A rating scale is a set of categories designed to obtain information about a quantitative or a qualitative attribute. Item response theory (IRT) proposes that a probability function over a single latent variable represents the overall attribute evolution that the scale is designed to assess. Here we utilize an information theory approach to IRT to analyze rating scale data. The proposed IRT analyses, based on surprisal, offer new tools for assessing raters, rated items, and the whole rating scale. The information transformation from probability to surprisal is a new lens from which to view choice data and is an important augmentation of probability-based IRT. It also offers new graphical tools to measure the amount of information captured by an item in an additive metric, and to measure covariation among items using mutual information. The proposed methodology is illustrated using two scales from real clinical data and the proposed approach is compared with analyses made with the commonly used parametric IRT graded response model. Practical implications of the proposed methodology are provided.