印尼留学生汉语主动句被动句命题表征项目顺序

采用句子-图形验证任务初步探讨了不同汉语水平的印尼留学生理解汉语主动句、被动句所形成的命题表征中的项目顺序特点。结果表明,初级汉语水平印尼留学生理解汉语主动句、被动句后所建构的命题表征中项目顺序均为“受事→施事”;中级汉语水平印尼留学生理解汉语主动句所形成的命题表征项目顺序为“施事→受事”,而其理解汉语被动句所形成的命题表征项目顺序为“受事→施事”。母语主动句、被动句的理解特点影响印尼留学生汉语主动句、被动句命题表征项目顺序;这种影响作用与其汉语水平有一定的关系。     

检验项目功能差异的两类方法-CFA和IRT的比较

目前在验证性因素分析(CFA)和项目反应理论(IRT)两个领域,都有一些检验方法来识别项目功能差异(DIF)。该文主要针对单维的多级计分项目,分别介绍CFA和IRT检测DIF的方法,并进行二者的比较。     

Examination of the Japanese version of the Fear of COVID-19 Scale among Adults Using Classical Test Theory and Item Response Theory1, 2

Globally, the COVID-19 pandemic has impaired every aspect of life, especially causing much psychological damage—for instance, increasing the risk of suicide. Intense fear and anxiety are considered to play a central role in mental health problems. This study examined the psychological properties of the Japanese version of the Fear of COVID-19 Scale (FCV-19S) using classical test theory (CTT) and item response theory (IRT). Five hundred fifty participants aged 18–69 years and from across Japan completed questionnaires, including the Japanese FCV-19S, the Japanese Depression Anxiety Stress Scales-15 (DASS-15), and the Japanese version of the Kessler 6 (K6). CTT showed that each item of the Japanese FCV-19S had no ceiling and floor effect and was close to the normal distribution, and IRT revealed that each item had an appropriate parameter of discrimination and difficulty. Finally, the Japanese FCV-19S was shown to have an acceptable reliability and moderate good concurrent validity. Consequently, the Japanese FCV-19S has robust psychometric properties and can be useful for early detection of adults impacted by the COVID-19 pandemic.     

项目特征曲线等值的抽样误差

现在,等值越来越受到各考试测验机构及测量学研究人员的重视,特别是项目反应理论等值的优越性更使他们有了信心。然而,很多人却没有注意到被试能力分布形态可能给等值结果带来的影响效果及程度。本研究以项目反应理论两级记分模型的项目参数等值在不同被试能力分布形态下的结果差异作为重点,探讨被试抽样偏差可能给项目特征曲线等值带来的误差问题。研究结果表明,被试能力分布形态会显著地影响项目参数等值的系数,特别地,能力分布的偏态系数与等值方程的截距存在显著的线性相关关系,但能力分布形态的变化对等值方程中斜率的影响并不明显     

图形项目记忆与位置来源提取的ERP研究

采用事件相关电位（ERP）方法研究大学生图形项目记忆与位置来源提取新/旧效应的时、空分布特征。学习屏幕左侧（或右侧）呈现的图形后在屏幕中央呈现某一测验项目（已学图形或新图形）,对受试者进行两类测验：其一是判断该项目是否已学过;其二是将从某侧学过的测验项目判断为目标,而将其它测验项目判断为非目标的来源测验（排除任务）。实验结果表明：图形位置来源提取比项目再认新/旧效应的头皮分布更广,持续时程更长;与前人相同实验范式的研究结果相比,本研究位置来源提取效应的头皮分布范围较广;非目标-旧图形与目标图形新/旧效应的头皮激活程度不同。上述结果说明：图形位置来源提取比项目再认激活的大脑区域更多,这与双重加工模型的观点一致;实验范式和来源知觉的特性共同调节来源记忆新/旧效应的时、空分布特征;意识水平对来源信息提取的新/旧效应存在一定的影响     

基于认知理论的测验编制技术—项目生成

传统测验重视统计技术,不重视测量结构的心理学意义,使得它的功能局限于筛选,不能提供更多的信息。由于认知心理学理论、心理计量学以及计算机技术的发展,使得基于认知理论指导下的项目生成技术逐渐成熟。该文从项目生成的概念、方法,及研究的意义和难点等方面对项目生成技术作了一个简要述评,以期推进我国认知心理学与心理测量学相结合的研究工作。     

Item difficulty parameter estimation using the idea of the graded response model and computerized adaptive testing

Abstract:  In test operations using IRT (item response theory), items are included in a test before being used to rate subjects and the response data is used to estimate their item parameters. However, this method of test operation may lead to item content leakage and an adequate test operation can become difficult. To address this problem, Ozaki and Toyoda (2005, 2006 ) developed item difficulty parameter estimation methods that use paired comparison data from the perspective of the difficulty of items as judged by raters familiar with the field. In the present paper, an improved method of item difficulty parameter estimation is developed. In this new method, an item for which the difficulty parameter is to be estimated is compared with multiple items simultaneously, from the perspective of their difficulty. This is not a one-to-one comparison but a one-to-many comparison. In the comparisons, raters are informed that items selected from an item pool are ordered according to difficulty. The order will provide insight to improve the accuracy of judgment.     

Identifying Variables Responsible for Data not Missing at Random

When data are not missing at random (NMAR), maximum likelihood (ML) procedure will not generate consistent parameter estimates unless the missing data mechanism is correctly modeled. Understanding NMAR mechanism in a data set would allow one to better use the ML methodology. A survey or questionnaire may contain many items; certain items may be responsible for NMAR values in other items. The paper develops statistical procedures to identify the responsible items. By comparing ML estimates (MLE), statistics are developed to test whether the MLEs are changed when excluding items. The items that cause a significant change of the MLEs are responsible for the NMAR mechanism. Normal distribution is used for obtaining the MLEs; a sandwich-type covariance matrix is used to account for distribution violations. The class of nonnormal distributions within which the procedure is valid is provided. Both saturated and structural models are considered. Effect sizes are also defined and studied. The results indicate that more missing data in a sample does not necessarily imply more significant test statistics due to smaller effect sizes. Knowing the true population means and covariances or the parameter values in structural equation models may not make things easier either. The research was supported by NSF grant DMS04-37167, the James McKeen Cattell Fund.     

Identifiability and Equivalence of GLLIRM Models

The generalized logit–linear item response model (GLLIRM) is a linearly constrained nominal categories model (NCM) that computes the scale and intercept parameters for categories as a weighted sum of basic parameters. This paper addresses the problems of the identifiability of the basic parameters and the equivalence between different GLLIRM models. It is shown that the identifiability of the basic parameters depends on the size and rank of the coefficient matrix of the linear functions. Moreover, two models are observationally equivalent if the product of the respective coefficient matrices has full column rank. Finally, the paper also explores the relations between the parameters of nested models. I would like to express my gratitude to the editor and three anonymous reviewers for their helpful suggestions on earlier versions of the paper. This work was supported by the Comunidad de Madrid (Spain) grant: CCG07-UAM/ESP-1615.     

多级评分题计算机自适应测验选题策略比较

研究比较了多级评分题计算机化自适应测验五种选题策略的优劣。应用的IRT模型是Samejima的等级反应模型。参加比较的选题策略有难度均值与能力匹配法、难度中值与能力匹配法、信息量最大法和两种A分层法。比较指标采用了能力估计值返回真值偏差、能力估计标准差、人均用题数和试题调用次数标准差四个。研究采用蒙特卡罗模拟法,结果显示每种方法各有优劣,在分层得当情况下,A分层法(中)的综合效果最佳