Categorization and validation of handedness using latent class analysis

BACKGROUND: A view that handedness is not a dichotomous, i.e. left-right, phenomenon is shared by majority of researchers. However, there are different opinions about the exact number of hand-preference categories and criteria that should be used for their classification. OBJECTIVES: This study examined hand-preference categories using the latent class analysis (LCA) and validated them against two external criteria (i.e. hand demonstration test and a series of arbitrary cut-off points). METHOD: The Edinburgh Handedness Inventory was applied to 354 individuals randomly selected from the general population, and the obtained data were analysed using the LatentGOLD software. RESULTS: Three discrete hand-preference clusters were identified, i.e. left-, right- and mixed-handed category. Further subdivision of hand-preference clusters resulted in a non-parsimonious subcategorization of individuals. There was a good agreement between the LCA-based classification and classification based on hand-preference demonstration tests. The highest agreement between the LCA model and the different types of arbitrary classification criteria ranged between 0 ± 50 and 0 ± 70 of the laterality quotient. CONCLUSIONS: The study findings supported the view that handedness is not a bimodal phenomenon. However, definitions and subcategorizations of mixed-handedness using the cut-off points that are outside of the recommended range may lead to misclassification of cases. It is hoped that the categorization and validation of handedness developed in the context of this study will make future research in this area less dependent on arbitrary values and criteria.