Does adaptive testing violate local independence?

Item response theory posits local independence, or conditional independence of item responses given item parameters and examinee proficiency parameters. The usual definition of local independence, however, addresses the context of fixed tests, and initially appears to yield incorrect response-pattern probabilities in the context of adaptive testing. The paradox is resolved by introducing additional notation to deal with the item selection mechanism.We are grateful to Charlie Lewis, Ming-Mei Wang, and Pao-Kuei Wu for discussions on this topic, and to the Editor, the reviewers, and Howard Wainer for helpful comments on an earlier version of the paper. The first author's work was supported in part by the National Center for Research on Evaluation, Standards, Student Testing (CRESST), Educational Research and Development Program, cooperative agreement number R117G10027 and CFDA catalog number 84.117G, as administered by the Office of Educational Research and Improvement, U.S. Department of Education.     

应用项目反应理论对瑞文测验联合型的分析

使用BILOG-MG3.0软件,边际极大似然估计,3参数Logistic模型对354名不同能力水平的男性青年的瑞文测验联合型数据进行了分析。结果显示:大多数瑞文测验联合型的题目都适合3参数Logistic模型(有6道题不适合)。整个测验的信息函数峰值的位置在难度量表的-3到-2之间,其值为16.82。共有18道题的信息函数峰值在0.2以下。从区分度来看,72道题目的区分度均大于0.5,比较理想。难度参数显示所有题目均较低,绝大部分都在0以下,最高的只有1.01。题目的难度主要由所需的操作水平决定。伪猜测参数在0.07-0.24之间。综合分析表明瑞文测验联合型对正常青年的智力评价精度较差。     

Conditional Covariance Theory and Detect for Polytomous Items

This paper extends the theory of conditional covariances to polytomous items. It has been proven that under some mild conditions, commonly assumed in the analysis of response data, the conditional covariance of two items, dichotomously or polytomously scored, given an appropriately chosen composite is positive if, and only if, the two items measure similar constructs besides the composite. The theory provides a theoretical foundation for dimensionality assessment procedures based on conditional covariances or correlations, such as DETECT and DIMTEST, so that the performance of these procedures is theoretically justified when applied to response data with polytomous items. Various estimators of conditional covariances are constructed, and special attention is paid to the case of complex sampling data, such as those from the National Assessment of Educational Progress (NAEP). As such, the new version of DETECT can be applied to response data sets not only with polytomous items but also with missing values, either by design or at random. DETECT is then applied to analyze the dimensional structure of the 2002 NAEP reading samples of grades 4 and 8. The DETECT results show that the substantive test structure based on the purposes for reading is consistent with the statistical dimensional structure for either grade. This research was supported by the Educational Testing Service and the National Assessment of Educational Progress (Grant R902F980001), US Department of Education. The opinions expressed herein are solely those of the author and do not necessarily represent those of the Educational Testing Service. The author would like to thank Ting Lu, Paul Holland, Shelby Haberman, and Feng Yu for their comments and suggestions. Requests for reprints should be sent to Jinming Zhang, Educational Testing Service, MS 02-T, Rosedale Road, Princeton, NJ 08541, USA. E-mail: jzhang@ets.org     

混合IRT潜在模型及其应用轨迹

项目反应理论是测量被试潜在特质的现代测量理论, 潜在类别分析是基于模型的潜在特质分类技术。混合项目反应理论将项目反应理论与潜在类别分析相结合, 能够同时对被试分类并量化其潜在特质。在阐述混合项目反应理论概念、原理的基础上, 介绍了MRM、mNRM和mPCM等几种常见混合模型及其参数估计方法, 并从心理与行为特征分类、项目功能差异检测、测验效度评价等方面评述了其在心理测验中的应用发展轨迹。     

Equivalent linear logistic test models

This paper is about the Linear Logistic Test Model (LLTM). We demonstrate that there are infinitely many equivalent ways to specify a model. An implication is that there may well be many ways to change the specification of a given LLTM and achieve the same improvement in model fit. To illustrate this phenomenon, we analyze a real data set using a Lagrange multiplier test for the specification of the model. This Lagrange multiplier test is similar to the modification index used in structural equation modeling.     

Marginal maximum likelihood estimation of item response theory (IRT) equating coefficients for the common-examinee design

A method of estimating item response theory (IRT) equating coefficients by the common-examinee design with the assumption of the two-parameter logistic model is provided. The method uses the marginal maximum likelihood estimation, in which individual ability parameters in a common-examinee group are numerically integrated out. The abilities of the common examinees are assumed to follow a normal distribution but with an unknown mean and standard deviation on one of the two tests to be equated. The distribution parameters are jointly estimated with the equating coefficients. Further, the asymptotic standard errors of the estimates of the equating coefficients and the parameters for the ability distribution are given. Numerical examples are provided to show the accuracy of the method.     

认知元反应理论--IRT直接应用于多值记分题

0-1记分测验的项目反应理论已经得到广泛的研究和应用.但是,许多测验都含有多值记分题,所以需要将IRT推广到此类情况.从认知理论的观点看,每个0-1记分题(项目)和多值记分题的每个测试点都可同样地看成一个由若干知识点构成的集合,称之为认知元;根据认知元之间存在的关系可以确定各受测者对各试题作出特定答案的概率,从而不需要引用任何其它假设就可将IRT的方法直接应用于含多值记分题的测验.本文应用这一理论分析了某些测验样本,结果表明是可行的.     

