School systems are the primary providers for the increasing number of children with mental health needs. School-based universal screening offers a valuable way to identify children that would benefit from school-based mental health services. However, many existing screening systems rely on teacher ratings alone and do not incorporate student self-ratings. The current study evaluates the psychometric properties of the Social, Academic, and Emotional Behavior Risk Screener-Student Rating Scale (SAEBRS-SRS), a new 20-item multidimensional universal screener intended to provide assessment data on students' social, academic, and emotional functioning. The SAEBRS-SRS complements the SAEBRS Teacher Rating Scale (TRS), which has previously demonstrated robust psychometric evidence. In the current study, data were collected from a racially and ethnically diverse sample of middle school students. Confirmatory factor analyses supported a bifactor structure consistent with the SAEBRS-TRS, with items corresponding to internally consistent Social, Academic, and Emotional Behaviors subscales, as well as an overall Total Behavior scale. The current analyses yield promising initial support for the development of the SAEBRS-SRS. Implications and the need for future research to provide additional psychometric evidence are discussed.

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The purposes of this study were twofold. The first was to use latent class analysis to identify groupings of students defined by the presence or absence of academic or behavioral risk. The second was to determine whether these groups differed across various dichotomous academic and behavioral outcomes (e.g., suspensions, office discipline referrals, statewide achievement test failure). Students (N ϭ 1,488) were sampled from Grades 3-5. All students were screened for academic risk using AIMSweb Reading Curriculum-Based Measure and AIMSweb Mathematics Computation, and behavioral risk using the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS). Latent class analyses supported the fit of a three-class model, with resulting student classes defined as low-risk academic and behavior (Class 1), at-risk academic and high-risk behavior (Class 2), and at-risk math and behavior (Class 3). Logistic regression analyses indicated the classes demonstrated statistically significant differences statewide achievement scores, as well as suspensions. Further analysis indicated that the odds of all considered negative outcomes were higher for both groups characterized by risk (i.e., Classes 2 and 3). Negative outcomes were particularly likely for Class 2, with the odds of negative behavioral and academic outcomes being 6 -15 and 112-169 times more likely, respectively. Results were taken to support an integrated approach to universal screening in schools, defined by the evaluation of both academic and behavioral risk. Impact and ImplicationsThe results of this study speak to the value of an integrated universal screening procedure that incorporates both academic and behavioral screening tools. Results suggested this approach can identify a group of students who are at high risk for a number of negative academic and behavioral outcomes.

Detecting mental illness in school students may prevent poor school outcomes. Clinicians often use universal behavioral screeners to identify students at risk for mental illness. This study examined the applicability of Kane's interpretation and use argument (IUA) to the Social, Academic, and Emotional Behavior Risk Screener-Teacher Rating Scale (SAEBRS-TRS). Using an imputed sample of N = 1,357 students, latent transition analysis was employed to understand the IUA framework on this sample and the stability of the latent classes of student risk over time. Results provide initial support for the interpretation and use of the SAEBRS-TRS, and suggest that student risk statuses remain relatively stable across time. Future directions and implications for practice are discussed.

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