This project extends the semi-automated method for obtaining a valid and reliable instrument using the Rasch model as a basis. We aim to incorporate the differential item functioning (DIF) assessment into the procedure. For the implementation, we parameterize DIF effect sizes for each item and consider DIF items as split items based on the DIF-inducing covariates (test participants' background information). This implementation leads to a new criterion called in-plus-out-of-questionnaire log likelihood with differential item functioning (IPOQ-LL-DIF). The datasets presented here are used in empirical studies. The simulated datasets are generated randomly and independently to illustrate the characteristics of the semi-automated method. Using real-world datasets, we show that semi-automated and manual Rasch methods produce comparable results. These real-world datasets consist of preprocessed versions of three publicly available datasets that can be found in the publications' supplementary data of Brett Vaughan , Brett Vaughan , and Rosalba Rosato et al. .