Optimal Bandwidth Selection in Observed‐Score Kernel Equating

The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent groups design and a nonequivalent group with anchor test design. The performance of the methods was evaluated through simulation studies using both symmetric and skewed score distributions. In addition, the bandwidth selection methods were applied to real data from a college admissions test. The results show that the traditional penalty method works well although double smoothing is a viable alternative because it performs reasonably well compared to the traditional method.     

Evaluating Equating Results: Percent Relative Error for Chained Kernel Equating

This article presents a method for evaluating equating results. Within the kernel equating framework, the percent relative error (PRE) for chained equipercentile equating was computed under the nonequivalent groups with anchor test (NEAT) design. The method was applied to two data sets to obtain the PRE, which can be used to measure equating effectiveness. The study compared the PRE results for chained and poststratification equating. The results indicated that the chained method transformed the new form score distribution to the reference form scale more effectively than the poststratification method. In addition, the study found that in chained equating, the population weight had impact on score distributions over the target population but not on the equating and PRE results.     

Local Observed‐Score Kernel Equating

Three local observed‐score kernel equating methods that integrate methods from the local equating and kernel equating frameworks are proposed. The new methods were compared with their earlier counterparts with respect to such measures as bias—as defined by Lord's criterion of equity—and percent relative error. The local kernel item response theory observed‐score equating method, which can be used for any of the common equating designs, had a small amount of bias, a low percent relative error, and a relatively low kernel standard error of equating, even when the accuracy of the test was reduced. The local kernel equating methods for the nonequivalent groups with anchor test generally had low bias and were quite stable against changes in the accuracy or length of the anchor test. Although all proposed methods showed small percent relative errors, the local kernel equating methods for the nonequivalent groups with anchor test design had somewhat larger standard error of equating than their kernel method counterparts.     

A Comparison of the Kernel Equating Method with Traditional Equating Methods Using SAT® Data

This study applied kernel equating (KE) in two scenarios: equating to a very similar population and equating to a very different population, referred to as a distant population, using SAT^® data. The KE results were compared to the results obtained from analogous traditional equating methods in both scenarios. The results indicate that KE results are comparable to the results of other methods. Further, the results show that when the two populations taking the two tests are similar on the anchor score distributions, different equating methods yield the same or very similar results, even though they have different assumptions.     

A Comparison Between Linear IRT Observed‐Score Equating and Levine Observed‐Score Equating Under the Generalized Kernel Equating Framework

In this article, linear item response theory (IRT) observed‐score equating is compared under a generalized kernel equating framework with Levine observed‐score equating for nonequivalent groups with anchor test design. Interestingly, these two equating methods are closely related despite being based on different methodologies. Specifically, when using data from IRT models, linear IRT observed‐score equating is virtually identical to Levine observed‐score equating. This leads to the conclusion that poststratification equating based on true anchor scores can be viewed as the curvilinear Levine observed‐score equating.     

Using Kernel Equating to Assess Item Order Effects on Test Scores

This study explored the use of kernel equating for integrating and extending two procedures proposed for assessing item order effects in test forms that have been administered to randomly equivalent groups. When these procedures are used together, they can provide complementary information about the extent to which item order effects impact test scores, in overall score distributions and also at specific test scores. In addition to detecting item order effects, the integrated procedures also suggest the equating function that most adequately adjusts the scores to mitigate the effects. To demonstrate, the statistical equivalences of alternate versions of two large-volume advanced placement exams were assessed.     

Equating Error and Statistical Bias in Small Sample Linear Equating

A resampling study was conducted to compare the statistical bias and standard errors of nonequivalent-groups linear test equating in small samples of examinees. Sample sizes of 15, 25, 50, and 100 were examined. One thousand samples of each size were drawn with replacement from each of 5 archival data files from teacher subject area tests. For each test, data files from 2 parallel forms were used. Results suggest trivial levels of equating bias even with small samples, but substantial increases in standard errors as sample size decreases. Results were interpreted in terms of applications to testing situations in which small numbers of examinees are available.     

