Chalmers R Philip, Counsell Alyssa, Flora David B
York University, Toronto, Ontario, Canada.
Educ Psychol Meas. 2016 Feb;76(1):114-140. doi: 10.1177/0013164415584576. Epub 2015 Jun 29.
Differential test functioning, or DTF, occurs when one or more items in a test demonstrate differential item functioning (DIF) and the aggregate of these effects are witnessed at the test level. In many applications, DTF can be more important than DIF when the overall effects of DIF at the test level can be quantified. However, optimal statistical methodology for detecting and understanding DTF has not been developed. This article proposes improved DTF statistics that properly account for sampling variability in item parameter estimates while avoiding the necessity of predicting provisional latent trait estimates to create two-step approximations. The properties of the DTF statistics were examined with two Monte Carlo simulation studies using dichotomous and polytomous IRT models. The simulation results revealed that the improved DTF statistics obtained optimal and consistent statistical properties, such as obtaining consistent Type I error rates. Next, an empirical analysis demonstrated the application of the proposed methodology. Applied settings where the DTF statistics can be beneficial are suggested and future DTF research areas are proposed.
差异测验功能（DTF）是指当测验中的一个或多个项目表现出差异项目功能（DIF），且这些效应的总和在测验层面上被观察到时出现的情况。在许多应用中，当测验层面上DIF的总体效应可以量化时，DTF可能比DIF更重要。然而，尚未开发出用于检测和理解DTF的最佳统计方法。本文提出了改进的DTF统计量，该统计量能够恰当地考虑项目参数估计中的抽样变异性，同时避免了预测临时潜在特质估计以创建两步近似值的必要性。使用二分和多分IRT模型的两项蒙特卡罗模拟研究检验了DTF统计量的性质。模拟结果表明，改进后的DTF统计量获得了最优且一致的统计性质，例如获得一致的I类错误率。接下来，实证分析展示了所提出方法的应用。提出了DTF统计量可能有益的应用场景，并提出了未来DTF的研究领域。
