铆题比例对等值精度的影响

在非等组铆测验设计中,铆题量占测验长度的多大比例比较合适,这个比例随测验长度的增大可否发生变化？这些是实际工作者和研究者非常关心的问题。该文在固定被试数和测验长度的条件下,探查铆题量所占测验长度比例（简称铆题比例）的变化对等值精度的影响,讨论了在实际等值中如何在等值精度和铆题比例之间取得平衡的问题。并在模拟研究的条件下,给出了几个反应实际等值精度的指标。     

我国一大型考试等值的铆题参数漂移检验

设置铆题来链接不同测验形式是一种常用的等值设计。但受到曝光等因素影响,铆题功能在不同施测时间会发生改变。本研究采用MH检验和logistic回归考察我国一大型考试等值的铆题质量,结果发现,有22个铆题发生参数漂移,铆题的难度参数和区分度参数可能发生漂移;这些铆题中大部分在二次使用时无法通过模型拟合检验;若不删除参数发生漂移的铆题导致较大的系统等值误差,应将铆题参数漂移检验作为等值中的一步必要工作。     

不同铆测验设计下多维IRT等值方法的比较

实际应用中测验往往具有多维结构, 如果仍采用单维IRT方法进行等值, 会得到不准确的结果。因此对于多维结构的测验, 需要使用多维IRT等值方法来实现参数的转换。基于共同题设计, 文章通过模拟研究的方法, 考察了不同铆测验设计下几种多维IRT等值方法的表现, 同时考虑了测验长度、两个维度题目数量的比例、铆测验长度、铆测验的选择策略、两个维度之间的相关和等值群体的能力水平差异六个因素的影响。所比较的多维IRT等值方法有：均值/均值(MM)方法, 均值/标准差(MS)方法, Stoking-Lord (SL)方法, Haebara (HB)方法, 最小平方(LS)方法。结果显示：(1) SL, HB和LS方法得到的等值误差均方根最小, 且在各条件下表现较为稳定。(2) MM和MS方法在非等组条件下呈现出很大的误差均方根。(3)铆测验设计对SL, HB和LS方法的等值结果没有显著影响。(4)在两个维度之间的相关较高, 测验长度和铆测验长度较长, 等值群体的能力水平没有差异的条件下, SL, HB和LS方法得到的等值误差均方根最小。     

高中会考等值方法的比较研究

本研究采用随机等组设计与铆测验相结合的方案。首先验证了两随机等组的平均数、方差和分布状态无显著差异,再用随机等组的等值分作为等值效标来检验其他等值方法的误差,然后比较了在铆测验设计中三种线性等值方法（在不同总体权重下）的误差值,以选出适合高中合考的等值方法及总体权重。经研究发现：会考等值宜采用Tucker观察分数线性等值方法,并宜选择总体权重W1＝1。     

大学英语四、六级考试分数等值研究

对现有的大学英语四、六级考试分数等值模式中存在的若干问题进行了深入的分析,并提出了新的解决方案——一个基于铆题设计和两参数IRT模型的解决方案。主要包括：（1）用两参数逻辑斯蒂模型替代原来的Rasch模型,以改进题目模型的适合性;（2）用共同题目的等值设计取代原来的共同被试等值设计,解决共同被试等值设计中,等值考生的动机水平难以控制的难题;（3）建立专用的等值用题库,并且一次性完成其中铆题的预测和参数标定工作,以解决原来等值模式中存在的误差累积问题。同时,由于铆题的保密工作难度较小,因此,等值专用题库对保证等值结果的可靠性也具有重大意义;（4）本文还对新的分数等值方案进行了真实的考试数据等值计算实验,并得到了一个令人满意的分数等值结果。     

含题组的测验等值

题组越来越多地出现在各类考试中, 采用标准的IRT模型对有题组的测验等值, 可能因忽略题组的局部相依性导致等值结果的失真。为解决此问题, 我们采用基于题组的2PTM模型及IRT特征曲线法等值, 以等值系数估计值的误差大小作为衡量标准, 以Wilcoxon符号秩检验为依据, 在几种不同情况下进行了大量的Monte Carlo模拟实验。实验结果表明, 考虑了局部相依性的题组模型2PTM绝大部分情况下都比2PLM等值的误差小且有显著性差异。另外, 用6种不同等值准则对2PTM等值并评价了不同条件下等值准则之间的优劣。     