Multi-user Detection Based on ICA with Processing of Optimal Bandwidth Kernel Estimatio

将核估计中递归选择窗宽的ICA算法应用于DS--CDMA系统的多用户检测中,在样本总体真实密度未知时,根据给定的核密度和抽取的样本点来选择最优窗宽。采用ICA最优核窗估计算法检测器的输出初始化独立分量分析的迭代,对任意混叠信号进行盲分离。通过仿真证明了该算法在DS--CDMA多用户检测中的有效性。     

Statistical Models and Inference for the True Equating Transformation in the Context of Local Equating

Based on Lord's criterion of equity of equating, van der Linden (this issue) revisits the so‐called local equating method and offers alternative as well as new thoughts on several topics including the types of transformations, symmetry, reliability, and population invariance appropriate for equating. A remarkable aspect is to define equating as a standard statistical inference problem in which the true equating transformation is the parameter of interest that has to be estimated and assessed as any standard evaluation of an estimator of an unknown parameter in statistics. We believe that putting equating methods in a general statistical model framework would be an interesting and useful next step in the area. van der Linden's conceptual article on equating is certainly an important contribution to this task.     

基于自适应梯度算法的SVM核极化参数选择方法

在机器学习领域,支持向量机SVM(Support Vector Machine)是一个有监督的学习模型,通常用来进行模式识别、分类以及回归分析.模型选择是影响SVM性能的主要因素,具体地讲是SVM中的核函数及其惩罚系数C的选择决定着SVM的分类能力.本文首先讨论了一种评价核函数好坏的重要标准,即核极化.在此基础上提出了基于核函数优化问题的自适应梯度算法,即用一种自适应步长去改进原来的固定步长.UCI数据集(机器学习数据库)上的实验结果验证了这种自适应梯度算法的有效性,结果表明该算法能有效减少程序运行的迭代步及SVM学习训练的时间.     

Selection Strategies for Univariate Loglinear Smoothing Models and Their Effect on Equating Function Accuracy

In this study, we compared 12 statistical strategies proposed for selecting loglinear models for smoothing univariate test score distributions and for enhancing the stability of equipercentile equating functions. The major focus was on evaluating the effects of the selection strategies on equating function accuracy. Selection strategies' influence on the estimation of cumulative test score distributions was also assessed. The results of this simulation study differentiate the selection strategies and define the situations where their use has the most important implications for equating function accuracy. The recommended strategy for estimating test score distributions and for equating is AIC minimization.     

中国梦价值内核选择的国内动因探析

社会主义核心价值观是中国梦构成的价值内核,这是经过历史和实践选择的结果.主要从国内经济、政治、文化和社会等四个维度入手,分析了中国梦选择社会主义核心价值观为其价值内核的国内动因.     

The Effects of Selection Strategies for Bivariate Loglinear Smoothing Models on NEAT Equating Functions

In this study, eight statistical strategies were evaluated for selecting the parameterizations of loglinear models for smoothing the bivariate test score distributions used in nonequivalent groups with anchor test (NEAT) equating. Four of the strategies were based on significance tests of chi-square statistics (Likelihood Ratio, Pearson, Freeman-Tukey, and Cressie-Read) and four additional strategies were based on different evaluations of the Likelihood Ratio Chi-Square statistic (Akaike Information Criterion, Bayesian Information Criterion, Consistent Akaike Information Criterion, and an index traced to Goodman). The focus was the implications of the selection strategies' selection tendencies for the accuracy of chained and poststratification equating functions. The results differentiated the strategies in terms of their tendencies to select models with particular bivariate parameterizations and the implications of these tendencies for equating bias and variability .     

