Discovering how people perceive objects such as faces, cars, foods, etc., is an important objective of researchers in marketing, clinical psychology, and cognitive science. Various methodologies exist for uncovering mental representations of items often modeled as spatial maps. One task that has been used extensively when large numbers of objects are considered asks individuals to sort objects into user-chosen categories according to their overall similarities. Different statistical strategies for analyzing sorting data have been proposed, but have not been directly compared for their relative abilities to uncover accurate spatial representations of the objects. Using Munsell colors in one task and personality traits in second task, we investigated three sorting analysis methods: homogeneity analysis (HOMALS), dissimilarity from correspondence matrices, and a method proposed by Bimler and Kirkland (2001) that used additional information from a subsequent merge task. All methods were assessed according to how well they recovered a known configuration established by other means (external validity), as well as how accurately each predicts the actual participant data (internal validity). For the colors, both external and internal validity measures suggested that the configuration obtained using the Bimler and Kirkland merge method was superior to that from the other two methods. For the traits, results were mixed in that different methods were superior along different indices. These results are not surprising given that traits are more complex categories as compared to simple perceptual colors, and may be better represented by sets of features rather than as points in a cognitive map. |