We have applied a multivariate exploratory technique called Correspondence Analysis (CA) to create and analyze a model of the dataset of experiment results. The dataset originates from a comparative usability study of tracing with the use of mouse, pen, and touch input and contains both categorical and continuous data – i.e. results of questionnaires and task measurements. CA allowed to visually and numerically assess the main variables in the dataset and how they interact with each other. In our study, pen input had the best measured performance and was preferred by the users. Touch input was the least accurate of all input methods tested but it was preferred by users over mouse especially in the conditions lacking of visual feedback of drawing. CA helped to detect that secondary effect even though it cannot be explained by the performance results alone. The importance of the influence of user's previous experience is also noted. We conclude that CA helped to identify all major phenomena known from previous studies but also was sensitive to minor and secondary effects, what makes it a well suited method to quickly evaluate usability data.