Educational equity is the idea that every student gets what they need in order to be successful in school and life. It forms the core of our educational system and is how educators ensure that each student shows appropriate growth throughout the school year regardless of their background, zip code, or personal circumstances. But how do educators know if they are meeting the needs of all students? With the average total school enrollment in the United States hovering around 550 students, it becomes necessary for educators to summarize student data to make informed decisions. Data disaggregation is one tool that educators can use to understand the performance of various groups of students.
Data disaggregation is a method of analyzing and presenting data that is divided into segments to show a greater level of detail. In education, it is common to disaggregate data by gender, race/ethnicity, ability, economic status, English fluency, course section, or schools or districts. This process helps educators better understand discrepancies in student performance and build plans to address inequities.
Data disaggregation became the norm in educational data analysis during the No Child Left Behind (NCLB) era. NCLB required states to report data disaggregated for six groups: race/ethnicity, economic disadvantage, disability, limited English proficiency, migrant status, and gender. Over time, educators and data analysts alike discovered new understandings and new benefits of disaggregating data more deeply.
The primary advantage to the data disaggregation process is that it allows you to understand the performance between groups. When one group of students performs at a lower rate it is an indicator that inequity exists within the systems of a school. Those systems may be behavioral processes, curriculum, access to extended services like tutoring or after school activities, or access to community services. When educators are able to identify these inequities, then they can begin to ask hard questions, uncover difficult truths, and begin to resolve long-standing and systemic problems.
There are many tools at your disposal to assist with data disaggregation. If you are working in spreadsheet software, you can use a variety of formulas, such as =AVERAGEIF, =SUMIF, =COUNTIF, =MAXIF, and =MINIF to quickly summarize a subset of a distribution. Additionally, you can use pivot tables to quickly disaggregate your data. You can find a detailed breakdown of each of these formulas in my Essential Spreadsheet Formulas eBook as well as in my Excel and Google Sheets for Educators video series. Both are available for free in The Repository.
Another option is to use my free Data Disaggregation Tool. With this tool, you simply upload your data set and select your disaggregating variable and outcome variable from the drop down menus. The tool will instantly disaggregate your data and give you the mean, median, mode, standard deviation, minimum, and maximum for each subgroup. It will also create a boxplot to quickly visualize the differences between your subgroups. All of this can be copy and pasted into your data reports and slide decks so you can quickly share the results with your team.
Once you have mastered the basics of data disaggregation, take your data analysis up a notch by considering intersectionality. Intersectionality is the idea that as individuals we are part of multiple groups and that the layering of those groups may impact our experiences and outcomes. If you first disaggregate your data based on race, then it may be beneficial to further disaggregate your racial data by gender. This deeper level analysis would allow you to you to compare the outcome of Hispanic/Latino males with Hispanic/Latino females, for example.
It is important to consider a wide range of intersectionality. An exploratory data analysis process that involves data disaggregation process may allow you to discover new insights. Recently, I was working with a district on just this task. Their data disaggregation was showing an inequity in student access to advanced coursework. Their initial disaggregation showed them that African American students were experiencing barriers to access at a greater rate then all other racial and ethnic groups. However; when they added layers to examine intersectionality, they found that the barriers were actually related to poverty. African American students from low income backgrounds were experiencing barriers at the same rate as white students from low income backgrounds. Similarly, African American and white students from higher income households were enrolling in advanced placement courses at roughly the same rate.
Had this district not disaggregated their data and dug more deeply to examine intersectionality, they may have created a continuous improvement plan that sought to solve the wrong problem. Herein lies the benefit of this work. Continuous improvement work is hard! It takes countless hours of planning, strategizing, deployment, and monitoring. You must examine your work, and constantly tweak your plans to ensure the greatest levels of success. By taking time to deeply analyze your data and disaggregate your data you will be able to create better goals that translate into meaningful results for all students.
Good luck on your journey friends and let me know if I can help.