Within many online learning platforms, struggling students are provided with support to guide them through challenging material. Support comes in many forms, and is typically evaluated based on its ability to improve students' performance on future tasks. However, there is little experimentation to evaluate how these supports impact students' emotional states. Student's emotional state, or affect, significantly impacts their motivation to engage with learning material and persist through challenges. Positive emotions can foster intrinsic engagement and deeper commitment, whereas negative emotions may lead to disengagement and avoidance of challenging tasks. In this work, we use publicly available data from online experiments and affect modeling to causally evaluate the impact that different support strategies have on students' affect. Through analysis of 25 experiments with 6,463 total participants, we find multiple significant positive and negative changes in students' affect when receiving hints, examples, or scaffolding questions, despite all three having a positive impact on performance, revealing the need for more nuanced evaluations of support strategies to uncover their impact beyond just performance. The code for this project is available at https://osf.io/74dgx.