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State data team: higher student belonging linked to sharply lower odds of causing school incidents
Summary
A USBE data presentation using Panorama survey scores found that a one‑unit increase in students' sense of belonging was associated with a roughly 38%–41% reduction in the odds of causing a school incident; presenters cautioned the analysis uses matched data from four LEAs and is preliminary.
A Utah State Board of Education (USBE) data presenter told attendees that preliminary analyses linking Panorama survey measures of students’ sense of belonging to state incident records show a sizable protective association.
The presenter said that after matching Panorama survey scores to state records, about 31,000 students were available in the winter sample and nearly 47,000 in spring. “Holding all other variables constant, for every one‑unit increase in sense of belonging mean scores, the odds of causing an incident decreased by 38 percent,” the presenter summarized for the audience, and later noted the spring model showed about a 41 percent decrease.
The presenter said the models accounted for student‑level controls (sex, special education status, low‑income status, chronic absenteeism and a simplified race indicator) and included school as a random effect. The outcome measured whether a student caused an incident at all; the analysis did not account for incident severity.
Why it matters: school incidents and disciplinary actions are connected to attendance, engagement and academic outcomes. If the association holds in broader samples and under more rigorous designs, interventions that increase students’ sense of belonging could reduce disciplinary incidents and related downstream harms.
Limits and cautions: the presenter emphasized the sample is not population‑representative — data came from four LEAs that partnered with USBE and Panorama — and described these results as preliminary. “These are definitely preliminary results,” the presenter said, noting additional modeling and sensitivity checks are planned. The team also removed predictors that were non‑significant in initial runs (English‑learner and Title I status) before presenting the condensed model.
What came next: attendees from LEAs described how they use Panorama locally — for example, one NEBO district representative said the platform combines academic, behavior and well‑being data to support early‑warning systems. Presenters suggested next steps include examining seasonal differences (winter vs. spring) and expanding to a more representative set of LEAs.
Data and provenance: the presenter reported the matched sample sizes (about 31,000 winter; about 47,000 spring) and incident counts (1,743 in the winter matched sample; 2,513 in spring). The USBE presenter flagged that the study treats change across the year carefully (separate winter and spring models) and that plans are in place to examine grade‑band and other heterogeneity.
The project is ongoing; presenters described the analysis as useful to identify belonging as a protective factor but not as definitive causal evidence.
