美国小班教学综述

小班教学是美国教育改革中一项重要的课题，一些研究人员对这一课题进行了卓有成效的研究，许多州开展了小班教学实验或者推行与此相关的计划。美国小班教学的理论研究与实践表明：实施小班教学能够使教师给予每一个学生更多的关注时间，从而提高他们的学习成绩。     

整体观照 精心梳理 准确考量——评全展《传记文学：阐释与批评》

《传记文学:阐释与批评》在观照中国当代传记文学时,能够从整体出发,这样,中国当代传记文学经过全展精心梳理,就从一个侧面清晰地显示出了"一个与时代同时出现的秩序",一些理论或创作中的问题就得到了科学的界定和系统的阐发,一系列具体文本就得到了客观公正的评价。     

Sample Size Calculations for Precise Interval Estimation of the Eta-Squared Effect Size

Analysis of variance is one of the most frequently used statistical analyses in the behavioral, educational, and social sciences, and special attention has been paid to the selection and use of an appropriate effect size measure of association in analysis of variance. This article presents the sample size procedures for precise interval estimation of eta-squared and partial eta-squared in fixed-effects analysis of variance designs. The desired precision of a confidence interval is assessed with respect to (a) the control of expected width and (b) the tolerance probability of interval width within a designated value. In addition, sample size calculations for standardized contrasts of treatment effects and corresponding partial strength of association effect sizes are also considered.     

Effect of Sample Size Ratio and Model Misfit When Using the Difficulty Parameter Differences Procedure to Detect DIF

This study examined the effect of sample size ratio and model misfit on the Type I error rates and power of the Difficulty Parameter Differences procedure using Winsteps. A unidimensional 30-item test with responses from 130,000 examinees was simulated and four independent variables were manipulated: sample size ratio (20/100/250/500/1000); model fit/misfit (1 PL and 3PLc =. 15 models); impact (no difference/mean differences/variance differences/mean and variance differences); and percentage of items with uniform and nonuniform DIF (0%/10%/20%). In general, the results indicate the importance of ensuring model fit to achieve greater control of Type I error and adequate statistical power. The manipulated variables produced inflated Type I error rates, which were well controlled when a measure of DIF magnitude was applied. Sample size ratio also had an effect on the power of the procedure. The paper discusses the practical implications of these results.     

Class Size and Learning: A Theoretical Model

Abstract

A theoretical model of the relationship between class size and achievement is proposed, based on the assumption that a teacher adjusts the style and pace of a lesson to the least able student in the class. It is shown that the model and an extension to take into account the duration of instruction account well for various features of the data collected in the Glass and Smith (1978) meta-analysis of research. In particular, a correlation of 0.62 between predicted and empirical effect sizes was obtained.     

两级·三督一体·多样化的教学督导模式的建立与实践探索

遵义医学院自1999年成立教学督导组以来,经过近10年来的不断研究与探索,形成了独具特色的两级·三督一体·多样化的教学督导模式,对我校教学质量监控体系的建立与完善,对提高我校教学质量效果显著.本文试图通过多年的实践经验总结,对教学督导模式进行深层次探索,希望时督导工作的发展有所帮助.     

Class Size and Student Achievement: Small Effects of Small Classes

Based on reviews by Glass, Cahen, Smith, and Filby (1982) and the Educational Research Service (1978), Cooper (this issue) concludes that substantial reductions in class size can have important effects on low-achieving students in the early grades. This article critiques these reviews and summarizes the findings of experimental studies that compared the achievement levels of elementary school students in larger classes to classes with no more than 20 students. Even in studies that made such substantial reductions, achievement differences were slight, averaging only 13% of a standard deviation. Not until class size approaches one is there evidence of meaningful effects. Based on this and other evidence, it is suggested that Chapter 1 programs provide one-to-one tutoring in reading rather than providing small-group pullouts or reducing overall class size.     

职业教育吸引力:来自农村中小企业的调查

对农村中小企业调查显示,当下职业教育吸引力不仅有所体现,而且较令人满意,并还在不断增强的过程之中。吸引力的着力点是全面贯彻和彰显促进学生获得全面发展和更好地赢得尊严这一办学宗旨。随着《规划纲要》的实施,更为实现职教办学宗旨提供了越来越好的职教吸引机制。为落实好《规划纲要》,更好地实现办学宗旨,建议将增强职业教育吸引力纳入到对地方政府的考核之中;职业教育自身要围绕办学宗旨创新吸引机制。     

例说教师的创造性劳动与教育效果

前苏联教育家霍姆林斯基说：“我们工作的对象是正在形成中的个性最细腻的精神生活领域，即智慧、感性、意志、信念和自我意识。这些领域，也只能用同样的东西即智慧、感性、意志、信念和自我意识去施加影响。”这一论断强调教师是做人的工作的。对人的教育是一种特殊的认识活动，作为教育教学过程活动中的主体──学生，他们各自的认识水平与情感特征等个性是各不相同的。因而在教育教学过程中随时都可能出现意外的情况，这包括道德情感及接受知识过程中发生的问题。如何及时处理这些意外的情况，使之不影响教育目的的实现，或者说如何把这…     

考生样本量对项目反应理论(IRT)等值稳定性的影响

测验等值使得不同形式的考试能进行比较,从而保证了测验之间的相对稳定性。基于IRT的分数等值是在估计出参数的基础上进行的参数转换,等值结果的稳定性与考生样本量密不可分。本研究针对汉语水平考试(HSK)阅读分测验,采用真实数据模拟共同组锚测验设计,确定等值的参照标准,考察考生样本量的变化对IRT分数等值稳定性的影响。结果表明,考生样本量为2000左右时各种方案的等值结果均比较稳定。考生样本量进一步增大时,等值误差不降反增。     

