基于PLS-SEM模型的高校学生评教实证分析

"学生评教"是教学质量评价体系的重要组成部分,对学生评教数据进行合理的处理有利于提高评教结论的科学性和说服力,文章基于PLS-SEM建构学生评教模型,通过SmartPLS的计算和验证,探讨了学生评教指标体系的内部结构和数据关系,并通过实证分析指出了学生评教存在的问题和改进建议.     

正态分布在教学管理中的应用

考试是学校教学管理工作中的一项重要内容。考试分数可为各级教学管理人员提供必要的信息，使教师了解和把握教学效果，及时总结经验教训，不断改进教学方法，提高教学质量。同时可使学生在横向和纵向的比较中，清楚自己的学习效果，明确今后的努力方向。对教学管理人员来讲，可了解“教”和“学”的情况，及时发现教学工作中的问题，为教学管理和评估工作提供必要的资料。因此，如何科学地评价考试结果，据此提供有效而可靠的信息，是需要解决的关键问题。本文利用正态分布的有关知识，探讨对考试结果的科学评价问题。一、考试分数与正态分…     

教师教学质量评估方法的探讨

教学工作是学校的中心工作,是培养人才、实现教育目的的基本途径。教师教学质量评价是实现这一途径的重要措施和保证,是突出教学中心地位及作用的主要动力,是检验教育教学质量的重要手段。本文从教学质量评估过程遇到的问题出发,提出可供参考的三级评估方法。     

评教分数的统计量化算法

学生评教是教学型学校的教学质量监控的一个重要手段,但由于不同的班或学生对打分的标准把握不同、打分随意性等原因,评教结果明显不合理.本文是在某高校上年评教结果基础上,对数据采用数学期望、方差和标准差计算,得出一个量化模型,使评教结果科学化和合理化.     

对学生评教评学问卷调查的评价

一所学校的教育质量关键在于教师。教师的教学能力往往决定学校教学质量的高低，因此学校对教师教学能力进行正确地评价是保证教师高质量的完成教学工作，促使学生在德、智、体诸方面和谐发展的有效手段。那么，如何正确评价教师的教学能力呢？我们主要通过教学常规检查来评价，其中学生（由各班学习委员和课代表组成）（以下称为学生代表）评教评学问卷调查为一项评价内容。而这样的调查情况（对各任课教师的讲课、批改、辅导的调查）能否代表该班全体学生的评价呢？即：学生代表与全班学生对各任课教师的讲课、批改、辅导的评价是否一致呢…     

统计方法在英语考试质量评估中的应用

考试是教学效果评价的一种重要手段。对高校英语专业的教师而言，如何根据英语教学的特点及授课的内容，通过对学生的命题考试，检验教学效果，是其在教学工作中的一项重要工作。但是，考试结果是否能够准确反映英语教学的质量和效果，就成为关键。本文利用统计方法在此方面作一探讨。一、可信度评估英语专业学生的考试成绩，受多方面因素的影响，时好时坏，一次考试成绩并不一定能够反映学生对本门课程的学习效果，这就对教学老师提出了一个问题：期末考试成绩是否能够代表或反映本门课程的教学质量和效果，以什么标准去衡量或测定考试结果…     

高校学生评教制度有效性的调查分析

高校师生对"学生评教"的认同度,关系到学生评教本身的效度和信度,也关系到"学生评教"结果用于改进教学和管理工作的效果,是该制度成功的关键因素。本文对广东四所高校"学生评教"情况进行了调查分析,指出存在的问题,并提出相关的改进意见。     

高等学校教师教学质量的统计评价方法研究

本文根据高等学校教师教学质量评价的特点,将模糊综合评价法和马尔可夫评估法相结合,对教学质量进行综合评价。对教学内容、教学态度、教学方法三个指标,应用模糊综合评价,较好的解决了强制打分中硬性截割和评分的问题;对教学效果则根据学生成绩变化情况利用马尔可夫链评估法评价,解决了由于学生来源、基础差别而产生的对教学效果的影响问题。并用实例分析了该模型的具体应用。     

