Tutz Gerhard, Schauberger Gunther
Department of Statistics, Ludwig-Maximilians-Universität, Munich, Germany,
Psychometrika. 2015 Mar;80(1):21-43. doi: 10.1007/s11336-013-9377-6. Epub 2013 Dec 3.
A new diagnostic tool for the identification of differential item functioning (DIF) is proposed. Classical approaches to DIF allow to consider only few subpopulations like ethnic groups when investigating if the solution of items depends on the membership to a subpopulation. We propose an explicit model for differential item functioning that includes a set of variables, containing metric as well as categorical components, as potential candidates for inducing DIF. The ability to include a set of covariates entails that the model contains a large number of parameters. Regularized estimators, in particular penalized maximum likelihood estimators, are used to solve the estimation problem and to identify the items that induce DIF. It is shown that the method is able to detect items with DIF. Simulations and two applications demonstrate the applicability of the method.
提出了一种用于识别差异项目功能（DIF）的新诊断工具。传统的DIF方法在研究项目的解决方案是否取决于子群体成员身份时，只允许考虑少数子群体，如种族群体。我们提出了一个用于差异项目功能的显式模型，该模型包括一组变量，包含度量和分类成分，作为诱导DIF的潜在候选变量。纳入一组协变量的能力意味着该模型包含大量参数。正则化估计器，特别是惩罚最大似然估计器，用于解决估计问题并识别诱导DIF的项目。结果表明，该方法能够检测出具有DIF的项目。模拟和两个应用实例证明了该方法的适用性。
