Bimanual coordination is critical in many robotic and haptic systems, such as surgical robots and rehabilitation robots. While these systems often incorporate two robotic manipulators for each limb, there may be a missed opportunity to leverage overarching models of human bimanual coordination to improve the way in which the robotic manipulators are controlled and respond to the dynamic human operator. In this paper, we study the influences of several bimanual motion factors (e.g., symmetry and direction) on kinematic human joint-space features and performance outcome task-space features in a user study with eleven subjects and two haptic devices. Additionally, we evaluated the ability to use joint-space features to classify types of bimanual movement, showing the potential for a robotic system to predict how users coordinate their limbs. Three classifiers: (1) likelihood ratio, (2) k-nearest neighbor, and (3) support vector machine, were evaluated for classification accuracy in regards to the factor of number of targets. Likelihood ratio resulted in an accuracy of 79.6% with the majority of correct predictions occurring immediately at the start of movement. The task-space performance results reveal that despite the relative direction of both hands, reaching two targets results in lower performance than a single target, and symmetry alone does not contribute to performance disparity. Also, dimensionless integrated absolute jerk (DIAJ) is an indicator of superior performance for this particular task. Furthermore, these results align with current bimanual coordination theory by showing manual performance disparities are a consequence of task constraints and conceptualization.