寻找大学好老师——基于准实验的教育增值评估研究

教学、科研、服务社会是高等院校三大基本功能,随着市场化、科研竞赛的深化,我国高等教育教学面临巨大挑战.利用大学新生随机分班的准实验设计,本文解决了教育增值评估中的学生自选择问题.研究发现,教师影响存在明显挥发性,但是其短期和长期影响是一致的,“为考试而教”与“为知识而教”的长短期影响不一致性在语言类课程中并不明显.分类别来看,编制内讲师的教学效果最差,外聘教师教学效果最好,在读研究生虽然有着不错的短期效果,但是长期效果差.最后,本文从课程教师聘用和教学评估的视角对提高大学教学质量给出思考.     

改进的属性约简算法在教学评估系统中的应用

教学质量评估系统中,建立评估指标体系是教学质量评估最基本最关键的一步。根据评估的基本原则,一般对指标体系从以下几方面着手:教学态度、教学内容、教学方法、教学效果等。为了找出规律,必须对原始信息进行科学的分析和研究,才能得出客观、真实的结论。如何以较少的指标做出准确的评价,同时具有代表性和决策性,是本文所要讨论的。     

政府统计数据准确性评估方法述评

目前,政府统计数据评估已经从方法研究上升至方法实践。针对现有评估实践中出现的种种问题,本文在系统介绍政府统计数据准确性评估方法原理和代表文献的基础上,重点分析了各种方法的应用前提、优势和局限性。文中提出应根据统计数据的特性,结合评估目的进行方法选择,尽量满足方法的使用前提,可以使用一种方法,也可以多种方法并用,并强调审慎对待评估结果。     

统计数据质量与用户满意度：测评量表设计与实证研究

 针对政府统计数据质量评估问题,综合若干国家统计部门与国际组织对统计质量的定义,确立政府统计数据质量维度体系;进而参照国际货币基金组织的“数据质量评估框架”设计具体的用户满意度测评量表。基于该量表的问卷调查结果显示,量表具有良好的测量信度与效度。研究中提出的八项质量维度可以充分反映政府统计数据质量的不同侧面,可以有效解构用户满意度的特征;用户对各质量维度的重要性认知、用户专业技术职务、对统计数据的依赖程度以及搜集渠道的选择等,与其满意度之间亦表现出明显的关联模式。     

Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science

ABSTRACT

Scientific research of all kinds should be guided by statistical thinking: in the design and conduct of the study, in the disciplined exploration and enlightened display of the data, and to avoid statistical pitfalls in the interpretation of the results. However, formal, probability-based statistical inference should play no role in most scientific research, which is inherently exploratory, requiring flexible methods of analysis that inherently risk overfitting. The nature of exploratory work is that data are used to help guide model choice, and under these circumstances, uncertainty cannot be precisely quantified, because of the inevitable model selection bias that results. To be valid, statistical inference should be restricted to situations where the study design and analysis plan are specified prior to data collection. Exploratory data analysis provides the flexibility needed for most other situations, including statistical methods that are regularized, robust, or nonparametric. Of course, no individual statistical analysis should be considered sufficient to establish scientific validity: research requires many sets of data along many lines of evidence, with a watchfulness for systematic error. Replicating and predicting findings in new data and new settings is a stronger way of validating claims than blessing results from an isolated study with statistical inferences.     

On estimands and the analysis of adverse events in the presence of varying follow‐up times within the benefit assessment of therapies

The analysis of adverse events (AEs) is a key component in the assessment of a drug's safety profile. Inappropriate analysis methods may result in misleading conclusions about a therapy's safety and consequently its benefit‐risk ratio. The statistical analysis of AEs is complicated by the fact that the follow‐up times can vary between the patients included in a clinical trial. This paper takes as its focus the analysis of AE data in the presence of varying follow‐up times within the benefit assessment of therapeutic interventions. Instead of approaching this issue directly and solely from an analysis point of view, we first discuss what should be estimated in the context of safety data, leading to the concept of estimands. Although the current discussion on estimands is mainly related to efficacy evaluation, the concept is applicable to safety endpoints as well. Within the framework of estimands, we present statistical methods for analysing AEs with the focus being on the time to the occurrence of the first AE of a specific type. We give recommendations which estimators should be used for the estimands described. Furthermore, we state practical implications of the analysis of AEs in clinical trials and give an overview of examples across different indications. We also provide a review of current practices of health technology assessment (HTA) agencies with respect to the evaluation of safety data. Finally, we describe problems with meta‐analyses of AE data and sketch possible solutions.     

