多维项目反应理论在数学素养测验中的应用

学生的数学素养具有多维结构,素养导向的数学学业成就测评需要提供被试在各维度上的表现信息,而不仅是一个单一的总分。以PISA数学素养结构为理论模型,以多维项目反应理论(MIRT)为测量模型,利用R语言的MIRT程序包处理和分析某地区8年级数学素养测评题目数据,研究数学素养的多维测量方法。结果表明:MIRT兼具单维项目反应理论和因子分析的优点,利用其可对测试的结构效度和测试题目质量进行分析,以及对被试进行多维能力认知诊断。     

A Multidimensional Item Response Theory Model for Continuous and Graded Responses With Error in Persons and Items

Item response theory “dual” models (DMs) in which both items and individuals are viewed as sources of differential measurement error so far have been proposed only for unidimensional measures. This article proposes two multidimensional extensions of existing DMs: the M-DTCRM (dual Thurstonian continuous response model), intended for (approximately) continuous responses, and the M-DTGRM (dual Thurstonian graded response model), intended for ordered-categorical responses (including binary). A rationale for the extension to the multiple-content-dimensions case, which is based on the concept of the multidimensional location index, is first proposed and discussed. Then, the models are described using both the factor-analytic and the item response theory parameterizations. Procedures for (a) calibrating the items, (b) scoring individuals, (c) assessing model appropriateness, and (d) assessing measurement precision are finally discussed. The simulation results suggest that the proposal is quite feasible, and an illustrative example based on personality data is also provided. The proposals are submitted to be of particular interest for the case of multidimensional questionnaires in which the number of items per scale would not be enough for arriving at stable estimates if the existing unidimensional DMs were fitted on a separate-scale basis.     

基于项目反应理论的测验编制方法研究

本文在简单介绍项目反应理论的基础上,从计量分析的角度,深入探讨了应用项目反应理论编制各种测验的一般步骤;探讨了项目反应理论题库建设方法及基于题库的测验编制方法;探讨了标准参照测验合格分数线的划分方法。     

基于题目反应理论的网络自适应考试

随着计算机的普及、网络的发展、教学和考试测评理论的更新,一种基于题目反应理论的计算机自适应考试已经越来越普及,它以其题目适应不同能力学生水平自动变化的特点,已经被越来越多的考试所采用,针对题目反应理论,需要对自适应考试实现等问题加以论述。     

对IRT局部独立性假设问题的认识

本文对项目反应理论（IRT）局部独立性问题的主要研究成果进行了文献梳理。在此基础上,阐释局部独立性假设的定义。文章同时就局部独立性与测验维度的关系,局部依赖的甄别与计算、起因和控制程序,以及局部依赖对测量实践的影响进行讨论,并探讨了题组中局部题目依赖问题的解决策略。     

Improving the Measurement of School Climate Using Item Response Theory

The U.S. government has become increasingly focused on school climate, as recently evidenced by its inclusion as an accountability indicator in the Every Student Succeeds Act. Yet, there remains considerable variability in both conceptualizing and measuring school climate. To better inform the research and practice related to school climate and its measurement, we leveraged item response theory (IRT), a commonly used psychometric approach for the design of achievement assessments, to create a parsimonious measure of school climate that operates across varying individual characteristics. Students (n = 69,513) in 111 secondary schools completed a school climate assessment focused on three domains of climate (i.e., safety, engagement, and environment), as defined by the U.S. Department of Education. Item and test characteristics were estimated using the mirt package in R using unidimensional IRT. Analyses revealed measurement difficulties that resulted in a greater ability to assess less favorable perspectives on school climate. Differential item functioning analyses indicated measurement differences based on student academic success. These findings support the development of a broad measure of school climate but also highlight the importance of work to ensure precision in measuring school climate, particularly when considering use as an accountability measure.     

Minimizing the Influence of Item Parameter Estimation Errors in Test Development: A Comparison of Three Selection Procedures

In test development, item response theory (IRT) is a method to determine the amount of information that each item (i.e., item information function) and combination of items (i.e., test information function) provide in the estimation of an examinee's ability. Studies investigating the effects of item parameter estimation errors over a range of ability have demonstrated an overestimation of information when the most discriminating items are selected (i.e., item selection based on maximum information). In the present study, the authors examined the influence of item parameter estimation errors across 3 item selection methods—maximum no target, maximum target, and theta maximum—using the 2- and 3-parameter logistic IRT models. Tests created with the maximum no target and maximum target item selection procedures consistently overestimated the test information function. Conversely, tests created using the theta maximum item selection procedure yielded more consistent estimates of the test information function and, at times, underestimated the test information function. Implications for test development are discussed.     

