Clarifying the Relationship Between Ostracism and Relational Devaluation

We examine how three perspectives on relational devaluation relate to needs threat following ostracism. In two experiments with 179 first-year psychology students, distress was greatest when participants were ostracized without any prior throws, and distress decreased linearly with increasing prior inclusion. In Experiment 3, using 76 first-year psychology students, we manipulated expectations of exclusion and found expectations predicted distress following ostracism, suggesting ostracism's distress can be influenced by norm-based expectations of inclusion, and that progressive relational devaluation is not a necessary condition for ostracism's distress.     

帕金森病诊断标记物系统回顾与展望

本篇综述总结了帕金森病的诊断、鉴别诊断和病程监测的各领域的生物学标记物的研究进展。其中重点介绍了神经影像学和生物化学领域的生物学标记物,也是当前研究较为成熟的两大领域。神经影像学标记物以多巴胺能影像及非多巴胺能影像为代表,生物化学标记物为包括a一突触核蛋白在内的多种蛋白和激素,两者的结合能够有效提高疾病诊断的特异性和灵敏性。同时文章还介绍了当前研究的热点——帕金森病的前驱诊断手段,即在疾病典型的运动症状和非运动症状出现前便可预测帕金森病的诊断手段。     

5～9岁儿童在不同复杂性任务上类比推理的发展特点

采用经典的A:B:C:D范式,设计两类难度不同的图片任务,依据关系-表征复杂性模型从等级复杂性和水平复杂性角度分析任务难度.在此基础上,选取5～9岁115名儿童探讨两类任务上类比推理的特点和发展趋势.表明:(1)儿童类比推理呈现多样化特点,主要有:充分意义上掌握了类比推理、部分完成了类比推理、位置关系类比推理、不能类比推理和不确定情况类比推理,其中部分完成了类比推理是复杂任务上所特有的.(2)简单、复杂两类任务上,随着年龄增长,5-9岁儿童充分意义上掌握类比推理的人次比例不断上升,不能类比推理的人次比例不断下降.此外,复杂任务上,随年龄增长,部分完成类比任务的人次比例降低.结果表明,在简单任务上,前运算阶段儿童已具备了类比推理能力;但在复杂任务上,儿童到了具体运算阶段才具备类比推理能力.     

经皮肺活检对肺部病变的诊断价值

经皮肺活检技术应用已有100多年历史。随着影像技术的不断进步,目前已发展成为肺部疾病的重要诊断技术。本文综述了经皮肺活检对肺部病变的诊断价值。并探讨和比较了不同引导技术的特点,以及适应症、并发症及其处理。     

An empirical Q-matrix validation method for the sequential generalized DINA model

As a core component of most cognitive diagnosis models, the Q-matrix, or item and attribute association matrix, is typically developed by domain experts, and tends to be subjective. It is critical to validate the Q-matrix empirically because a misspecified Q-matrix could result in erroneous attribute estimation. Most existing Q-matrix validation procedures are developed for dichotomous responses. However, in this paper, we propose a method to empirically detect and correct the misspecifications in the Q-matrix for graded response data based on the sequential generalized deterministic inputs, noisy ‘and’ gate (G-DINA) model. The proposed Q-matrix validation procedure is implemented in a stepwise manner based on the Wald test and an effect size measure. The feasibility of the proposed method is examined using simulation studies. Also, a set of data from the Trends in International Mathematics and Science Study (TIMSS) 2011 mathematics assessment is analysed for illustration.     

一种非参数化的Q矩阵估计方法：ICC-IR方法开发

摘要：相对于参数化的方法,本研究根据题目测量模式关系开发出ICC指标,并提出基于理想得分的ICC指标法进行Q矩阵估计。Monte Carlo模拟研究与实证研究发现（1）基于理想得分ICC指标法估计Q矩阵具有很好的效果,当属性个数越少、基础题个数越多,估计效果越好。（2）相对于以往方法——D2统计量的方法,ICC-IR法效果更好,并且是一种非参数化的方法,计算简单快捷。（3）实证数据分析表明,ICC-IR法估计的Q矩阵在模型拟合度上也优于D2统计量方法。     

Cognitive diagnosis modelling incorporating item response times

To provide more refined diagnostic feedback with collateral information in item response times (RTs), this study proposed joint modelling of attributes and response speed using item responses and RTs simultaneously for cognitive diagnosis. For illustration, an extended deterministic input, noisy ‘and’ gate (DINA) model was proposed for joint modelling of responses and RTs. Model parameter estimation was explored using the Bayesian Markov chain Monte Carlo (MCMC) method. The PISA 2012 computer-based mathematics data were analysed first. These real data estimates were treated as true values in a subsequent simulation study. A follow-up simulation study with ideal testing conditions was conducted as well to further evaluate model parameter recovery. The results indicated that model parameters could be well recovered using the MCMC approach. Further, incorporating RTs into the DINA model would improve attribute and profile correct classification rates and result in more accurate and precise estimation of the model parameters.     

Two-Stage maximum likelihood estimation in the misspecified restricted latent class model

The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.