Unaddressed social, emotional, and behavioral (SEB) needs and academic challenges may lead to negative youth outcomes. Universal behavioral risk screeners, like the student self-report Social, Academic, and Emotional Behavior Risk Screener (SAEBRS-SRS), identify at-risk students. To improve screening tool use, research is needed to identify the grouping of students in different types (e.g., emotional and social) and intensities of need (e.g., low, medium, and high). The Emotional Behavior subscale of the SAEBRS-SRS may serve as an initial screener to identify youth with SEB concerns. A latent class analysis was employed to identify classes of student emotional behavior and investigate the relationship to math and reading performance. Model results supported three distinct classes (i.e., low, low-moderate, and high risk) and promise for using the subscale independently. Future directions and implications for research are discussed.