项目反应理论在中考命题质量评价中的应用

应用项目反应理论对中考命题质量进行分析,可以排除抽样干扰,准确评估试题的难度,客观精细地描述试题的区分度,评估整套试卷和各试题对学生能力估计的精度,查找赋分标准和阅卷过程中存在的问题。     

用项目反应理论修订教学效能感量表

采用随机整群抽样抽取505名中小学教师作为被试,其中,男教师189名,女教师271名,年龄均在25至55岁之间。采用教学效能感问卷进行施测,基于项目反应理论,对测试结果进行分析,得出所有项目的区分度、难度和项目信息峰值,参考项目区分度、难度及项目信息函数峰值对教学效能感量表做了修订,再运用结构方程模型、层面理论技术和最小空间分析对修订后的量表进行质量检验,结果表明修订后的量表测量拥有更为清晰的结构效度和更高的信度,测量更为精确。运用SPSS15.0管理数据,运用Hudap6.0和MULTILOG 7.03分析数据,研究得出如下五个结论:1)教学效能感量表为单一维度,可以使用项目反应理论进行分析;2)修订后的量表项目的区分度、难度更为合理;3)修订后的量表的测验信息峰值较原量表稍低;4)修订前后量表对应层面元素之间存在高相关;5)量表的三个方面内容结构得以证实,即学生品德行为教育、课堂组织管理和知识传授。     

Performance of the S[... formula ...]2 Statistic for the Multidimensional Graded Response Model

S−χ2 is a popular item fit index that is available in commercial software packages such as flexMIRT. However, no research has systematically examined the performance of S−χ2 for detecting item misfit within the context of the multidimensional graded response model (MGRM). The primary goal of this study was to evaluate the performance of S−χ2 under two practical misfit scenarios: first, all items are misfitting due to model misspecification, and second, a small subset of items violate the underlying assumptions of the MGRM. Simulation studies showed that caution should be exercised when reporting item fit results of polytomous items using S−χ2 within the context of the MGRM, because of its inflated false positive rates (FPRs), especially with a small sample size and a long test. S−χ2 performed well when detecting overall model misfit as well as item misfit for a small subset of items when the ordinality assumption was violated. However, under a number of conditions of model misspecification or items violating the homogeneous discrimination assumption, even though true positive rates (TPRs) of S−χ2 were high when a small sample size was coupled with a long test, the inflated FPRs were generally directly related to increasing TPRs. There was also a suggestion that performance of S−χ2 was affected by the magnitude of misfit within an item. There was no evidence that FPRs for fitting items were exacerbated by the presence of a small percentage of misfitting items among them.     

非参数项目反应理论模型及其在教育考试中的应用

非参数项目反应理论模型包括单调均匀性模型和双单调模型。用单调均匀性模型对某英语听力考试结果研究发现,按照顺序选择法,可从16道听力试题中选出11道满足要求的试题,组成单维量表。用考生在这11道试题上的总得分对考生进行排序与按照潜质排序等效。利用双单调模型对11道听力试题组成的单维量表进行试题功能偏差研究发现,有5道试题在女生子群体中的排序与在男生子群体以及整个群体排序不同,显示女生子群体作出正确应答的概率明显高于男生子群体作出正确应答的概率。这种差异至少部分是由两个子群体听力能力上的差异引起的。     

An NCME Instructional Module on Polytomous Item Response Theory Models

A polytomous item is one for which the responses are scored according to three or more categories. Given the increasing use of polytomous items in assessment practices, item response theory (IRT) models specialized for polytomous items are becoming increasingly common. The purpose of this ITEMS module is to provide an accessible overview of polytomous IRT models. The module presents commonly encountered polytomous IRT models, describes their properties, and contrasts their defining principles and assumptions. After completing this module, the reader should have a sound understating of what a polytomous IRT model is, the manner in which the equations of the models are generated from the model's underlying step functions, how widely used polytomous IRT models differ with respect to their definitional properties, and how to interpret the parameters of polytomous IRT models.     

Using Response Time to Detect Item Preknowledge in Computer‐Based Licensure Examinations

This article addresses the issue of how to detect item preknowledge using item response time data in two computer‐based large‐scale licensure examinations. Item preknowledge is indicated by an unexpected short response time and a correct response. Two samples were used for detecting item preknowledge for each examination. The first sample was from the early stage of the operational test and was used for item calibration. The second sample was from the late stage of the operational test, which may feature item preknowledge. The purpose of this research was to explore whether there was evidence of item preknowledge and compromised items in the second sample using the parameters estimated from the first sample. The results showed that for one nonadaptive operational examination, two items (of 111) were potentially exposed, and two candidates (of 1,172) showed some indications of preknowledge on multiple items. For another licensure examination that featured computerized adaptive testing, there was no indication of item preknowledge or compromised items. Implications for detected aberrant examinees and compromised items are discussed in the article.     

Modeling Growth in Electronic Learning Environments Using a Longitudinal Random Item Response Model

In educational environments, monitoring persons' progress over time may help teachers to evaluate the effectiveness of their teaching procedures. Electronic learning environments are increasingly being used as part of formal education and resulting datasets can be used to understand and to improve the environment. This study presents longitudinal models based on the item response theory (IRT) for measuring persons' ability within and between study sessions in data from web-based learning environments. Two empirical examples are used to illustrate the presented models. Results show that by incorporating time spent within- and between-study sessions into an IRT model; one is able to track changes in ability of a population of persons or for groups of persons at any time of the learning process.     

