Raju's Differential Functioning of Items and Tests (DFIT)

Nambury S. Raju (1937–2005) developed two model‐based indices for differential item functioning (DIF) during his prolific career in psychometrics. Both methods, Raju's area measures ( Raju, 1988 ) and Raju's DFIT ( Raju, van der Linden, & Fleer, 1995 ), are based on quantifying the gap between item characteristic functions (ICFs). This approach provides an intuitive and flexible methodology for assessing DIF. The purpose of this tutorial is to explain DFIT and show how this methodology can be utilized in a variety of DIF applications.     

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.     

国际教育成效评价协会儿童认知发展状况测验项目功能差异分析

本研究旨在从一维和多维的角度检测国际教育成效评价协会(IEA)儿童认知发展状况测验中中译英考题的项目功能差异(DIF)。我们分析的数据由871名中国儿童和557名美国儿童的测试数据组成。结果显示,有一半以上的题目存在实质的DIF,意味着这个测验对于中美儿童而言,并没有功能等值。使用者应谨慎使用该跨语言翻译的比较测试结果来比较中美两国考生的认知能力水平。所幸约有半数的DIF题目偏向中国,半数偏向美国,因此利用测验总分所建立的量尺,应该不至于有太大的偏误。此外,题目拟合度统计量并不能足够地检测到存在DIF的题目,还是应该进行特定的DIF分析。我们探讨了三种可能导致DIF的原因,尚需更多学科专业知识和实验来真正解释DIF的形成。     

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.     

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.     

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。     

Digital Module 06: Bayesian Psychometrics—Posterior Predictive Model Checking https://ncme.elevate.commpartners.com

Drawing valid inferences from modern measurement 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. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model‐data fit models is critical. In this instructional module, Allison Ames and Aaron Myers provide an overview of Posterior Predictive Model Checking (PPMC), the most common Bayesian model‐data fit approach. Specifically, they review the conceptual foundation of Bayesian inference as well as PPMC and walk through the computational steps of PPMC using real‐life data examples from simple linear regression and item response theory analysis. They provide guidance for how to interpret PPMC results and discuss how to implement PPMC for other model(s) and data. The digital module contains sample data, SAS code, diagnostic quiz questions, data‐based activities, curated resources, and a glossary.     

Can Item Response Times Provide Insight Into Students’ Motivation and Self‐Efficacy in Math? An Initial Application of Test Metadata to Understand Students’ Social–Emotional Needs

As computer‐based tests become more common, there is a growing wealth of metadata related to examinees’ response processes, which include solution strategies, concentration, and operating speed. One common type of metadata is item response time. While response times have been used extensively to improve estimates of achievement, little work considers whether these metadata may provide useful information on social–emotional constructs. This study uses an analytic example to explore whether metadata might help illuminate such constructs. Specifically, analyses examine whether the amount of time students spend on test items (after accounting for item difficulty and estimates of true achievement), and difficult items in particular, tell us anything about the student's academic motivation and self‐efficacy. While results do not indicate a strong relationship between mean item durations and these constructs in general, the amount of time students spend on very difficult items is highly correlated with motivation and self‐efficacy. The implications of these findings for using response process metadata to gain information on social–emotional constructs are discussed.     

An NCME Instructional Module on Using Differential Step Functioning to Refine the Analysis of DIF in Polytomous Items

Traditional methods for examining differential item functioning (DIF) in polytomously scored test items yield a single item‐level index of DIF and thus provide no information concerning which score levels are implicated in the DIF effect. To address this limitation of DIF methodology, the framework of differential step functioning (DSF) has recently been proposed, whereby measurement invariance is examined within each step underlying the polytomous response variable. The examination of DSF can provide valuable information concerning the nature of the DIF effect (i.e., is the DIF an item‐level effect or an effect isolated to specific score levels), the location of the DIF effect (i.e., precisely which score levels are manifesting the DIF effect), and the potential causes of a DIF effect (i.e., what properties of the item stem or task are potentially biasing). This article presents a didactic overview of the DSF framework and provides specific guidance and recommendations on how DSF can be used to enhance the examination of DIF in polytomous items. An example with real testing data is presented to illustrate the comprehensive information provided by a DSF analysis.