USING PREDICTORS TO PARTITION MENU SELECTION TIMES

Selection times of drop-down menus are in many ways influenced by cogn itive and motor processes of the user and by design variables of the m enu. Since the number of these variables is too large, the contributio n of individual variables to selection time cannot be assessed by usin g factorial designs. Multiple regression is introduced to solve this p roblem. The technique uses selection times as criterions and a set of general menu characteristics as predictors. The non-standardized slope s beta report the increase (or decrease) in selection time which can b e assessed for each predictor. In a first experiment, the validity of the technique was demonstrated replicating various well-known effects in a mouse-driven editor. For example, the selection times increased w ith the number of subordinate menu items or atypical items. Further, d ue to motor components of the mouse movement, selection times depended on the spatial position of an item within the menu. In a second exper iment, mouse selection was replaced by key selection to stress cogniti ve processes contributing to response times. The technique yielded res ults that were sensitive to this variation. Limitations of the techniq ue are discussed.