The aim of this study is to investigate a novel method for dealing with incomplete scale scores in longitudinal data that result from missing item responses. This method includes item information as auxiliary variables, which is advantageous because it incorporates the observed item-level data while maintaining the scale scores as the focus of the analysis. These auxiliary variables do not change the analysis model, but improve missing data handling. The investigated novel method uses the item scores or some summary of a parcel of item scores as auxiliary variables, while treating the scale scores missing in a latent growth model. The performance of these methods was examined in several simulated longitudinal data conditions and analyzed through bias, mean square error, and coverage. Results show that including the item information as auxiliary variables results in rather dramatic power gains compared with analyses without auxiliary variables under varying conditions.