A Comparison of IRT Equating and Beta 4 Equating

Four equating methods (3PL true score equating, 3PL observed score equating, beta 4 true score equating, and beta 4 observed score equating) were compared using four equating criteria: first-order equity (FOE), second-order equity (SOE), conditional-mean-squared-error (CMSE) difference, and the equipercentile equating property. True score equating more closely achieved estimated FOE than observed score equating when the true score distribution was estimated using the psychometric model that was used in the equating. Observed score equating more closely achieved estimated SOE, estimated CMSE difference, and the equipercentile equating property than true score equating. Among the four equating methods, 3PL observed score equating most closely achieved estimated SOE and had the smallest estimated CMSE difference, and beta 4 observed score equating was the method that most closely met the equipercentile equating property.     

A Comparison of IRT Linking Procedures

Various applications of item response theory often require linking to achieve a common scale for item parameter estimates obtained from different groups. This article used a simulation to examine the relative performance of four different item response theory (IRT) linking procedures in a random groups equating design: concurrent calibration with multiple groups, separate calibration with the Stocking-Lord method, separate calibration with the Haebara method, and proficiency transformation. The simulation conditions used in this article included three sampling designs, two levels of sample size, and two levels of the number of items. In general, the separate calibration procedures performed better than the concurrent calibration and proficiency transformation procedures, even though some inconsistent results were observed across different simulation conditions. Some advantages and disadvantages of the linking procedures are discussed.     

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.     

A Comparison of Angoff's Design I and Design II for Vertical Equating Using Traditional and IRT Methodology

Practical considerations in conducting an equating study often require a trade-off between testing time and sample size. A counterbalanced design (Angoff's Design II) is often selected because, as each examinee is administered both test forms and therefore the errors are correlated, sample sizes can be dramatically reduced over those required by a spiraling design (Angoff's Design I), where each examinee is administered only one test form. However, the counterbalanced design may be subject to fatigue, practice, or context effects. This article investigated these two data collection designs (for a given sample size) with equipercentile and IRT equating methodology in the vertical equating of two mathematics achievement tests. Both designs and both methodologies were judged to adequately meet an equivalent expected score criterion; Design II was found to exhibit more stability over different samples.     

Comparison of Item Preequating and Random Groups Equating Using IRT and Equipercentile Methods

An item-preequating design and a random groups design were used to equate forms of the American College Testing (ACT) Assessment Mathematics Test. Equipercentile and 3-parameter logistic model item-response theory (IRT) procedures were used for both designs. Both pretest methods produced inadequate equating results, and the IRT item preequating method resulted in more equating error than had no equating been conducted. Although neither of the item preequating methods performed well, the results from the equipercentile preequating method were more consistent with those from the random groups method than were the results from the IRT item pretest method. Item context and position effects were likely responsible, at least in part, for the inadequate results for item preequating. Such effects need to be either controlled or modeled, and the design further researched before the item preequating design can be recommended for operational use.     

Evaluating the Effects of Multidimensionality on IRT True-Score Equating

The performance of the item response theory (IRT) true-score equating method is examined under conditions of test multidimensionality. It is argued that a primary concern in applying unidimensional equating methods when multidimensionality is present is the potential decrease in equity (Lord, 1980) attributable to the fact that examinees of different ability are expected to obtain the same test scores. In contrast to equating studies based on real test data, the use of simulation in equating research not only permits assessment of these effects but also enables investigation of hypothetical equating conditions in which multidimensionality can be suspected to be especially problematic for test equating. In this article, I investigate whether the IRT true-score equating method, which explicitly assumes the item response matrix is unidimensional, is more adversely affected by the presence of multidimensionality than 2 conventional equating methods-linear and equipercentile equating-using several recently proposed equity-based criteria (Thomasson, 1993). Results from 2 simulation studies suggest that the IRT method performs at least as well as the conventional methods when the correlation between dimensions is high (³ 0.7) and may be only slightly inferior to the equipercentile method when the correlation is moderate to low (£ 0.5).     

