Problem: Simple language reword.
Collecting and disaggregating discipline data by race, ethnicity, and disability is essential because it helps schools identify hidden patterns of disproportionality that are often masked in overall discipline rates. As highlighted in McIntosh et al. (2021) and other equity-focused PBIS research, breaking data down by subgroup allows teams to see which students are receiving more referrals or harsher consequences for similar behaviors, increasing awareness of implicit bias and guiding culturally responsive supports. Without disaggregated data, schools may assume discipline practices are fair when disparities actually exist. Tools like the School-Wide Information System (SWIS) support this process by organizing discipline data into clear, usable reports such as the "Big 5" (type of behavior, location, time, student, and staff). SWIS allows teams to filter data by demographic groups, track trends over time, and evaluate whether interventions reduce disproportionality. This helps schools use structured problem-solving models, like TIPS, to select targeted strategies, monitor fidelity, and adjust supports based on evidence. Overall, disaggregated data combined with systems like SWIS strengthens data-driven decision making, improves equity in discipline, and supports sustainable PBIS implementation-an approach that aligns well with the kind of structured results analysis you've been practicing in your behavior intervention literature reviews. Need Assignment Help?