测验项目编制与等值的一种有效策略——层面理论

回转翻译法关注的是“文字等价”,项目反应理论注重“统计指标等价”。层面理论项目等价注重项目的同一测量目标,即等值的项目应该在相同的条件下测试被试相同的反应。层面理论通过映射语句技术清晰地界定项目的测量目标,使得项目等值与项目编制更加科学。通过层面理论编制的项目维度结构更加清楚,测验的结构效度更有保证。将层面理论和心理计量学的其他方法结合起来,可以有效提高测验项目编制与等值的质量。     

Application of the Bi-Factor Multidimensional Item Response Theory Model to Testlet-Based Tests

Four item response theory (IRT) models were compared using data from tests where multiple items were grouped into testlets focused on a common stimulus. In the bi-factor model each item was treated as a function of a primary trait plus a nuisance trait due to the testlet; in the testlet-effects model the slopes in the direction of the testlet traits were constrained within each testlet to be proportional to the slope in the direction of the primary trait; in the polytomous model the item scores were summed into a single score for each testlet; and in the independent-items model the testlet structure was ignored. Using the simulated data, reliability was overestimated somewhat by the independent-items model when the items were not independent within testlets. Under these nonindependent conditions, the independent-items model also yielded greater root mean square error (RMSE) for item difficulty and underestimated the item slopes. When the items within testlets were instead generated to be independent, the bi-factor model yielded somewhat higher RMSE in difficulty and slope. Similar differences between the models were illustrated with real data.     

Assessing the Sensitivity of Weighted Least Squares Model Fit Indexes to Local Dependence in Item Response Theory Models

Given the relationships of item response theory (IRT) models to confirmatory factor analysis (CFA) models, IRT model misspecifications might be detectable through model fit indexes commonly used in categorical CFA. The purpose of this study is to investigate the sensitivity of weighted least squares with adjusted means and variance (WLSMV)-based root mean square error of approximation, comparative fit index, and Tucker–Lewis Index model fit indexes to IRT models that are misspecified due to local dependence (LD). It was found that WLSMV-based fit indexes have some functional relationships to parameter estimate bias in 2-parameter logistic models caused by violations of LD. Continued exploration into these functional relationships and development of LD-detection methods based on such relationships could hold much promise for providing IRT practitioners with global information on violations of local independence.     

Parameter Estimation Accuracy of the Effort-Moderated Item Response Theory Model Under Multiple Assumption Violations

As low-stakes testing contexts increase, low test-taking effort may serve as a serious validity threat. One common solution to this problem is to identify noneffortful responses and treat them as missing during parameter estimation via the effort-moderated item response theory (EM-IRT) model. Although this model has been shown to outperform traditional IRT models (e.g., two-parameter logistic [2PL]) in parameter estimation under simulated conditions, prior research has failed to examine its performance under violations to the model’s assumptions. Therefore, the objective of this simulation study was to examine item and mean ability parameter recovery when violating the assumptions that noneffortful responding occurs randomly (Assumption 1) and is unrelated to the underlying ability of examinees (Assumption 2). Results demonstrated that, across conditions, the EM-IRT model provided robust item parameter estimates to violations of Assumption 1. However, bias values greater than 0.20 SDs were observed for the EM-IRT model when violating Assumption 2; nonetheless, these values were still lower than the 2PL model. In terms of mean ability estimates, model results indicated equal performance between the EM-IRT and 2PL models across conditions. Across both models, mean ability estimates were found to be biased by more than 0.25 SDs when violating Assumption 2. However, our accompanying empirical study suggested that this biasing occurred under extreme conditions that may not be present in some operational settings. Overall, these results suggest that the EM-IRT model provides superior item and equal mean ability parameter estimates in the presence of model violations under realistic conditions when compared with the 2PL model.     

A Second-Order Longitudinal Model for Binary Outcomes: Item Response Theory Versus Structural Equation Modeling

Measuring academic growth, or change in aptitude, relies on longitudinal data collected across multiple measurements. The National Educational Longitudinal Study (NELS:88) is among the earliest, large-scale, educational surveys tracking students’ performance on cognitive batteries over 3 years. Notable features of the NELS:88 data set, and of almost all repeated measures educational assessments, are (a) the outcome variables are binary or at least categorical in nature; and (b) a set of different items is given at each measurement occasion with a few anchor items to fix the measurement scale. This study focuses on the challenges related to specifying and fitting a second-order longitudinal model for binary outcomes, within both the item response theory and structural equation modeling frameworks. The distinctions between and commonalities shared between these two frameworks are discussed. A real data analysis using the NELS:88 data set is presented for illustration purposes.