The Effect of Multidimensionality on IRT True-Score Equating for Subgroups of Examinees

The purpose of this study was to assess the dimensionality of two forms of a large-scale standardized test separately for 3 ethnic groups of examinees and to investigate whether differences in their latent trait composites have any impact on unidimensional item response theory true-score equating functions. Specifically, separate equating functions for African American and Hispanic examinees were compared to those of a Caucasian group as well as the total test taker population. On both forms, a 2-dimensional model adequately accounted for the item responses of Caucasian and African American examinees, whereas a more complex model was required for the Hispanic subgroup. The differences between equating functions for the 3 ethnic groups and the total test taker population were small and tended to be located at the low end of the score scale.     

大规模测评中IRT等值的影响因素研究

通过模拟和实证研究探讨样本量、题本量以及锚题题型对大尺度测评中项目参数等值精度的影响,模拟研究和实证研究的结果均表明:(1)0/1计分项目参数的等值精度在大多数条件下均好于多级计分项目,相对而言,实证研究的差异不如模拟研究明显;(2)相对而言,样本容量的增加对于提高项目参数等值精度有着重要的作用,而增加题本数量的作用甚微;(3)无论是区分度参数还是难度参数,均表现为3个题本和2 000人的搭配已经可以达到较好的等值精度,如果进一步提高等值精度,只需将每一题本的样本容量增加到3 000人即可;在多级计分时,当选用5个题本时,每一个题本2 000人是最适宜的组合。     

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。     

A Comparison of Tests for Equality of Two or More Independent Alpha Coefficients

This article extends the Bonett (2003a) approach to testing the equality of alpha coefficients from two independent samples to the case of m ≥ 2 independent samples. The extended Fisher-Bonett test and its competitor, the Hakstian-Whalen (1976) test, are illustrated with numerical examples of both hypothesis testing and power calculation. Computer simulations are used to compare the performance of the two tests and the Feldt (1969) test (for m = 2) in terms of power and Type I error control. It is shown that the Fisher-Bonett test is just as effective as its competitors in controlling Type I error, is comparable to them in power, and is equally robust against heterogeneity of error variance.     

Lord's Wald Test for Detecting DIF in Multidimensional IRT Models: A Comparison of Two Estimation Approaches

Lord's Wald test for differential item functioning (DIF) has not been studied extensively in the context of the multidimensional item response theory (MIRT) framework. In this article, Lord's Wald test was implemented using two estimation approaches, marginal maximum likelihood estimation and Bayesian Markov chain Monte Carlo estimation, to detect uniform and nonuniform DIF under MIRT models. The Type I error and power rates for Lord's Wald test were investigated under various simulation conditions, including different DIF types and magnitudes, different means and correlations of two ability parameters, and different sample sizes. Furthermore, English usage data were analyzed to illustrate the use of Lord's Wald test with the two estimation approaches.     

考生能力分布与被试量对IRT等值的影响

基于项目反应理论中的LOGISTIC双参数模型研究共同题非等组设计下,考生能力分布与被试量对等值的影响。等值方法采用分别校准下的项目特征曲线法、Stocking-Lord法、Haebara法。等值结果采用等值分数标准误、等值系数标准误、共同题参数稳定性三种方法进行评价。研究结果表明,考生能力分布越接近,被试量越大,等值误差越小;且Stocking-Lord法较Haebara法的等值结果更稳定。     

A Comparison of Item Fit Statistics for Mixed IRT Models

In this study we examined procedures for assessing model-data fit of item response theory (IRT) models for mixed format data. The model fit indices used in this study include PARSCALE's G² , Orlando and Thissen's S − X² and S − G² , and Stone's χ²* and G²* . To investigate the relative performance of the fit statistics at the item level, we conducted two simulation studies: Type I error and power studies. We evaluated the performance of the item fit indices for various conditions of test length, sample size, and IRT models. Among the competing measures, the summed score-based indices S − X² and S − G² were found to be the sensible and efficient choice for assessing model fit for mixed format data. These indices performed well, particularly with short tests. The pseudo-observed score indices, χ²* and G²* , showed inflated Type I error rates in some simulation conditions. Consistent with the findings of current literature, the PARSCALE's G² index was rarely useful, although it provided reasonable results for long tests.     

