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。     

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。     

Using Multidimensional Item Response Theory to Evaluate Educational and Psychological Tests

Many educational and psychological tests are inherently multidimensional, meaning these tests measure two or more dimensions or constructs. The purpose of this module is to illustrate how test practitioners and researchers can apply multidimensional item response theory (MIRT) to understand better what their tests are measuring, how accurately the different composites of ability are being assessed, and how this information can be cycled back into the test development process. Procedures for conducting MIRT analyses–from obtaining evidence that the test is multidimensional, to modeling the test as multidimensional, to illustrating the properties of multidimensional items graphically-are described from both a theoretical and a substantive basis. This module also illustrates these procedures using data from a ninth-grade mathematics achievement test. It concludes with a discussion of future directions in MIRT research.     

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。     

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。     

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.     

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.     

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.     

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.     

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。     

Estimating High School GPA Weighting Parameters With a Graded Response Model

The high school grade point average (GPA) is often adjusted to account for nominal indicators of course rigor, such as “honors” or “advanced placement.” Adjusted GPAs—also known as weighted GPAs—are frequently used for computing students’ rank in class and in the college admission process. Despite the high stakes attached to GPA, weighting policies vary considerably across states and high schools. Previous methods of estimating weighting parameters have used regression models with college course performance as the dependent variable. We discuss and demonstrate the suitability of the graded response model for estimating GPA weighting parameters and evaluating traditional weighting schemes. In our sample, which was limited to self‐reported performance in high school mathematics courses, we found that commonly used policies award more than twice the bonus points necessary to create parity for standard and advanced courses.     

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.     

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.     

An NCME Instructional Module on Item‐Fit Statistics for Item Response Theory Models

Drawing valid inferences from item response theory (IRT) models is contingent upon a good fit of the data to the model. Violations of model‐data fit have numerous consequences, limiting the usefulness and applicability of the model. This instructional module provides an overview of methods used for evaluating the fit of IRT models. Upon completing this module, the reader will have an understanding of traditional and Bayesian approaches for evaluating model‐data fit of IRT models, the relative advantages of each approach, and the software available to implement each method.     

An NCME Instructional Module on Latent DIF Analysis Using Mixture Item Response Models

The purpose of this ITEMS module is to provide an introduction to differential item functioning (DIF) analysis using mixture item response models. The mixture item response models for DIF analysis involve comparing item profiles across latent groups, instead of manifest groups. First, an overview of DIF analysis based on latent groups, called latent DIF analysis, is provided and its applications in the literature are surveyed. Then, the methodological issues pertaining to latent DIF analysis are described, including mixture item response models, parameter estimation, and latent DIF detection methods. Finally, recommended steps for latent DIF analysis are illustrated using empirical data.     

结构方程模型和IRT等级反应模型在人格量表项目筛选中的对比研究

为比较结构方程模型和 IRT等级反应模型在人格量表项目筛选上的作用,以《中国大学生人格量表》的7229个实际测量数据为基础,针对因素二“爽直”分别以Lisrel8.70和Multilog7.03进行结构方程模型和等级反应模型的参数估计与拟合,比较两种方法的项目筛选结果.二者统计结果均认为项目5、6、7、8拟合度不佳,在结构方程模型上表现为因子负荷较低,整体拟合指数不理想;在等级反应模型上表现为区分度参数和位置参数不理想,相关项目的特征曲线和信息曲线形态较差.但结构方程模型倾向于项目6、8更差,而等级反应模型则倾向于项目5、6更差.结构方程模型和 IRT等级反应模型对人格量表项目的统计推断结果从总体上讲是一致的,但在个别项目上略有差异.二者各有优势,可以结合使用.     

校风研究述评

文章从校风的研究历史、界定方法、结构研究、相关研究领域等几个方面全面地回顾了国内外对校风定量的研究，并讨论了在我国开展校风定量研究需要注意的几个问题。     

Digital Module 13: Monte Carlo Simulation Studies in Item Response Theory

In this digital ITEMS module, Dr. Brian Leventhal and Dr. Allison Ames provide an overview of Monte Carlo simulation studies (MCSS) in item response theory (IRT). MCSS are utilized for a variety of reasons, one of the most compelling being that they can be used when analytic solutions are impractical or nonexistent because they allow researchers to specify and manipulate an array of parameter values and experimental conditions (e.g., sample size, test length, and test characteristics). Dr. Leventhal and Dr. Ames review the conceptual foundation of MCSS in IRT and walk through the processes of simulating total scores as well as item responses using the two-parameter logistic, graded response, and bifactor models. They provide guidance for how to implement MCSS using other item response models and best practices for efficient syntax and executing an MCSS. The digital module contains sample SAS code, diagnostic quiz questions, activities, curated resources, and a glossary.