非参数认知诊断方法下诊断结果的概率化表征

非参数认知诊断分类方法非常适合课堂评估,其诊断结果采用0-1形式而缺乏概率化表征,不能精细地区分被试属性掌握程度的差异或变化,还缺乏可用于评价真实测验分类结果的信度和效度指标。要刻画被试属性掌握程度的差异,首要的问题是要为非参数认知诊断方法提供一种可以量化属性掌握概率的方法。针对此问题,基于二项分布和玻尔兹曼分布提出非参数认知诊断方法下诊断结果的概率化表征方法,并用于构建分类准确性和分类一致性指标。模拟研究与实测数据分析结果显示：概率化表征方法与非参数认知诊断方法的分类结果高度一致;概率化表征方法与认知诊断模型所得的属性掌握概率十分接近;概率化表征方法所得的属性（模式）掌握概率可用于计算属性（模式）分类准确性和分类一致性指标,在实际测验情景下可作为信度和效度指标,评价诊断结果的重测一致率和判准率。     

基于CAT的在线标定:设计与方法

项目增补是题库建设和维护的重要手段, 而标定新题参数是项目增补的重要内容。在线标定设计和在线标定方法分别研究新题的施测方式和参数估计方法, 是计算机化自适应测验(computerized adaptive testing, CAT)情景下项目增补的核心技术。重点厘清在线标定设计与在线标定方法的发展思路和脉络, 并对它们的特点、联系和表现进行介绍和评价。未来应基于其他信息指标进一步研究在线标定设计, 可基于联合估计和误差校正的思路探究在线标定方法, 应加强研究认知诊断CAT和多维CAT的在线标定技术, 深入开展项目增补方法的实证研究。     

Effects of choice and relative frequency elicitation on overconfidence: further tests of an exemplar‐retrieval model

An experiment is reported in which participants rendered judgments regarding the disease states of hypothetical patients. Participants either reported likelihoods that patients had the target disease (no choice), or classified patients into disease categories and then reported likelihoods that their classifications were correct (choice included). Also, participants' likelihood judgments were made in response to either a probability probe question, or a relative frequency probe. Two distinct exemplar‐memory models were compared on their ability to predict overconfidence under these procedures. Both propose that people learn and judge by storing and retrieving examples. The exemplar retrieval model (ERM) proposes that amount of retrieval drives choice inclusion and likelihood probe effects. The alternative model assumes that response error mediates choice inclusion effects. Choice inclusion and the relative frequency probe reduced overconfidence, but the combined effects were subadditive. Only the ERM predicted this pattern, and it further provided good quantitative fits to these results. Copyright © 2003 John Wiley & Sons, Ltd.     

心理咨询问题的三层面分类

对心理咨询面对的来访者问题从社会学、心理学和医学三个层面进行初步界定与分类,形成一个心理咨询问题的三层面分类系统。三层面分类系统包括三个层面三个等级,为来访者问题的可能原因提供多维的思考角度。重点探讨了社会层面分类,根据社会生活事件性质及其与当事人的关系分为五个方面。     

DSM-IV Literature reviews: Rationale,process, and limitations

DSM-IV will benefit from the extensive research and interest in diagnosis generated in large part by DSM-III. The development of DSM-IV is proceeding through three stages of empirical documentation, including literature reviews, analysis of unpublished data sets, and field trials. This paper discusses the rationale, process, and limitations of the literature reviews. The purpose of the literature reviews is to provide and to document a comprehensive review of the clinical and empirical literature to facilitate objective and empirically based decisions. The review process is guided by the explicit and systematic methods developed within meta-analysis. Limitations of the reviews are also discussed.The opinions expressed in this paper are those of the authors and do not necessarily represent the position of the American Psychiatric Association and its Task Force on DSM-IV.     

谱聚类算法在不同属性层级结构诊断评估中的应用

聚类分析已成功用于认知诊断评估(CDA)中,使用广泛的聚类分析方法为K-means算法,有研究已证明K-means在CDA中具有较好的聚类效果。而谱聚类算法通常比K-means分类效果更佳,本研究将谱聚类算法引进CDA,探讨了属性层级结构、属性个数、样本量和失误率对该方法的影响。研究发现：（1）谱聚类算法要比K-means提供更好的聚类结果,尤其在实验条件较苛刻时,谱聚类算法更加稳健;（2）线型结构聚类效果最好,收敛型和发散型相近,独立型结构表现较差;（3）属性个数和失误率增加后,聚类效果会下降;（4）样本量增加后,聚类效果有所提升,但K-means方法有时会有反向结果出现。     

