By only looking at data through averages and totals, you often lose the story of what children and families are actually experiencing. “Data disaggregation” might sound complex, but it simply refers to breaking down large data categories into more specific demographic sub-categories like race/ethnicity, gender, socioeconomic status, and geographic location.  

Looking at data by different disaggregated subgroups helps you to answer different questions. For example, if you wanted to better understand exclusionary discipline in school settings in New York, there are several ways to use disaggregated data to frame, and eventually answer, your questions:  

• No Disaggregation: How many students were suspended statewide last year? How many days of instruction were missed statewide due to exclusionary discipline? 

• Geography: How do suspensions vary by school district? Are there differences in suspension rates in rural vs suburban vs urban districts? 

• Age: How do suspension rates vary by grade level? What impact do early grade suspensions have on a student’s long-term educational outcomes? 

• Gender: How do suspension rates vary for male, female, and nonbinary students? Does gender influence how likely a student is to receive an in-school vs out-of-school suspension? 

• Race/Ethnicity: How do suspension rates vary by student race/ethnicity? What role does systemic racism and bias play in the disproportionate suspension rates for students of color? 

• Socioeconomic Status: How do suspension rates vary for low-income vs higher income students? Is community wealth correlated to a district’s overall suspension rate? 

• Disability Status: How do suspension rates compare between students with disabilities and general education students? Does having adequate supports impact how likely a student with disabilities is to be suspended? 

In order to get to the root of a problem, it is important to look at data through an intersectional lens. For exclusionary discipline, you cannot truly understand the scope of the problem without looking at the disproportionate use of suspensions with Black male students and the wider context of systemic racism and the school-to-prison pipeline. Unfortunately, this type of data can be even more difficult to acquire than standard disaggregated data because of privacy concerns and redaction requirements.  

To learn more about data disaggregation in New York’s Early Intervention, Child Care, Pre-Kindergarten/Preschool Special Education, and Behavioral Health systems, check out our new Counting All Kids fact sheets.  

To learn more about how data plays a role in the fight for equity, including with exclusionary discipline, check out the reports and resources from The Children’s Equity Project. 

To learn more about other data disaggregation campaigns in New York State, visit:  

The Coalition for Asian American Children & Families – Invisible No More Campaign 

New York Immigration Coalition – Invisible in the Data report 

Hispanic Federation