考试分数等值的新框架

对考试分数进行等值处理不仅是保证测验信度和公平性的重要环节,也是建立题库和实现计算机化自适应性考试的核心环节。由美国教育协会(ACE)和全美教育测量学会(NCME)联合组织编写的《教育测量》一书被称为教育测量领域中的"圣经"。在2006年出版的《教育测量》(第四版)中提出了一个关于考试分数等值的新框架。本文介绍了这一新框架,并结合作者多年从事考试分数等值的实践,对等值问题进行了讨论。     

HSK和MHK的等值

HSK是为测试母语为非汉语者(包括外国人和华侨)的汉语水平而设立的国家级标准化考试。MHK是专门测试母语为非汉语的中国少数民族汉语学习者汉语水平的国家级标准化考试。HSK和MHK都是证书考试。如果证书授予标准缺乏稳定性和公平性,如果对使用这一份试卷的人一个标准,对使用另一份试卷的人又一个标准,那么,不仅会大大影响HSK的信度和效度,而且会对有关的决策产生误导,会使考生受到不公平的对待。在HSK和MHK的开发和实施过程中,一直坚持了对考试分数的统计等值处理。在HSK和MHK的等值设计方面,我们综合采用了共同组等值、共同题等值和分半组合的混合设计。在HSK和MHK的等值数据处理方面,我们综合采用了线性等值、等百分位等值和IRT等值。本文介绍了HSK和MHK的等值方法。讨论了各种方法的得失,讨论了今后继续改进的可能性。     

On the Choice of Anchor Tests in Equating

The choice of anchor tests is crucial in applications of the nonequivalent groups with anchor test design of equating. Sinharay and Holland (2006, 2007) suggested “miditests,” which are anchor tests that are content‐representative and have the same mean item difficulty as the total test but have a smaller spread of item difficulties. Sinharay and Holland (2006, 2007), Cho, Wall, Lee, and Harris (2010), Fitzpatrick and Skorupski (2016), Liu, Sinharay, Holland, Curley, and Feigenbaum (2011a), Liu, Sinharay, Holland, Feigenbaum, and Curley (2011b), and Yi (2009) found the miditests to lead to better equating than minitests, which are representative of the total test with respect to content and difficulty. However, these findings recently came into question as Trierweiler, Lewis, and Smith (2016) concluded, based on a comparison of correlation coefficients of miditests and minitests with the total test, that making an anchor test a miditest does not generally increase the anchor to total score correlation and recommended the continuation of the practice of using minitests over miditests. Their recommendation raises the question, “Should miditests continue to be considered in practice?” This note defends the miditests by citing literature that favors miditests and then by showing that miditests perform as well as the minitests in most realistic situations considered in Trierweiler et al. (2016), which implies that miditests should continue to be seriously considered by equating practitioners.     

面向统计词义消歧的核选择研究进展

词义消歧解决自然语言中同形异义词语在不同上下文环境中的义项标注问题,是自然语言处理领域的基础性关键问题.核方法是机器学习中一类强有力的统计学习技术,被广泛应用于分类、回归、聚类等诸多领域.基于核方法的词义消歧的关键是如何构造一个能够充分表达待消歧词上下文信息的核函数.在介绍基于核方法的词义消歧系统的一般框架之后,系统阐述了国内外面向统计词义消歧的核函数构造与选择的研究现状及进展,重点分析了研究中存在的问题及解决方法,最后探讨了未来研究的重点与可能的发展方向.     

基于带宽估算的无线网络链路选择研究

在多网络环境中,移动用户在短时间内能否选择较优路径进行数据传输,直接影响用户对网络的性能体验。鉴于上述情况,以网络带宽为衡量路径优劣的基准,基于主动测量技术,在终端记录数据包分组的ACK返回时间,获取数据包之间的时间间隔,计算一定时间段内的可用带宽,带宽较大的即可视为较优路径从而进行数据传输。在测量过程中,为消除瞬时突发流量对测量结果的影响,采用统计学中非参数估计方法中的核密度估算方法,从而获得稳定带宽的值,为移动终端进入的新网络路径计算拥塞控制窗口提供计算依据,为发送大数据选择合适的发送速率,最终达到数据包的高效率传输,提高网络利用率。     

HSK和MHK测验等值分析

本研究通过随机选取2382名考生,采用共同组等值设计和线性等值法,对MHK三级与HSK三级、四级、五级、六级的考生成绩进行了等值,等值结果包括听力、阅读、书面表达各分测验分数及测验总分。