多级评分认知诊断模型述评

认知诊断评估旨在探讨个体内部的知识掌握结构,并提供关于学生优缺点的详细诊断信息,以促进个体的全面发展。当前研究者已开发了大量0-1评分的认知诊断模型,但对于多级评分认知诊断模型的研究还比较少。本文对已有的多级评分认知诊断模型进行了归纳,介绍了模型的假设,计量特征以及适用范围,为实际应用者和研究者在多级评分认知诊断模型的比较和选用上提供借鉴和参考。最后,对未来关于多级评分诊断模型的研究方向进行了展望。     

关于糖尿病的几点认识

糖尿病及其并发症严重影响了人类的健康。随着科学技术的不断发展,人们对糖尿病 的认识逐步加深。目前关于糖尿病的命名、诊断分型标准和治疗原则在临床工作中逐渐显露 出其不足之处,这势必会影响人们对于糖尿病的进一步研究。本文从糖尿病的命名、诊断、 分型、治疗和糖尿病研究等方面发表了作者的看法,旨在引起大家的思考。     

Clinicians' Perspectives on Negative Effects of Psychological Treatments

Negative effects of psychological treatments is a fairly unexplored area of clinical research. Previous investigations have indicated that a portion of all patients experience negative effects in terms of deterioration and various adverse events. Meanwhile, evidence suggests that many clinicians are untrained in identifying negative effects and unaware of the current research findings. The objective of the current study is thus to investigate clinicians' own perspectives and experiences of possible negative effects of psychological treatments. An invitation to participate in an anonymous online survey consisting of 14 open-ended questions was distributed via three mailing lists used by clinicians that primarily identify themselves as cognitive behavior therapists. The responses were analyzed using a qualitative method based on thematic analysis. In total, 74 participants completed the survey. A majority agreed that negative effects of psychological treatments exist and pose a problem, and many reported having experienced both deterioration and adverse events among patients in their own practice. The thematic analysis resulted in three core themes: characteristics of negative effects, causal factors, as well as methods and criteria for evaluating negative effects. The clinicians recognize that negative effects exist, but many are unaware of the current research findings and are unfamiliar with methods and criteria for identifying and preventing deterioration and adverse events. The results provide evidence for further dissemination of the present knowledge regarding negative effects, particularly during basic clinical training, as well as the need for raising awareness of the available methods for identifying and preventing negative effects.     

产品类别与网上购物决策过程的关系

产品的分类方法有很多,在网络购物环境中,应用较广的是将产品分为搜索产品、体验产品和信任产品;在传统购物环境中,应用较广的是将产品分为日用品、选购品和特殊产品。文献的研究结果表明,消费者在网上购买不同类别的产品时,网上购物决策过程中的信息搜索、网上购物意愿和偏好等行为存在着许多不同之处,文章对此进行了详细的介绍,并对今后的研究方向提出了建议     

A general proof of consistency of heuristic classification for cognitive diagnosis models

The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633–665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non‐parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non‐parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.     

Heuristic cognitive diagnosis when the Q‐matrix is unknown

Cognitive diagnosis models of educational test performance rely on a binary Q‐matrix that specifies the associations between individual test items and the cognitive attributes (skills) required to answer those items correctly. Current methods for fitting cognitive diagnosis models to educational test data and assigning examinees to proficiency classes are based on parametric estimation methods such as expectation maximization (EM) and Markov chain Monte Carlo (MCMC) that frequently encounter difficulties in practical applications. In response to these difficulties, non‐parametric classification techniques (cluster analysis) have been proposed as heuristic alternatives to parametric procedures. These non‐parametric classification techniques first aggregate each examinee's test item scores into a profile of attribute sum scores, which then serve as the basis for clustering examinees into proficiency classes. Like the parametric procedures, the non‐parametric classification techniques require that the Q‐matrix underlying a given test be known. Unfortunately, in practice, the Q‐matrix for most tests is not known and must be estimated to specify the associations between items and attributes, risking a misspecified Q‐matrix that may then result in the incorrect classification of examinees. This paper demonstrates that clustering examinees into proficiency classes based on their item scores rather than on their attribute sum‐score profiles does not require knowledge of the Q‐matrix, and results in a more accurate classification of examinees.     

