In the present research, we proposed a systematic approach to disentangling the shared and unique variance explained by achievement goals, reasons for goal pursuit, and specific goal-reason combinations (i.e., achievement goal complexes). Four studies using this approach (involving nearly 1,800 participants) led to 3 basic sets of findings. First, when testing goals and reasons "separately," mastery (-approach) goals and autonomous reasons explained variance in beneficial experiential (interest, satisfaction, positive emotion) and self-regulated learning (deep learning, help-seeking, challenging tasks, persistence) outcomes. Second, when testing goals and reasons "simultaneously," mastery goals and autonomous reasons explained independent variance in most of the outcomes, with the predictive strength of each being diminished. Third, when testing goals, reasons, and goal complexes "together," the autonomous mastery goal complex explained incremental variance in most of the outcomes, with the predictive strength of both mastery goals and autonomous reasons being diminished. Comparable results were observed for performance (-approach) goals, the autonomous performance goal complex, and performance goal-relevant outcomes. These findings suggest that achievement goals and reasons are both distinct and overlapping constructs, and that neither unilaterally eliminates the influence of the other. Integrating achievement goals and reasons offers the most promising avenue for a full account of competence motivation.