Equity-Minded Data Analysis

Data Conversations

Having the right conversations about data is key when conducting an equity-minded inquiry. It is important that campuses spend time building capacity for practitioners undertaking equity-minded data inquiry to ensure that everyone understands how to use data in equity-minded ways and determining which areas may need further analysis.

Data conversations can be uncomfortable for some of your colleagues, particularly when applying a racial lens to examining equity gaps. The discomfort with discussing data disaggregated by race can, in turn, lead to pushback and an inclination for the data to be examined in an aggregate level (i.e. students of color). Challenge your campus teams to work through this discomfort and remind colleagues that data disaggregation is a critical step and tool to advance equity and a critical component to revealing inequities.

Getting Started

Conducting data inquiries into racial-ethnic equity gaps allows for campuses to begin to understand the magnitude of disparities that exist across programs and/or the college/university. Ultimately, the question campuses should strive to answer is: “Are we improving equity in education and student success outcomes through our programming and policy?” The first step toward understanding which student populations experience disparities is to disaggregate the data. Doing so may reveal inequities that have not been obvious before and allow them to come into a much sharper focus.

Some Common Data Obstacles

Having conversations about data can be difficult. Here are some common examples of what may come up when discussing data:

  • Fear of what data will reveal (and how that affects the sense of self).
  • Concern about how the data will be used (i.e. punitive?).
  • Deficit-minded deflection (focus away from self-change).
  • Criticism of the data itself (in some cases another deflection).
  • Discomfort with talking about race.
  • Reluctance or unwillingness to examine gaps from any perspective other than student deficit explanations.

Why Disaggregate Data?

Examining disparity patterns in students’ experiences and outcomes is critical for identifying, understanding, and narrowing equity gaps for underserved, unrepresented, or marginalized student populations. Limiting the analysis to the large aggregate population of students of color obscures from consideration the substantial variations in outcomes and experiences of many ethnic and racial groups encompassed in the category. We must be able to see the group-level differences in our data. The differences in outcomes are a function of a variety of different factors, which also include pedagogical practices, curriculum design, and institutional (in-classroom and out-classroom) engagement support practices.

Collapsing all racial and ethnic groups into a single aggregate category of “students of color” does not allow for consideration of the tremendously varied experiences, histories, socioeconomic positions, and political factors that have, and continue to shape their disparate outcomes. For example, combining African American and Asian students together into a single category of “students of color”, does not allow for viewing and divergent outcomes for these groups, let alone consideration of the different factors that shape their outcomes. Simply put, not all students of color or those of Native/Indigenous background/ancestry are the “same.”

Using equity-minded data analysis, campuses may find areas that the data requires further disaggregation. For example, after reviewing retention rates by racial groups and identifying a disparity for a certain population, campuses may want to further disaggregate that population’s data based on gender or first-generation status to identify if there are more pronounced disparities within a given group. Examples of data disaggregation inquires include:

  • Race and ethnicity
  • Gender
  • Socio-economic status
  • First-generation status
  • Ability status
  • Age
  • Enrollment status
  • Parental Education Attainment level