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Strategies for Equitably Analyzing Disciplinary Data

Understanding inequitable patterns in disciplinary data


Community advocates in two Southern school districts sought to examine potential discrepancies in the use of exclusionary disciplinary practices by carefully analyzing data. However, during the process, a broader narrative emerged – one of education opportunity.

Because both in-school and out-of-school suspensions resulted in lost instructional time, and since missing learning directly affects a student’s ability to make academic gains, examining disciplinary data alongside achievement data revealed inequities in terms of disproportionate discipline and differences in learning progress. Originally, families and community stakeholders intended to advocate for specific policy changes related to exclusionary practices. After examining all of the data, a more complex equity challenge emerged. As a result, data helped inform advocacy for students experiencing lost instructional time.

Consider the two research questions that drove the analysis of disciplinary data in the Southern school districts:

  1. What is the frequency with which the districts use exclusionary disciplinary practices?

  2. How do documented disciplinary infractions vary by school and by subpopulation?


The first research question sought to understand the number of expulsions, suspensions, and transfers to alternative schools that occurred each year for all students before looking more specifically by school, race, gender, grade level, English language learning status, and other factors, as well as the percentage of incidents associated with each subgroup. Although the question asked about frequency, depending on how the data was structured, there were different ways to display the findings. For example, a line graph would show change over time, a box plot could be used to identify schools with excessive numbers of incidents, and a bar chart might allow for a comparison of percentages.

Given the need to communicate potential disparities based on subpopulation, tables also presented a powerful way to show discrepancies in representation. For example, the table below illustrates a persistent trend that students classified as having a disability were disproportionately disciplined over time. Although they comprised only 14-15% of the student population, between 15 and 28% of disciplinary incidents were associated with them.


At the core of this analysis was the idea that disciplinary practices unduly targeted specific subpopulations of students. As a result, variation could be observed using a number of different analysis strategies.

  • Box plots illustrated the variation in the number of incidents per school;

  • Tables displayed incongruities between the percentage of different subgroups in the student population and the percentage of incidents associated with each group; and

  • Bar charts described the variation in the frequency of different incidents or responses.