利用综合英语课提高学生的综合语言能力

综合英语课是一门综合性的语言基础课。在综合英语课教学中，结合所教学生的实际英语水平，采用不同的教学方法，并组织形式多样的课堂教学活动，使学生在大量的语言实践活动中掌握并具备听、说、读、写等综合语言能力。     

Multiple imputation of missing data with ante-dependence covariance structure

A controlled clinical trial was conducted to investigate the efficacy effect of a chemical compound in the treatment of Premenstrual Dysphoric Disorder (PMDD). The data from the trial showed a non-monotone pattern of missing data and an ante-dependence covariance structure. A new analytical method for imputing the missing data with the ante-dependence covariance is proposed. The PMDD data are analysed by the non-imputation method and two imputation methods: the proposed method and the MCMC method.     

化学学习动机的调查分析

作者运用教育心理学的课堂成就动机理论结合化学教学实际 ,选取了 14种化学学习动机进行问卷调查 ,并进行总体统计分析和抽样统计分析 ,得出了这样的结论。化学教师可以在教学中有效地培养 ,激发和强化学生的化学学习动机 ,提高教与学的效率 ,更好地培养学生的创新精神和实践能力。     

中等收入者论

中等收入者指一个国家的中等富裕群体。为确保社会经济健康、协调、持续发展 ,必须努力提高低收入者素质 ,降低转化门槛 ,扩大中等收入者比重。     

疫情防控期间高校线上教学的效果调查与分析

突发的疫情使得线上教学在所有高校全面铺开。为了了解这次“应急线上教学”的教学效果,上海理工大学教师发展中心对全校师生从线上教学的评价、线上线下教学体验对比、线上教学带给师生的改变等方面进行了线上教学效果的问卷调查。研究对专项调查的数据进行了深入分析,得出了线上与线下学习效果基本等效、线上线下教学各具优势、线上教学带来深刻改变的调查结果,提出了线上教学存在的主要问题,并给出了相应提升线上教学效果的建议。     

众筹教育与高校美育课改革的契合及实现路径

众筹教育通过打造并完善教育资源共享平台,推动教育资源的虚拟重组、均衡发展,实现跨区域教育的协同发展,有助于实现知识增值的目标。众筹教育所具备的创意性筹资、柔性化生产、互动化交流、区域化共享等特征使其与当下的高校美育选修课教育体制改革的需求高度契合。从实现路径来看,众筹教育介入高校美育选修课改革可以从校内模拟众筹、区域内课程共享、完善教育资源共享平台3个层面逐步铺开,形成高校美育选修课线上与线下互动发展的新模式,为推动我国教育体制改革困局的破解提供思路。     

Nonparametric analysis of treatment effects with missing observations

This paper develops a test for comparing treatment effects when observations are missing at random for repeated measures data on independent subjects. It is assumed that missingness at any occasion follows a Bernoulli distribution. It is shown that the distribution of the vector of linear rank statistics depends on the unknown parameters of the probability law that governs missingness, which is absent in the existing conditional methods employing rank statistics. This dependence is through the variance–covariance matrix of the vector of linear ranks. The test statistic is a quadratic form in the linear rank statistics when the variance–covariance matrix is estimated. The limiting distribution of the test statistic is derived under the null hypothesis. Several methods of estimating the unknown components of the variance–covariance matrix are considered. The estimate that produces stable empirical Type I error rate while maintaining the highest power among the competing tests is recommended for implementation in practice. Simulation studies are also presented to show the advantage of the proposed test over other rank-based tests that do not account for the randomness in the missing data pattern. Our method is shown to have the highest power while also maintaining near-nominal Type I error rates. Our results clearly illustrate that even for an ignorable missingness mechanism, the randomness in the pattern of missingness cannot be ignored. A real data example is presented to highlight the effectiveness of the proposed method.     

Model-based clustering of multivariate skew data with circular components and missing values

Motivated by classification issues that arise in marine studies, we propose a latent-class mixture model for the unsupervised classification of incomplete quadrivariate data with two linear and two circular components. The model integrates bivariate circular densities and bivariate skew normal densities to capture the association between toroidal clusters of bivariate circular observations and planar clusters of bivariate linear observations. Maximum-likelihood estimation of the model is facilitated by an expectation maximization (EM) algorithm that treats unknown class membership and missing values as different sources of incomplete information. The model is exploited on hourly observations of wind speed and direction and wave height and direction to identify a number of sea regimes, which represent specific distributional shapes that the data take under environmental latent conditions.     

Simultaneous edit-imputation and disclosure limitation for business establishment data

Business establishment microdata typically are required to satisfy agency-specified edit rules, such as balance equations and linear inequalities. Inevitably some establishments' reported data violate the edit rules. Statistical agencies correct faulty values using a process known as edit-imputation. Business establishment data also must be heavily redacted before being shared with the public; indeed, confidentiality concerns lead many agencies not to share establishment microdata as unrestricted access files. When microdata must be heavily redacted, one approach is to create synthetic data, as done in the U.S. Longitudinal Business Database and the German IAB Establishment Panel. This article presents the first implementation of a fully integrated approach to edit-imputation and data synthesis. We illustrate the approach on data from the U.S. Census of Manufactures and present a variety of evaluations of the utility of the synthetic data. The paper also presents assessments of disclosure risks for several intruder attacks. We find that the synthetic data preserve important distributional features from the post-editing confidential microdata, and have low risks for the various attacks.     

Multiple imputation for missing edge data: A predictive evaluation method with application to Add Health

Recent developments have made model-based imputation of network data feasible in principle, but the extant literature provides few practical examples of its use. In this paper, we consider 14 schools from the widely used In-School Survey of Add Health (Harris et al., 2009), applying an ERGM-based estimation and simulation approach to impute the network missing data for each school. Add Health's complex study design leads to multiple types of missingness, and we introduce practical techniques for handing each. We also develop a cross-validation based method – Held-Out Predictive Evaluation (HOPE) – for assessing this approach. Our results suggest that ERGM-based imputation of edge variables is a viable approach to the analysis of complex studies such as Add Health, provided that care is used in understanding and accounting for the study design.