A regularization approach for the detection of differential item functioning in generalized partial credit models
A regularization approach for the detection of differential item functioning in generalized partial credit models
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DOI：
10.3758/s13428-019-01224-2
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发表时间：
2020-02-01
影响因子：
5.4
通讯作者：
Mair, Patrick
中科院分区：
文献类型：
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作者：
Schauberger, Gunther;Mair, Patrick
在项目反应理论中，大多数用于检测差异项目功能（DIF）的常见分析工具仅限于使用单个协变量。如果必须考虑多个变量，则对每个变量独立地重复各自的方法。提出了一种基于lasso原理的均匀DIF检测的正则化方法。该方法适用于多种多元项目反应模型，以广义部分信用模型为最一般的情况。指定一个联合模型，其中所有项目和所有协变量的可能的DIF效应被显式参数化。该模型使用惩罚似然方法进行估计，该方法自动检测DIF效应，并提供同时从不同协变量中校正检测到的DIF效应的特征估计。通过若干仿真研究对该方法进行了评价。一个应用程序提出了数据从儿童抑郁症的清单。
Most common analysis tools for the detection of differential item functioning (DIF) in item response theory are restricted to the use of single covariates. If several variables have to be considered, the respective method is repeated independently for each variable. We propose a regularization approach based on the lasso principle for the detection of uniform DIF. It is applicable to a broad range of polytomous item response models with the generalized partial credit model as the most general case. A joint model is specified where the possible DIF effects for all items and all covariates are explicitly parameterized. The model is estimated using a penalized likelihood approach that automatically detects DIF effects and provides trait estimates that correct for the detected DIF effects from different covariates simultaneously. The approach is evaluated by means of several simulation studies. An application is presented using data from the children's depression inventory.