Robust nonparametric estimation of the intensity function of point data

Intensity functions—which describe the spatial distribution of the occurrences of point processes—are useful for risk assessment. This paper deals with the robust nonparametric estimation of the intensity function of space–time data from events such as earthquakes. The basic approach consists of smoothing the frequency histograms with the local polynomial regression (LPR) estimator. This method allows for automatic boundary corrections, and its jump-preserving ability can be improved with robustness. We derive a robust local smoother from the weighted-average approach to M-estimation and we select its bandwidths with robust cross-validation (RCV). Further, we develop a robust recursive algorithm for sequential processing of the data binned in time. An extensive application to the Northern California earthquake catalog in the San Francisco, CA, area illustrates the method and proves its validity.     

Assessing noninferiority: Evaluating efficacy of a new treatment without complete data

The FDA released the final guidance on noninferiority trials in November 2016. In noninferiority trials, validity of the assessment of the efficacy of the test treatment depends on the control treatment's efficacy. Therefore, it is critically important that there be a reliable estimate of the control treatment effect—which is generally obtained from historical trials, and often assumed to hold in the current setting (the assay constancy assumption). Validating the constancy assumption requires clinical data, which are typically lacking. The guidance acknowledges that “lack of constancy can occur for many reasons.” We clarify the objectives of noninferiority trials. We conclude that correction for bias, rather than assay constancy, is critical to conducting valid noninferiority trials. We propose that assay constancy not be assumed and discounting or thresholds be used to address concern about loss of historical efficacy. Examples are provided for illustration.     

A pseudo likelihood approach to analyze rate difference of binary response data in longitudinal factorial studies

Although Fan showed that the mixed-effects model for repeated measures (MMRM) is appropriate to analyze complete longitudinal binary data in terms of the rate difference, they focused on using the generalized estimating equations (GEE) to make statistical inference. The current article emphasizes validity of the MMRM when the normal-distribution-based pseudo likelihood approach is used to make inference for complete longitudinal binary data. For incomplete longitudinal binary data with missing at random missing mechanism, however, the MMRM, using either the GEE or the normal-distribution-based pseudo likelihood inferential procedure, gives biased results in general and should not be used for analysis.     

Nonparametric model checks in censored regression

Let M be a parametric model for an unknown regression function m. In order to check the validity of M, i.e., to test for m ∈ M, it is known that optinal tests should be based on the empirical process of the regressors marked by the residuals. In this paper we extend the methodology to censored regression. The asymptotic distribution of the underlying marked empirical process in provided. The Wild Bootstrap, appropriately modified to account for censhorship, provides distributional approximations. The method is applied to simulated data sets as well as tto the Stanford Heart Transplant Data.     

Efficiency of generalized estimating equations for binary responses

Summary.  Using standard correlation bounds, we show that in generalized estimation equations (GEEs) the so-called 'working correlation matrix' R ( α ) for analysing binary data cannot in general be the true correlation matrix of the data. Methods for estimating the correlation param-eter in current GEE software for binary responses disregard these bounds. To show that the GEE applied on binary data has high efficiency, we use a multivariate binary model so that the covariance matrix from estimating equation theory can be compared with the inverse Fisher information matrix. But R ( α ) should be viewed as the weight matrix, and it should not be confused with the correlation matrix of the binary responses. We also do a comparison with more general weighted estimating equations by using a matrix Cauchy–Schwarz inequality. Our analysis leads to simple rules for the choice of α in an exchangeable or autoregressive AR(1) weight matrix R ( α ), based on the strength of dependence between the binary variables. An example is given to illustrate the assessment of dependence and choice of α .     

Evaluation of the parameters of su by rational fractions

A rational fraction approximation is given for a function of one of the parameters defining Johnson's S_UError assessment for a segment of the domain of validity shows remarkable accuracy.