Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

论Windows映射模式在应用中若干问题的解决方法

在W indows应用程序中,只要与图形文字输出有关系,都要使用映射模式。实际应用中,我们会遇到一些与映射模式有关的问题,如OLEServer应用程序中映射模式的设置;映射模式下坐标转换误差等。本文主要针对这些问题,详细论述了解决的方法。     

深水蛙泳教学纠正错误动作的教法研究

在深水条件下进行游泳教学，存在的一个最大问题是：教师在水中无法帮助学生体会正确的游泳技术动作。所以，一般都是采用先在陆上模仿正确的游泳技术动作，然后下水进行练习的方法(常规教法)。用这种方法，学生在陆上做模仿练习时．在教师的指导和帮助下。技术动作的模仿练习做得非常正确．但下水练习时，大多数学生仍然表现出原有的错误动作。特别是一些学生，自学成一种近似于“狗爬式”和“侧泳”的混合姿势，由于他们长期使用这种不规范的游泳姿势，形成错误的技术动力定型，对其学习、掌握正确的游泳技术形成巨大的障碍。为了提高教学质量。加速改进学生错误技术动作的进程，我们对教学方法进行了改革，尝试在水中建立“教学平台”的方法，     

网络环境下主题标引误差的因素分析及对策

网络环境下,主题法将成为文献主要的标引与检索工具,并成为我国文献分类的主要手段,在对文献进行主题标引的过程中,往往会出现很多的偏差,造成文献的漏检.文章对文献主题标引工作中出现的问题进行了分析并提出网络环境下文献标引质量控制的对策.     

中文电子病历的分词及实体识别研究

[目的/意义]健康医疗大数据是我国重要的基础性战略资源，本研究对中文电子病历分词与实体识别的探讨与实证较好地完成了医疗数据的信息抽取任务，对今后医疗大数据在语义层面的应用发展具有重要意义。[方法/过程]本研究首先融合权威词表、官方标准、健康网站数据及其他医学补充词库构建了词语数量级达到10万的医学词表；然后对电子病历的字段进行分词，对比了jieba工具、导入词典后的jieba、无监督学习及AC自动机4种模型的分词效果；最后，以自动分词和人工标注结果为语料，实现基于条件随机场的电子病历实体识别研究，并比较不同实体类别以及不同文本特征下的实体识别效果，选出最优模板。[结果/结论]分词结果显示，AC自动机的效果最好，F值可达82%；实体识别结果表明，"检查"和"疾病"实体的识别效果最好，而"症状"的识别效果不太理想。     

Teaching principles of inference with ANOVA

Analysis of variance (ANOVA) is a test of mean differences, but the reference to variances in the name is often overlooked. Classroom activities are presented to illustrate how ANOVA works with emphasis on how to think critically about inferential reasoning.     

An NCME Instructional Module on Data Mining Methods for Classification and Regression

Data mining methods for classification and regression are becoming increasingly popular in various scientific fields. However, these methods have not been explored much in educational measurement. This module first provides a review, which should be accessible to a wide audience in education measurement, of some of these methods. The module then demonstrates using three real‐data examples that these methods may lead to an improvement over traditionally used methods such as linear and logistic regression in educational measurement.     

《国际商事合同通则》(中译本)词汇翻译错误辨析

从对英语单词、短语的理解入手,就《国际统一私法协会国际商事合同通则》(中英文对照本)中文译文中出现的对英语单词、短语意思的误解而导致的误译情形进行了简要分析和评述。     

用测量不确定度GUM表示测量误差的方法

测量不确定度GUM的评定与表示是一个较新的概念,其应用具有广泛性和实用性。用测量不确定度来表示测量误差的方法,是科技交流和国际贸易的迫切要求。它使各国进行的测量及其所得结果可以进行相互比对,以取得相互间的承认或共识,从而受到了国际组织和各国计量部门的高度重视。