题目难度分布和样本容量对两种CTT等值结果的影响

基于经典测验理论(CTT)的等值方法主要有线性等值和等百分位等值两种。在不同情境下,不同的等值方法会产生不同的等值结果。本研究以真分数等值为依据,用蒙特卡洛模拟研究方法,综合比较了各种题目难度分布条件下和各种样本容量条件下两种CTT等值方法的等值结果。研究结果表明:(1)线性等值的误差受题目难度分布影响较大,等百分位等值的误差几乎不受题目难度分布影响。(2)线性等值的误差几乎不受样本容量的影响,等百分位等值的误差受样本容量影响较大。(3)不论题目难度分布如何,只要样本容量足够大,等百分位等值的效果都比线性等值更好。     

基于多维题组反应模型的项目功能差异检验探究

本文将多维题组反应模型（MTRM）应用到多维题组测验的项目功能差异（DIF）检验中,通过模拟研究和应用研究探究MTRM在DIF检验中的准确性、有效性和影响因素,并与忽略题组效应的多维随机系数多项Logistic模型（MRCMLM）进行对比。结果表明：（1）随着样本量的增大,MTRM对有效DIF值检出率增高,错误率降低,在不同条件下结果的稳定性更高;（2）与MRCMLM相比,基于MTRM的DIF检验模型检验率更高,受到其他因素的影响更小;（3）当测验中题组效应较小时,MTRM与MRCMLM结果差异较小,但是MTRM模型拟合度更高。     

基于RSM对Q矩阵相同的无锚题测验的等值

传统锚题-非等组设计下的测验等值,等值要求的满足具有主观性,并且由于锚题失效或难以获得等因素的影响,则该方法的使用受到了限制。因此,本研究基于规则空间模型的Q矩阵理论,生成两个Q矩阵相同但无锚题的测验的共同受测者,使用共同组设计,利用同时性估计的方法对测验进行等值,并考虑了作答失误率和测验结构对等值稳定性的影响。结果表明:共同组设计同时估计方法的等值稳定性取得了优于或等于锚题-非等组同时估计方法;失误率的增大也会导致等值稳定性的下降;并且不同的测验结构也对等值稳定性产生了影响,其中直线型和收敛型结构稳定性较好,发散型和无结构型较差。     

经典测量理论等值的误差研究

1　引言　　等值 ,是以铆测验或铆被试组为桥梁建立两份同特质测验结果之间的比较关系。许多因素会影响等值的准确性 ,由于被试抽样给等值带来的误差叫等值抽样误差。它指的是 ,由于等值所用被试样本是从其总体中进行了不可避免的有一定程度偏性的抽样而得到的 ,据此建立的等值关系也就具有一定程度的偏差 ,这种偏差即是等值抽样误差。通过从总体中重复抽样、以一个完全拟合数据条件的等值方法进行等值 ,那么 ,等值结果分布的平均数即是真正的等值分数 ,而分布的标准差即是等值抽样标准误。本文将对等值抽样误差问题进行探讨。2　研究方法2 …     

The Missing Data Assumptions of the NEAT Design and their Implications for Test Equating

The Non-Equivalent groups with Anchor Test (NEAT) design involves missing data that are missing by design. Three nonlinear observed score equating methods used with a NEAT design are the frequency estimation equipercentile equating (FEEE), the chain equipercentile equating (CEE), and the item-response-theory observed-score-equating (IRT OSE). These three methods each make different assumptions about the missing data in the NEAT design. The FEEE method assumes that the conditional distribution of the test score given the anchor test score is the same in the two examinee groups. The CEE method assumes that the equipercentile functions equating the test score to the anchor test score are the same in the two examinee groups. The IRT OSE method assumes that the IRT model employed fits the data adequately, and the items in the tests and the anchor test do not exhibit differential item functioning across the two examinee groups. This paper first describes the missing data assumptions of the three equating methods. Then it describes how the missing data in the NEAT design can be filled in a manner that is coherent with the assumptions made by each of these equating methods. Implications on equating are also discussed.     

