Datasets for semi-automated Rasch analysis

Thus, we introduce a novel criterion to judge the instrument's quality: the higher the score, the better the instrument. With this criterion, we aim for the highest-scoring instrument. We, however, are careful enough to call this a semi-automated method rather than a fully automated one since we are aware that experts' knowledge is invaluable.

As a final point, 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 the semi-automated method produces comparable results to the manual Rasch method.

These real-world datasets consist of preprocessed versions of three publicly available datasets that can be found in the publications' supplementary data of Vijaya K. Gothwal et al. , Shu Imaizumi and Yoshihiko Tanno , and Elisa Morrone et al. . Date Submitted: 2022-10-27