计算机化多阶段自适应测验研究述评

摘 要 计算机化多阶段自适应测验是基于计算机技术的测验形式,它将题目集合作为测试单元,通过多阶段自适应的形式对被试进行测试和评分。近年来通过研究各种测验形式,发现其比计算机化自适应测验和传统纸笔测验突显出更大优势。与传统纸笔测验相比,其具有参数不变性、能力估计更精确等优势。与计算机化自适应测验相比,其具有可控制题目特性、被试可检查题目等优势。如何减小测量误差,使其应用更加便捷、有效,是未来研究的发展方向。     

识字能力的单维性检验研究

本研究以义务教育阶段学生识字量测验为工具,综合运用探索性结构方程建模(ESEM)以及非参数项目反应理论中的摩根量表(Mokken量表)和DETECT分析方法,探讨了识字能力的维度。探索性结构方程建模结果显示,识字的单维性模型优于多维模型,多维的结果更多的体现出一个难度维度的特征,即字频的作用。Mokken量表分析结果显示,1~2年级和3~9年级测验更倾向于单维量表的特征。DETECT分析结果显示,两个测验的D值趋近于零,表明识字能力是单维能力。结合三种分析方法,识字能力具有单维性。     

Bayesian estimation of a multilevel IRT model using gibbs sampling

In this article, a two-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather than observed scores as dependent variables of a multilevel model is that it offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to observed scores, latent scores are test-independent, which offers the possibility of using results from different tests in one analysis where the parameters of the IRT model and the multilevel model can be concurrently estimated. The two-parameter normal ogive model is used for the IRT measurement model. It will be shown that the parameters of the two-parameter normal ogive model and the multilevel model can be estimated in a Bayesian framework using Gibbs sampling. Examples using simulated and real data are given.     

Essential independence and likelihood-based ability estimation for polytomous items

A definition ofessential independence is proposed for sequences of polytomous items. For items satisfying the reasonable assumption that the expected amount of credit awarded increases with examinee ability, we develop a theory ofessential unidimensionality which closely parallels that of Stout. Essentially unidimensional item sequences can be shown to have a unique (up to change-of-scale) dominant underlying trait, which can be consistently estimated by a monotone transformation of the sum of the item scores. In more general polytomous-response latent trait models (with or without ordered responses), anM-estimator based upon maximum likelihood may be shown to be consistent for under essentially unidimensional violations of local independence and a variety of monotonicity/identifiability conditions. A rigorous proof of this fact is given, and the standard error of the estimator is explored. These results suggest that ability estimation methods that rely on the summation form of the log likelihood under local independence should generally be robust under essential independence, but standard errors may vary greatly from what is usually expected, depending on the degree of departure from local independence. An index of departure from local independence is also proposed.This work was supported in part by Office of Naval Research Grant N00014-87-K-0277 and National Science Foundation Grant NSF-DMS-88-02556. The author is grateful to William F. Stout for many helpful comments, and to an anonymous reviewer for raising the questions addressed in section 2. A preliminary version of section 6 appeared in the author's Ph.D. thesis.     

The role of secondary covariates when estimating latent trait population distributions

The U.S. National Assessment of Educational Progress (NAEP), the Third International Mathematics and Science Study (TIMSS), and the U.S. Adult Literacy Survey collect probability samples of students (or adults) who are administered brief examinations in subject areas such as mathematics and reading (cognitive variables), along with background demographic (primary) and educational environment (secondary) questions. The demographic questions are used in the primary reporting, while the numerous explanatory secondary variables, or covariates, are only directly utilized in subsequent secondary analyses. The covariates are also used indirectly to create the plausible values (multiple imputations) that are an integral part of analyses because of the use of sparse matrix sampling of cognitive items. The improvement in the precision of the primary reporting due to the inclusion of the covariates is assessed here and contrasted with the precision of reporting using plausible values created using only the primary demographic variables.The results demonstrate that the improvement in precision depends on the matrix sampling designs for the cognitive assessments. The improvements range from essentially none for the most common designs, to moderate for some less common designs. Consequently, two potential changes in the reporting procedures that could improve the statistical and operational efficiency of primary reporting are (a) eliminate or reduce the collection of covariates and increase the number of cognitive items, (b) to avoid delays, eliminate the covariates from the creation of plausible values used for the primary reports, but include them later when creating public-use files for secondary analyses. The potential improvements in statistical and operational efficiency must be weighed against the intrinsic interest in the covariates, and the potential for small discrepancies in the primary and secondary reporting.Thanks to Donald Rubin, Robert Mislevy, and John Barnard for their helpful comments and computing assistance. This work was supported by NCES Grant 84.902B980011.     

A New Concurrent Calibration Method for Nonequivalent Group Design under Nonrandom Assignment

This study proposes a new item parameter linking method for the common-item nonequivalent groups design in item response theory (IRT). Previous studies assumed that examinees are randomly assigned to either test form. However, examinees can frequently select their own test forms and tests often differ according to examinees’ abilities. In such cases, concurrent calibration or multiple group IRT modeling without modeling test form selection behavior can yield severely biased results. We proposed a model wherein test form selection behavior depends on test scores and used a Monte Carlo expectation maximization (MCEM) algorithm. This method provided adequate estimates of testing parameters.