一种高效的CD-CAT在线标定新方法：基于熵的信息增益与EM视角

项目增补(Item Replenishing)对认知诊断计算机自适应测验(CD-CAT)题库的维护有着至关重要的作用, 而在线标定是一种重要的项目增补方式。基于数据挖掘中特征选择(Feature Selection)的思路, 提出一种高效的基于熵的信息增益的在线标定方法(记为IGEOCM), 该方法利用被试在新旧题上的作答联合估计新题的Q矩阵和项目参数。研究采用Monte Carlo模拟实验验证所开发新方法的效果, 并同时与已有的在线标定方法SIE、SIE-R-BIC和RMSEA-N进行比较。结果表明：新开发的IGEOCM在各实验条件下均具有较好的项目标定精度和项目估计效率, 且整体上优于已有的SIE等方法; 同时, IGEOCM标定新题所需的时间低于SIE等方法。总之, 研究为CD-CAT题库中项目的增补提供了一种更为高效、准确的方法。     

肿瘤临床治疗中应关注肝脏

抗肿瘤细胞毒性药物有不同程度毒副作用,有些严重毒副反应是限制药物剂量或使用的直接原因。肝脏是药物代谢的主要器官,也是药物损伤的主要靶器官。随着临床肿瘤治疗关注治疗带来的副作用与关注疗效同等重要的发展,本文对抗肿瘤细胞毒性药物与肝损伤、发生机制、诊断、分型和临床治疗进展进行了阐述。     

Meta-diagnosis: Towards a hermeneutical perspective in medicine with an emphasis on alcoholism

This essay argues that making a diagnosis in medicine is essentially a hermeneutic enterprise, one in which interpretation skills play a major part in understanding a disease. The clinical encounter is an event comprised of two voices; one is the voice of science which is grounded in empiricism, the other is that of human experience, which is grounded in story-telling and the interpretation of those stories.Using two voices, one from the Diagnostic and Statistical Manual of Mental Disorders-III-Revised, which describes alcohol abuse and alcohol dependence, and the other, that of Claire, a character in Edward Albee's play, A Delicate Balance, who is conversing with her brother-in-law, Tobias, I apply principles from Hans-Georg Gadamer's hermeneutics to the clinical diagnostic process. The essay will demonstrate that we overlook an enormous amount of information about alcoholism by an overreliance on objective data and that our hope for understanding alcoholics is in listening to their voices, and sharing the interpretation of their experiences with them.     

治疼必需知痛

疼痛是人体可感知所有伤害性刺激中最难以忍受的恶性刺激,医学范畴下的疼痛是指组织损伤的前提下产生的＂躯体的不愉快的疼感觉＂加上＂与之相匹配的痛苦情感体验＂两方面内容。长期以来我们的医疗行为＂过多关注组织损伤对疼的调控、忽视了痛苦体验的人文关怀＂制约了我们服务层次。新的生物医学模式下诊疗工作强调细化疼痛分类、躯体与心理并重,以专科诊疗思路指导临床,提高医务人员对病痛的理解、引导供需匹配式的人性医疗服务是时代发展务实之举。     

Keeping Simon simple: Examining the relationship between sequential modulations and feature repetitions with two stimuli, two locations and two responses

The present study examined performance across three two-choice tasks that used the same two stimuli, the same two stimulus locations, and the same two responses to determine how task demands can alter the Simon Effect, its distribution across reaction time, and its sequential modulation. In two of the tasks, repetitions of stimulus features were not confounded with sequences of congruent and incongruent trials. This attribute allowed us to investigate the sequential modulation of the Simon Effect in a two-choice task while equalizing the occurrence of feature repetitions. All tasks showed a similar sequential modulation, suggesting that it is not driven by feature repetitions. Moreover, distributional analyses revealed that the advantage for congruent trials decreased as reaction time increased similarly following congruent and incongruent trials. Finally, a large increase in RT was observed when repeated responses were made to novel stimuli and when novel responses were made to repeated stimuli. This effect also showed a sequential modulation regardless of whether the stimulus repeated. The findings suggest that, even in two-choice tasks, response selection is mediated by complex, dynamic representations that encode abstract properties of the task rather than just simple features.     

Invariance Properties for General Diagnostic Classification Models

In item response theory (IRT), the invariance property states that item parameter estimates are independent of the examinee sample, and examinee ability estimates are independent of the test items. While this property has long been established and understood by the measurement community for IRT models, the same cannot be said for diagnostic classification models (DCMs). DCMs are a newer class of psychometric models that are designed to classify examinees according to levels of categorical latent traits. We examined the invariance property for general DCMs using the log-linear cognitive diagnosis model (LCDM) framework. We conducted a simulation study to examine the degree to which theoretical invariance of LCDM classifications and item parameter estimates can be observed under various sample and test characteristics. Results illustrated that LCDM classifications and item parameter estimates show clear invariance when adequate model data fit is present. To demonstrate the implications of this important property, we conducted additional analyses to show that using pre-calibrated tests to classify examinees provided consistent classifications across calibration samples with varying mastery profile distributions and across tests with varying difficulties.