评估基于测验题组的多值项目的正则化差异项目功能方法的性能。
Evaluating the Performance of a Regularized Differential Item Functioning Method for Testlet-Based Polytomous Items.
作者信息
Huang Jing, Miller M David, Huggins-Manley Anne Corinne, Leite Walter L, Knopf Herman T, Ritzhaupt Albert D
机构信息
University of Florida, Gainesville, FL, USA.
出版信息
Educ Psychol Meas. 2025 May 31:00131644251342512. doi: 10.1177/00131644251342512.
This study investigated the effect of testlets on regularization-based differential item functioning (DIF) detection in polytomous items, focusing on the generalized partial credit model with lasso penalization (GPCMlasso) DIF method. Five factors were manipulated: sample size, magnitude of testlet effect, magnitude of DIF, number of DIF items, and type of DIF-inducing covariates. Model performance was evaluated using false-positive rate (FPR) and true-positive rate (TPR). Results showed that the simulation had effective control of FPR across conditions, while the TPR was differentially influenced by the manipulated factors. Generally, the small testlet effect did not noticeably affect the GPCMlasso model's performance regarding FPR and TPR. The findings provide evidence of the effectiveness of the GPCMlasso method for DIF detection in polytomous items when testlets were present. The implications for future research and limitations were also discussed.
本研究调查了测试组对多分类项目中基于正则化的差异项目功能（DIF）检测的影响，重点关注具有套索惩罚的广义部分计分模型（GPCMlasso）DIF方法。操纵了五个因素：样本量、测试组效应的大小、DIF的大小、DIF项目的数量以及诱发DIF的协变量类型。使用假阳性率（FPR）和真阳性率（TPR）评估模型性能。结果表明，模拟在各种条件下有效控制了FPR，而TPR受到操纵因素的不同影响。一般来说，较小的测试组效应在FPR和TPR方面对GPCMlasso模型的性能没有明显影响。研究结果为GPCMlasso方法在存在测试组时对多分类项目进行DIF检测的有效性提供了证据。还讨论了对未来研究的启示和局限性。
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