Stark Stephen, Chernyshenko Oleksandr S, Drasgow Fritz
Department of Psychology, University of South Florida, Tampa, FL, USA.
J Appl Psychol. 2006 Nov;91(6):1292-306. doi: 10.1037/0021-9010.91.6.1292.
In this article, the authors developed a common strategy for identifying differential item functioning (DIF) items that can be implemented in both the mean and covariance structures method (MACS) and item response theory (IRT). They proposed examining the loadings (discrimination) and the intercept (location) parameters simultaneously using the likelihood ratio test with a free-baseline model and Bonferroni corrected critical p values. They compared the relative efficacy of this approach with alternative implementations for various types and amounts of DIF, sample sizes, numbers of response categories, and amounts of impact (latent mean differences). Results indicated that the proposed strategy was considerably more effective than an alternative approach involving a constrained-baseline model. Both MACS and IRT performed similarly well in the majority of experimental conditions. As expected, MACS performed slightly worse in dichotomous conditions but better than IRT in polytomous cases where sample sizes were small. Also, contrary to popular belief, MACS performed well in conditions where DIF was simulated on item thresholds (item means), and its accuracy was not affected by impact.
在本文中，作者开发了一种识别差异项目功能（DIF）项目的通用策略，该策略可在均值和协方差结构方法（MACS）以及项目反应理论（IRT）中实施。他们建议使用自由基线模型的似然比检验和Bonferroni校正的临界p值，同时检查负荷（区分度）和截距（位置）参数。他们将这种方法与针对各种类型和数量的DIF、样本量、反应类别数量以及影响量（潜在均值差异）的替代实施方案的相对功效进行了比较。结果表明，所提出的策略比涉及约束基线模型的替代方法有效得多。在大多数实验条件下，MACS和IRT的表现同样出色。正如预期的那样，在二分条件下MACS的表现略差，但在样本量较小的多分类情况下，其表现优于IRT。此外，与普遍看法相反，MACS在项目阈值（项目均值）上模拟DIF的条件下表现良好，其准确性不受影响量的影响。
