Evaluating the Sampling Performance of Exploratory and Cross-Validated DETECT Procedure with Imperfect Models

Among the methods proposed for identifying the number of latent traits in multidimensional IRT models, DETECT has attracted the attention of both methodologists and applied researchers as a nonparametric counterpart to other procedures. The current study investigated the overall performance of the DETECT procedure and its outcomes using a real-data sampling design recommended by MacCallum (2003 MacCallum, R.C. (2003). Working with imperfect models. Multivariate Behavioral Research, 38 (1), 113–139. http://dx.doi.org/10.1207/S15327906MBR3801_5[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) and compared the results from a purely simulated data set that was generated with a well-specified “perfect” model. The comparison revealed that the sampling behavior of the maximized DETECT value and R-ratio statistics was quite robust to minor factors and other model misspecifications that potentially exist in the real data set, as there were negligible differences between the results of the real and simulated data sets. Item classification accuracy was also nearly identical for the real and simulated data sets. The accuracy of the identified number of dimensions reported by DETECT was the only outcome with an obvious difference between the purely simulated data set and the real data set. While the difference was small for smaller sample sizes, the identified number of dimensions was more accurate for larger sample sizes when the population data set was purely simulated. In many instances, exploratory DETECT analysis outperformed the cross-validated DETECT analysis in terms of overall accuracy.     

非词复述测验的编制和初步分析

本研究编制了一种用于测量儿童语音工作记忆的测验———非词复述测验,并在48名四年级小学生中初步进行信度、效度检验和项目分析。结果表明:(1)该测验有较高的重测信度;(2)该测验具有较高的结构效度和效标效度;(3)分测验二的项目难度分布比较合理,多数项目鉴别力较高,而分测验一的项目难度分布和项目鉴别力则有待于在今后的研究中进一步提高。     

多级评分聚类诊断法的影响因素

从测验和被试两个层面探讨了属性数目、属性层级关系、被试知识状态分布、属性层级误设和Q矩阵误设等因素对GRCDM的影响, 以进一步考察GRCDM的特性。研究发现：(1)GRCDM对属性数目无依赖, 随属性数目的增多判准率反而增高; (2)被试知识状态分布对GRCDM判准率高低无影响; (3)属性层级误设对GRCDM的影响与属性层级类型有关, 当属性层级为无结构型和发散型时, “属性层级关系错乱”的判准率降幅最大; (4)Q矩阵误设对GRCDM的影响因层级关系而异, 收敛型和发散型受影响较小, 无结构型和线型的判准率在属性既冗余又缺失时降幅最大。     

Testing for DIF in a model with single peaked item characteristic curves: The parella model

The PARELLA model is a probabilistic parallelogram model that can be used for the measurement of latent attitudes or latent preferences. The data analyzed are the dichotomous responses of persons to items, with a one (zero) indicating agreement (disagreement) with the content of the item. The model provides a unidimensional representation of persons and items. The response probabilities are a function of the distance between person and item: the smaller the distance, the larger the probability that a person will agree with the content of the item. This paper discusses how the approach to differential item functioning presented by Thissen, Steinberg, and Wainer can be implemented for the PARELLA model. Requests for the PARELLA software should be sent to Iec Progamma PO Box 841, 9700 AV Groningen, The Netherlands.     

多维对数正态作答时间模型：对潜在加工速度多维性的探究

在心理与教育测量中,潜在加工速度反映学生运用潜在能力解决问题的效率。为在多维测验中探究潜在加工速度的多维性并实现参数估计,本研究提出多维对数正态作答时间模型。实证数据分析及模拟研究结果表明:(1)潜在加工速度具有与潜在能力相匹配的多维结构;(2)新模型可精确估计个体水平的多维潜在加工速度及与作答时间有关的题目参数;(3)冗余指定潜在加工速度具有多维性带来的负面影响低于忽略其多维性所带来的。     

传染病大暴发期间的临床研究：理论合理性和实践可行性

试图从治疗和研究在伦理基础及政策导向上的差异入手,分析在流行病大暴发期间扩大临床试验性治疗和缩短研究进程等策略的不足,指出其局限性及不可行性;阐明知情同意的伦理原则和预警的监管原则是衡量临床研究规范化和安全性的基准。论文得出结论,阶梯式群试验是流行病大暴发期间较为合宜的研究性治疗策略,它可以确保知情同意的伦理原则和预警的监管原则在不动摇的前提下,使研究性治疗及时为患者所得。     

双因子模型MCAT中多级评分项目选题策略的比较

双因子模型假设测验考察一个一般因子和多个组因子,符合很多教育和心理测验的因素结构。“维度缩减”方法将参数估计中多维积分计算化简为多个迭代二维积分,是双因子模型的重要特征。本文针对考察多级评分项目的计算机化自适应测验,首先推导双因子等级反应模型下Fisher信息量的计算,然后推导“维度缩减”方法在项目选择方法中的应用,最后在低、中、高双因子模式题库中比较D-优化方法、后验加权Fisher信息D优化方法(PDO)、后验加权Kullback-Leibler方法(PKL)、连续熵(CEM)和互信息(MI)方法在能力估计的相关、均方根误差、绝对值偏差和欧氏距离的表现。模拟研究表明：(1)双因子模式越强,即一般因子和组因子在项目上的区分度的差异越小,一般因子估计精度降低,组因子估计精度增加,整体能力的估计精度提高;(2)相同实验条件下,连续熵方法的测量精度最高,PKL方法的能力估计精度最低,其它方法的测量精度没有显著差异。     

计算机化多维测验中作答时间和作答精度数据的联合分析

随着心理与教育测量研究的发展和科技的进步,计算机化(大规模)测验逐渐受到人们的关注。为探究在计算机化多维测验中如何利用作答时间数据来辅助评估多维潜在能力,以及为我国义务教育阶段教育质量监测提供数据分析方法上的理论支持。本研究以2012年和2015年国际学生能力评估(PISA)计算机化数学测验数据为例,提出了一种可同时利用作答时间和作答精度数据的联合作答与时间的多维Rasch模型。根据新模型对PISA数据的分析结果,表明引入作答时间数据,不仅有助于提高模型参数的估计精度,还有助于数据分析者利用被试的作答时间信息来做进一步的决策和干预(e.g., 对异常作答行为或预备知识的诊断)。     

Exploring a Source of Uneven Score Equity across the Test Score Range

Score equity assessment (SEA) refers to an examination of population invariance of equating across two or more subpopulations of test examinees. Previous SEA studies have shown that score equity may be present for examinees scoring at particular test score ranges but absent for examinees scoring at other score ranges. No studies to date have performed research for the purpose of understanding why score equity can be inconsistent across the score range of some tests. The purpose of this study is to explore a source of uneven subpopulation score equity across the score range of a test. It is hypothesized that the difficulty of anchor items displaying differential item functioning (DIF) is directly related to the score location at which issues of score inequity are observed. The simulation study supports the hypothesis that the difficulty of DIF items has a systematic impact on the uneven nature of conditional score equity.     

眼动追踪在价值导向元记忆研究中的应用

价值导向元记忆关注人们在面对不同重要性信息时,通过元记忆监测和调节,有选择地优先加工高价值信息,以实现记忆效率最大化的目的。价值导向元记忆包括价值导向元记忆监测和控制,眼动追踪技术以其无干扰性、生态效度高等优势可以时时追踪这一监控过程。当前该领域研究中已采用的眼动指标集中在项目选择、学习时间分配、学习进程等方面。未来在项目选择、学习效率和策略比较等研究中可以探索眼动追踪技术的进一步应用。