To prevent response bias, personality questionnaires may use comparative response formats. These include forced choice, where respondents choose among a number of items, and quantitative comparisons, where respondents indicate the extent to which items are preferred to each other. The present article extends Thurstonian modeling of binary choice data to "proportion-of-total" (compositional) formats. Following the seminal work of Aitchison, compositional item data are transformed into log ratios, conceptualized as differences of latent item utilities. The mean and covariance structure of the log ratios is modeled using confirmatory factor analysis (CFA), where the item utilities are first-order factors, and personal attributes measured by a questionnaire are second-order factors. A simulation study with two sample sizes, N = 300 and N = 1,000, shows that the method provides very good recovery of true parameters and near-nominal rejection rates. The approach is illustrated with empirical data from N = 317 students, comparing model parameters obtained with compositional and Likert-scale versions of a Big Five measure. The results show that the proposed model successfully captures the latent structures and person scores on the measured traits.