It is meaningful to create a comprehensive score to extract information from mass continuous data when they measure the same latent concept. Therefore, this study adopts the logic of psychometrics to conduct scales on continuous data under the Rasch models. This study also explores the effect of different data discretization methods on scale development by using financial profitability ratios as a demonstration. Results show that retaining more categories can benefit Rasch modeling because it can better inform the models. The dynamic clustering algorithm, k-median is a better method for extracting characteristic patterns of the continuous data and preparing the data for the Rasch model. This study illustrates that there is no one-way good discretization method for continuous data under the Rasch models. It is more reasonable to use the traditional algorithms if each continuous data variable has target benchmark(s), whereas the k-median clustering algorithm achieves good modeling results when benchmark information is lacking.