为蓝本,并用以往的实证研究方法为基础,揭示了当代华人对人际关系如何进行概念化及其潜在的认知维度。杨氏模型认为华人人际关系主要由既定成分、情感成分和工具成分组成。该研究以中国香港大学生为被试进行调查,首先采用访谈的形式,分家族、学校及日常生活情境建立人际关系清单。然后采用多维量法（Multi-dimensional scaling）处理被试对关系进行分类的数据。结果发现,在不同的生活情境中,被试均依据情感程度对关系进行分类。在等级差异明显的家族和学校情境中,被试依据辈份和等级差异的突显性进行区分;而在等级差异小的生活情境中,则采用功利程度为标准。本文对被试在不同情境中所采用的不同维度进行了讨论,并提出华人人际关系的义务具有工具性质的实证结果     

情境模型研究中的分离与整合

情境模型概念出现后,有关情境模型研究重点解决以下两大问题：其一,情境模型不是课文基础表征;其二,情境模型具有多维结构。前者表现为情境模型与课文基础表征的分离研究,后者表现为情境模型从单维向多维的整合研究。在此基础上,研究者进一步统合情境模型研究,初步考察情境模型的实质问题。基于情境模型研究的分离与整合的趋势,情境模型的未来研究将进一步发展研究范式,深入探讨“情境模型是什么”与“情境模型的加工机制是怎么样”等问题。     

两种新的多维计算机化分类测验终止规则

计算机化分类测验(Computerized Classification Testing, CCT)由于具备分类的功能, 目前在职业资格考试、健康与护理问卷等以分类为目的的测验中得到广泛应用。作为CCT的重要组成部分, 终止规则不仅决定测验停止的条件而且直接影响分类准确率及测验效率。然而, 目前少有研究对多维CCT (Mulitidimensional CCT, MCCT)的终止规则进行探索。针对已有MCCT终止规则的不足, 提出两种新的MCCT终止规则(即基于马氏距离的多维序贯似然比规则Mahalanobis-SPRT和随机缩减的多维广义似然比规则M-SCGLR), 并开展模拟研究在不同实验条件下(比如, 不同的题库结构、能力维度间相关及分界函数)考查它们的表现。结果表明：(1)在使用补偿性分界函数的条件下, Mahalanobis-SPRT规则具有较高的分类精度和与同类方法相近的测验长度; (2)在几乎所有实验条件下, M-SCGLR规则不仅在测验精度上大幅优于已有的多维随机缩减规则, 而且具有较短的测验长度。     

多维题组效应认知诊断模型

当前认知诊断领域还缺少对包含题组的测验进行诊断分析的研究, 即已开发的认知诊断模型无法合理有效地处理含有题组效应的测验数据, 且已开发的题组反应模型也不具有对被试知识结构或认知过程进行诊断的功能。针对该问题, 本文尝试性地将多维题组效应向量参数引入线性Logistic模型中, 同时开发了属性间具有补偿作用的和属性间具有非补偿作用的多维题组效应认知诊断模型。模拟研究结果显示新模型合理有效, 与线性Logistic模型和DINA模型对比研究后表明：(1)作答数据含有题组效应时, 忽略题组效应会导致项目参数的偏差估计并降低对目标属性的判准率; (2)新模型更具普适性, 即便当作答数据不存在题组效应时, 采用新模型进行测验分析亦能得到很好的项目参数估计结果且不影响对目标属性的判准率。整体来看, 新模型既具有认知诊断功能又可有效处理题组效应。     

测验链接中的锚题代表性研究

本文旨在以“锚题代表性”这一研究命题切入,探索在非等组锚测验设计下,作为实现测验链接的重要载体,锚题和相关的测验试卷/水平之间究竟应该有什么关系。本文首先指出锚题代表性这一概念在等值和垂直量尺化领域中具有不同的含义,并给出其在垂直量尺化中的含义。通过考察测验链接中有关锚题代表性的既有研究,系统总结相关研究成果,本文概括出了当前锚题构建实践的可能优化方案,分析了锚题代表性研究的未来方向。     

Perhaps Unidimensional Is Not Unidimensional

Miller (1956) identified his famous limit of 7 ± 2 items based in part on absolute identification—the ability to identify stimuli that differ on a single physical dimension, such as lines of different length. An important aspect of this limit is its independence from perceptual effects and its application across all stimulus types. Recent research, however, has identified several exceptions. We investigate an explanation for these results that reconciles them with Miller’s work. We find support for the hypothesis that the exceptional stimulus types have more complex psychological representations, which can therefore support better identification. Our investigation uses data sets with thousands of observations for each participant, which allows the application of a new technique for identifying psychological representations: the structural forms algorithm of Kemp and Tenenbaum (2008) . This algorithm supports inferences not possible with previous techniques, such as multidimensional scaling.     

Item format and the structure of the buss-durkee hostility inventory

The Buss-Durkee Hostility Inventory is a widely employed multidimensional measure of aggression. Two studies, each involving the administration of both two-choice and seven-choice response format versions of the instrument, were conducted to determine if (1) the theoretical scales could be reproduced empirically, (2) the change in response format either changes or improves the structure, and (3) the structure of either response format replicates across administrations. The two-choice version provided some support for the present theoretical scoring but was not very stable across administrations. The seven-choice version resulted in a structure that was different from both the two-choice structure and theoretical scoring but was more stable across administrations.