Predictive Modeling of Uniform Differential Item Functioning Preservation Likelihoods After Applying Disclosure Avoidance Techniques to Protect Privacy
The need to publish and disseminate data continues to grow. Administrators of large-scale educational assessment should provide examinee microdata in addition to publishing assessment reports. Disclosure avoidance methods are applied to the data to protect examinee privacy before doing so, while attempting to preserve as many item statistical properties as possible. When important properties like differential item functioning are lost due to these disclosure avoidance methods, the microdata can give off misleading messages of effectiveness in measuring the test construct. In this research study, I investigated the preservation of differential item functioning in a large-scale assessment after disclosure avoidance methods have been applied to the data. After applying data swapping to protect the data, I attempted to empirically model and explain the likelihood of preserving various levels of differential item functioning as a function of several factors including the data swapping rate, the reference-to-focal group ratio, the type of item scoring, and the level of DIF prior to data swapping.