IRT理论不同模型下同时校准等值方法的跨样本研究

本研究基于IRT理论中最常用的LOGISTIC三种模型来探讨等值的跨样本一致性,研究对象为某一汉语类别的测验,等值方法采用同时校准法。研究结果表明,双参数模型下同时校准法等值跨样本一致性最好,最为稳定。     

Asymptotic Standard Errors of Observed‐Score Equating With Polytomous IRT Models

In observed‐score equipercentile equating, the goal is to make scores on two scales or tests measuring the same construct comparable by matching the percentiles of the respective score distributions. If the tests consist of different items with multiple categories for each item, a suitable model for the responses is a polytomous item response theory (IRT) model. The parameters from such a model can be utilized to derive the score probabilities for the tests and these score probabilities may then be used in observed‐score equating. In this study, the asymptotic standard errors of observed‐score equating using score probability vectors from polytomous IRT models are derived using the delta method. The results are applied to the equivalent groups design and the nonequivalent groups design with either chain equating or poststratification equating within the framework of kernel equating. The derivations are presented in a general form and specific formulas for the graded response model and the generalized partial credit model are provided. The asymptotic standard errors are accurate under several simulation conditions relating to sample size, distributional misspecification and, for the nonequivalent groups design, anchor test length.     

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 of Estimation Techniques for IRT Models With Small Samples

The usefulness of item response theory (IRT) models depends, in large part, on the accuracy of item and person parameter estimates. For the standard 3 parameter logistic model, for example, these parameters include the item parameters of difficulty, discrimination, and pseudo-chance, as well as the person ability parameter. Several factors impact traditional marginal maximum likelihood (ML) estimation of IRT model parameters, including sample size, with smaller samples generally being associated with lower parameter estimation accuracy, and inflated standard errors for the estimates. Given this deleterious impact of small samples on IRT model performance, use of these techniques with low-incidence populations, where it might prove to be particularly useful, estimation becomes difficult, especially with more complex models. Recently, a Pairwise estimation method for Rasch model parameters has been suggested for use with missing data, and may also hold promise for parameter estimation with small samples. This simulation study compared item difficulty parameter estimation accuracy of ML with the Pairwise approach to ascertain the benefits of this latter method. The results support the use of the Pairwise method with small samples, particularly for obtaining item location estimates.     

应用项目反应理论等值含有多种题型考试的一个实例

本文以美国一个州的高中统考为例介绍应用项目反应理论来对含有多种题型的考试进行等值处理的具体做法,同时也对考试的其他技术环节进行了一些探讨。     

云计算中两类主流数据库的比较研究

主要研究了Bigtable和HBase两个云计算的主流数据库;从数据模型的比较可以看出它们都采用了行、列和时间戳三要素;系统的主要架构也基本相似,只是在实现中有细微的差别,表现在对列族的处理上,Bigtable采用了锁的方式,而HBase则采用了节点方式来完成数据的协同访问;在性能的比较中可以知道,Bigtable的性能明显优于HBase,尤其在如数据错误安全和数据中心间的数据复制等关键性能是HBase所不能支持的;但由于HBase的开源性,使得用户可以获得更多的自由,方便开发个性应用。     

A Comparison of Six Methods for Combining Multiple IRT Item Parameter Estimates

Six procedures for combining sets of IRT item parameter estimates obtained from different samples were evaluated using real and simulated response data. In the simulated data analyses, true item and person parameters were used to generate response data for three different-sized samples. Each sample was calibrated separately to obtain three sets of item parameter estimates for each item. The six procedures for combining multiple estimates were each applied, and the results were evaluated by comparing the true and estimated item characteristic curves. For the real data, the two best methods from the simulation data analyses were applied to three different-sized samples and the resulting estimated item characteristic curves were compared to the curves obtained when the three samples were combined and calibrated simultaneously. The results support the use of covariance matrix-weighted averaging and a procedure that involves sample-size-weighted averaging of estimated item characteristic curves at the center of the ability distribution