As science, technology, engineering, and mathematics (STEM) education researchers continue to explore ways to increase college student persistence in STEM fields, the affective domain (e.g., attitudes, perceptions, and self-efficacy) stands out as an area that can significantly impact these efforts. Latent class analysis (LCA) and latent transition analysis (LTA) are mixture modeling approaches that take a person-centered approach to quantitative research, which can help us to further our efforts to diversify STEM fields. This study seeks to use LCA and LTA to investigate how students' attitudes toward science in general chemistry evolve over a semester. Using the