The Model Size Effect in SEM: Inflated Goodness-of-Fit Statistics Are Due to the Size of the Covariance Matrix

The size of a model has been shown to critically affect the goodness of approximation of the model fit statistic T to the asymptotic chi-square distribution in finite samples. It is not clear, however, whether this “model size effect” is a function of the number of manifest variables, the number of free parameters, or both. It is demonstrated by means of 2 Monte Carlo computer simulation studies that neither the number of free parameters to be estimated nor the model degrees of freedom systematically affect the T statistic when the number of manifest variables is held constant. Increasing the number of manifest variables, however, is associated with a severe bias. These results imply that model fit drastically depends on the size of the covariance matrix and that future studies involving goodness-of-fit statistics should always consider the number of manifest variables, but can safely neglect the influence of particular model specifications.     

A3模型的微振动

导出了A_3模型微振动方程组的通解,并讨论其算例振动图象.     

Simulation Studies of the Effects of Small Sample Size and Studied Item Parameters on SIBTEST and Mantel-Haenszel Type I Error Performance

Two simulation studies investigated Type I error performance of two statistical procedures for detecting differential item functioning (DIF): SIBTEST and Mantel-Haenszel (MH). Because MH and SIBTEST are based on asymptotic distributions requiring "large" numbers of examinees, the first study examined Type 1 error for small sample sizes. No significant Type I error inflation occurred for either procedure. Because MH has the potential for Type I error inflation for non-Rasch models, the second study used a markedly non-Rasch test and systematically varied the shape and location of the studied item. When differences in distribution across examinee group of the measured ability were present, both procedures displayed inflated Type 1 error for certain items; MH displayed the greater inflation. Also, both procedures displayed statistically biased estimation of the zero DIF for certain items, though SIBTEST displayed much less than MH. When no latent distributional differences were present, both procedures performed satisfactorily under all conditions.     

The Use of Incorrect Informative Priors in the Estimation of MIMIC Model Parameters with Small Sample Sizes

Recently, advancements in Bayesian structural equation modeling (SEM), particularly software developments, have allowed researchers to more easily employ it in data analysis. With the potential for greater use, come opportunities to apply Bayesian SEM in a wider array of situations, including for small sample size problems. Effective use of Bayseian estimation hinges on selection of appropriate prior distributions for model parameters. Researchers have suggested that informative priors may be useful with small samples, presuming that the mean of the prior is accurate with respect to the population mean. The purpose of this simulation study was to examine model parameter estimation for the Multiple Indicator Multiple Cause model when an informative prior distribution had an incorrect mean. Results demonstrated that the use of incorrect informative priors with somewhat larger variance than is typical, yields more accurate parameter estimates than do naïve priors, or maximum likelihood estimation. Implications for practice are discussed.     

论中小企业的发展和政府的作用

本文从论述我国中小企业生存和发展的关键要素入手，分析了制约我国中小企业发展的外部因素，并在此基础上提出，政府应在宏观和微观两个层面上为我国中小企业的快速发展营造一个良好的环境．     

An Investigation of the Effect of Sample Size and Specification Error on the Fit of Structural Equation Models

Abstract

Covariance structure analysis provides a useful methodology to test hypotheses about competing structural models. The chi-square goodness of fit test is basically an appropriate test for model evaluation. However, methodologists are particularly concerned about the validity of the test to detect misspecified models in small samples. At the same time, there is the concern of rejecting models with reasonably good fit in large samples. The present Monte Carlo study examined the validity of the chi-square test in different instances of misspecification and sample size. The usefulness of the chi-square difference statistic to compare competing structures and improvement in fit is also addressed.     

Sample Invariance of the Structural Equation Model and the Item Response Model: A Case Study

The sample invariance of item discrimination statistics is evaluated in this case study using real data. The hypothesized superiority of the item response model (IRM) is tested against structural equation modeling (SEM) for responses to the Center for Epidemiologic Studies-Depression (CES-D) scale. Responses from 10 random samples of 500 people were drawn from a base sample of 6,621 participants across gender, age, and different health groups. Hierarchical tests of multiple-group structural equation models indicated statistically significant differences exist in item regressions across contrast groups. Although the IRM item discrimination estimates were most stable in all conditions of this case study, additional research on the precision of individual scores and possible item bias is required to support the validity of either model for scoring the CES-D. The SEM approach to examining between-group differences holds promise for any field where heterogeneous populations are assessed and important consequences arise from score interpretations.     

The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models

The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying methods of estimation, level-1 and level-2 sample size, outcome prevalence, variance component sizes, and number of predictors using SAS software. Mean estimates of statistical power were influenced primarily by sample sizes at both levels. In addition, confidence interval coverage and width and the likelihood of nonpositive definite random effect covariance matrices were impacted by variance component size and estimation method. The interactions of these and other factors with various model performance outcomes are explored.     

Sample Size Requirements in Single- and Multiphase Growth Mixture Models: A Monte Carlo Simulation Study

Just as growth mixture models are useful with single-phase longitudinal data, multiphase growth mixture models can be used with multiple-phase longitudinal data. One of the practically important issues in single- and multiphase growth mixture models is the sample size requirements for accurate estimation. In a Monte Carlo simulation study, the sample sizes required for using these models are investigated under various theoretical and realistic conditions. In particular, the relationship between the sample size requirement and the number of indicator variables is examined, because the number of indicators can be relatively easily controlled by researchers in many multiphase data collection settings such as ecological momentary assessment. The findings not only provide tangible information about required sample sizes under various conditions to help researchers, but they also increase understanding of sample size requirements in single- and multiphase growth mixture models.