Maij-de Meij Annette M, Kelderman Henk, van der Flier Henk
a VU University Amsterdam.
Multivariate Behav Res. 2010 Nov 30;45(6):975-99. doi: 10.1080/00273171.2010.533047.
Usually, methods for detection of differential item functioning (DIF) compare the functioning of items across manifest groups. However, the manifest groups with respect to which the items function differentially may not necessarily coincide with the true source of the bias. It is expected that DIF detection under a model that includes a latent DIF variable is more sensitive to this source of bias. In a simulation study, it is shown that a mixture item response theory model, which includes a latent grouping variable, performs better in identifying DIF items than DIF detection methods using manifest variables only. The difference between manifest and latent DIF detection increases as the correlation between the manifest variable and the true source of the DIF becomes smaller. Different sample sizes, relative group sizes, and significance levels are studied. Finally, an empirical example demonstrates the detection of heterogeneity in a minority sample using a latent grouping variable. Manifest and latent DIF detection methods are applied to a Vocabulary test of the General Aptitude Test Battery (GATB).
通常，检测项目功能差异（DIF）的方法会比较各明显分组中项目的功能。然而，项目功能存在差异的明显分组不一定与偏差的真正来源一致。预计在包含潜在DIF变量的模型下进行DIF检测，对这种偏差来源会更敏感。在一项模拟研究中表明，包含潜在分组变量的混合项目反应理论模型在识别DIF项目方面比仅使用明显变量的DIF检测方法表现更好。随着明显变量与DIF真正来源之间的相关性变小，明显DIF检测与潜在DIF检测之间的差异会增大。研究了不同的样本量、相对组大小和显著性水平。最后，一个实证例子展示了使用潜在分组变量检测少数群体样本中的异质性。明显DIF检测方法和潜在DIF检测方法被应用于一般能力倾向测验电池（GATB）的词汇测试。
