This article provides a many-facet Rasch measurement (MFRM) analysis of go/no-go association task (GNAT)-based measures of implicit attitudes toward sweet and salty food. We describe the statistical model and the strategy we adopted to score the GNAT, and we emphasize that, when analyzing implicit measures, MFRM indexes have to be interpreted in a peculiar way. In comparison with traditional scoring algorithms, an MFRM analysis of implicit measures provides some additional information and suffers from fewer limitations and assumptions. MFRM might help to overcome some limitations of current implicit measures, since it directly addresses some known issues and potential confounds, such as those related to a rational zero point, to the arbitrariness of the metric, and to participants' task-set switching ability.