We present results from using Probit models to classify and rank texts of varying complexity from multiple sources. We use multiple linguistic sources includingSwedish easy-to-read books and investigate data augmentation and feature regularisation as optimisation methods for text complexity assessment. Multi-Scale and Single Scale Probit models are implemented using different ratios of training data, and then compared. Overall, the findings suggest that the Multi-Scale Probit model is an effective method for classifying text complexity and ranking new texts and could be used to improve the performance on small datasets as well as normalise datasets labelled using different scales.