迫选式人格测验的传统计分与IRT计分模型

迫选测验的传统计分方式会产生自模式数据, 不能进行传统的信效度检验、因素分析和方差分析等。近年来研究者提出了一些基于项目反应理论的计分模型, 如瑟斯顿IRT模型和MUPP模型等, 它们可以规避自模式数据的弊端。瑟斯顿IRT模型方便进行参数估计, 模型定义灵活; 而MUPP模型的拓展性较差, 参数估计的方法有待提高。另一方面, 已有研究者基于MUPP模型开发了一些抗作假的迫选测验, 而瑟斯顿IRT模型距离这种应用还比较远。此外, 两个模型的适用性和有效性都有待更多的实证研究来检验。     

多水平IRT的发展与应用述评

阶层线性模型是处理阶层结构数据的高级统计方法, 项目反应理论是精确测量被试能力的现代测量理论。多水平项目反应理论将阶层线性模型和项目反应理论相结合, 将项目反应模型嵌套在阶层线性模型内, 实现了项目参数和不同水平能力参数的估计, 对回归系数和误差项变异的估计也更加精确。作者概述了多水平项目反应理论的发展历程, 并从项目功能差异、测验等值、学校效能研究等方面评述了多水平项目反应理论在心理与教育测量中的应用, 总结了多水平项目反应理论的价值, 同时展望了今后的研究趋势。     

Employment trajectories: Exploring gender differences and impacts of drug use

This study investigated the impact of drug use on employment over 20 years among men and women, utilizing data on 7661 participants in the National Longitudinal Survey of Youth. Growth mixture modeling was applied, and five distinct employment trajectory groups were identified for both men and women. The identified patterns were largely similar for men and women except that a U-shape employment trajectory was uniquely identified for women. Early-initiation drug users, users of “hard” drugs, and frequent drug users were more likely to demonstrate consistently low levels of employment, and the negative relationship between drug use and employment was more apparent among men than women. Also, positive associations between employment and marriage became more salient for men over time, as did negative associations between employment and childrearing among women. Processes are dynamic and complex, suggesting that throughout the life course, protective factors that reduce the risk of employment problems emerge and change, as do critical periods for maximizing the impact of drug prevention and intervention efforts.     

二级项目题组混合IRT模型及其稳健权重估计

在心理测量和教育测量中,二级项目和题组项目是两类常见的项目类型。由这两种项目混合构成的测试在实践中有着重要的应用。被试在答题时,由于个人的潜在能力和项目难度不匹配,常常会产生异常反应,这些异常反应会影响IRT中潜在特质估计的准确性。仿真实验证明,二级项目题组混合IRT模型的稳健估计方法在出现异常值的情况下,能够比极大似然估计对被试的潜在特质做出更加准确的估计,能够满足实际测试的需求。     

高阶项目反应模型的发展与应用

在测量具有层阶结构的潜质时, 标准项目反应模型对项目参数估计和能力参数估计都具有较低的效率, 多维项目反应模型虽然在估计第一阶潜质时具有高效性, 但没有考虑到潜质层阶的情况, 所以它不适合用来处理具有层阶结构的潜质; 而高阶项目反应模型在处理这种具有层阶结构的潜质时, 不仅能够高效准确地对项目参数和能力参数进行估计, 而且还能同时获得高阶潜质与低阶潜质。目前存在的高阶项目反应模型有高阶DINA模型、高阶双参数正态肩型层阶模型、高阶逻辑斯蒂模型、多级评分的高阶项目反应模型和高阶题组模型。未来对高阶项目反应模型的研究方向应注意多水平高阶项目反应模型、项目内多维情况下的高阶项目反应模型以及高阶认知诊断模型。     

被试作答的偶然性对IRT能力估计的影响研究

被试能力参数估计是项目反应理论应用研究最重要的技术之一。本文在理想的测验情境下,研究被试作答的偶然性对被试能力值估计的影响。研究设计了被试作答的两种偶然性情况：一是偶然做对了一道项目难度高于其能力值的试题,二是偶然做错了一道或几道项目难度低于其能力值的试题．然后分别探讨了这两种情况下对被试的能力估计所带来的影响,并且就如何消除这些偶然性所带来的影响提出了相应的方法。     

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

ObjectivesLiver cancer is one of the leading cause of death in all over the world. Detecting the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided diagnosis (CAD) is used in decision making process for accurate detection for appropriate therapy. Therefore the main objective of this work is to detect the liver cancer accurately using automated method.MethodsIn this work, we have proposed a new system called as watershed Gaussian based deep learning (WGDL) technique for effective delineate the cancer lesion in computed tomography (CT) images of the liver. A total of 225 images were used in this work to develop the proposed model. Initially, the liver was separated using marker controlled watershed segmentation process and finally the cancer affected lesion was segmented using the Gaussian mixture model (GMM) algorithm. After tumor segmentation, various texture features were extracted from the segmented region. These segmented features were fed to deep neural network (DNN) classifier for automated classification of three types of liver cancer i.e. hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET).ResultsWe have achieved a classification accuracy of 99.38%, Jaccard index of 98.18%, at 200 epochs using DNN classifier with a negligible validation loss of 0.062 during the classification process.ConclusionsOur developed system is ready to be tested with huge database and can aid the radiologist in detecting the liver cancer using CT images.     

Analyzing Job Analysis Data Using Mixture Rasch Models

An important piece of validity evidence to support the use of credentialing exams comes from performing a job analysis of the profession. One common job analysis method is the task inventory method, where people working in the field are surveyed using rating scales about the tasks thought necessary to safely and competently perform the job. This article describes how mixture Rasch models can be used to analyze these data, and how results from these analyses can help to identify whether different groups of people may be responding to job tasks differently. Three examples from different credentialing programs illustrate scenarios that can be found when applying mixture Rasch models to job analysis data. Discussion of what these results may imply for the development of credentialing exams and other analyses of job analysis data is provided.