测验链接中的锚题代表性研究

本文旨在以“锚题代表性”这一研究命题切入,探索在非等组锚测验设计下,作为实现测验链接的重要载体,锚题和相关的测验试卷/水平之间究竟应该有什么关系。本文首先指出锚题代表性这一概念在等值和垂直量尺化领域中具有不同的含义,并给出其在垂直量尺化中的含义。通过考察测验链接中有关锚题代表性的既有研究,系统总结相关研究成果,本文概括出了当前锚题构建实践的可能优化方案,分析了锚题代表性研究的未来方向。     

基于CTT的锚测验非等组设计中四种等值方法的比较研究

采用锚测验非等组设计的数据收集方案,对4种基于经典测量理论的等值方法进行了比较研究。研究数据取自TIMSS1999数据库,兼用等值标准误和交叉验证方法作为各等值方法比较的检验标准,利用CIPE程序对实验数据进行分析。研究结果表明,针对本研究所设置的等值情境,线性等值优于等百分位等值,其中Tucker线性方法比Levine观察分数线性方法更好一些,Braun-Holland线性方法不宜采用,频数估计等百分位方法等值误差较大,亦不足取。     

Equipercentile equating via data-imputation techniques

In the design of common-item equating, two groups of examinees are administered separate test forms, and each test form contains a common subset of items. We consider test equating under this situation as an incomplete data problem—that is, examinees have observed scores on one test form and missing scores on the other. Through the use of statistical data-imputation techniques, the missing scores can be replaced by reasonable estimates, and consequently the forms may be directly equated as if both forms were administered to both groups. In this paper we discuss different data-imputation techniques that are useful for equipercentile equating; we also use empirical data to evaluate the accuracy of these techniques as compared with chained equipercentile equating.A paper presented at the European Meeting of the Psychometric Society, Barcelona, Spain, July, 1993.     

New Equating Methods and Their Relationships with Levine Observed Score Linear Equating Under the Kernel Equating Framework

In this paper, we develop a new curvilinear equating for the nonequivalent groups with anchor test (NEAT) design under the assumption of the classical test theory model, that we name curvilinear Levine observed score equating. In fact, by applying both the kernel equating framework and the mean preserving linear transformation of post-stratification equating, we obtain a family of observed score equipercentile equating functions, which also includes the classical Levine observed score linear equating and the Tucker linear equating as special cases.     

Tau-equivalence and equipercentile equating

Test scores that are not perfectly reliable cannot be strictly equated unless they are strictly parallel [Lord, 1980]. This fact implies that tau-equivalence can be lost if an equipercentile equating is applied to observed scores that are not strictly parallel. Seventy-two simulated testing conditions are produced to simulate equating tests with different difficulties and discriminations. Number-correct and trait metrics are examined. When an equipercentile equating is applied to these data, locally biased (i.e., non-tau-equivalent) results are produced for tests of unequal difficulty. Differences between the criteria of tau-equivalence and equipercentile equivalence are discussed.     

Accumulative Equating Error after a Chain of Linear Equatings

After many equatings have been conducted in a testing program, equating errors can accumulate to a degree that is not negligible compared to the standard error of measurement. In this paper, the author investigates the asymptotic accumulative standard error of equating (ASEE) for linear equating methods, including chained linear, Tucker, and Levine, under the nonequivalent groups with anchor test (NEAT) design. A recursive formula for the ASEE is provided for a series of equatings that makes use of only historical summary statistics. This formula can serve as a new tool to measure the magnitude of equating errors that have accumulated over a series of equatings, and to help monitor and design testing programs.     

Analytic smoothing for equipercentile equating under the common item nonequivalent populations design

A cubic spline method for smoothing equipercentile equating relationships under the common item nonequivalent populations design is described. Statistical techniques based on bootstrap estimation are presented that are designed to aid in choosing an equating method/degree of smoothing. These include: (a) asymptotic significance tests that compare no equating and linear equating to equipercentile equating; (b) a scheme for estimating total equating error and for dividing total estimated error into systematic and random components. The smoothing technique and statistical procedures are explored and illustrated using data from forms of a professional certification test.