Improvement in Detection of Differential Item Functioning Using a Mixture Item Response Theory Model
Improvement in Detection of Differential Item Functioning Using a Mixture Item Response Theory Model
复制标题
使用混合项目响应理论模型改进差异项目功能的检测
DOI：
10.1080/00273171.2010.533047
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发表时间：
2010
影响因子：
3.8
通讯作者：
H. van der Flier
中科院分区：
文献类型：
--
作者：
Annette M. Maij;H. Kelderman;H. van der Flier
通常，差异项目功能 (DIF) 检测方法会比较清单组中项目的功能。然而，项目功能不同的明显群体可能不一定与偏差的真正来源一致。预计包含潜在 DIF 变量的模型下的 DIF 检测对此偏差源更敏感。在模拟研究中，结果表明，包含潜在分组变量的混合项目响应理论模型在识别 DIF 项目方面比仅使用明显变量的 DIF 检测方法表现更好。随着显性变量与 DIF 真实来源之间的相关性变小，显性 DIF 检测与潜在 DIF 检测之间的差异也会增大。研究了不同的样本量、相对组大小和显着性水平。最后，一个实证示例演示了使用潜在分组变量检测少数样本中的异质性。显性和潜在 DIF 检测方法应用于通用能力倾向测试电池 (GATB) 的词汇测试。
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).