Measuring Equity Matters for Early Childhood Programs

National and state-level data on federal programs and participants can provide a window into the experiences of young children in early childhood programs from historically marginalized groups. However, the broad scope of these data creates gaps that sometimes limit their ability to help explore questions related to equity. The shortcomings of available data about the experiences of children of color and other historically marginalized groups in early childhood programs include the following:

  • Small sample sizes for some groups of children, which limits the ability to understand their specific experiences
  • The absence of data relevant for addressing equity, such as information on levels of cultural and linguistic competence among staff, their disciplinary practices, and their access to training on inclusive practices
  • Lack of data disaggregated by key characteristics such as race and ethnicity, language, and disability that shows how experiences differ and reveals the factors that shape those differences

In short, there are limitations in our ability to use existing national and state-level data to fully advance equity—especially for children of color, dual language learners, children with disabilities, children who are immigrants, and other historically marginalized groups.

Using national and state-level data as a tool for advancing equity is one way to strengthen existing early childhood data systems. This shift in perspective requires an expansion of available data and analysis and reporting approaches. Both federal and state-level data systems would benefit from the collection of data on historically marginalized groups (such as those who are traditionally underrepresented in research studies) to understand their experiences and ensure more equitable outcomes.

Employing data disaggregation in analysis and reporting can improve measurement of how specific groups experience their programs by helping to answer the following questions:

  • How do they benefit from participation?
  • How do programs fail to meet their specific needs?
  • How do programs build on their strengths?

Programs and systems could also benefit from additional data on equitable decision making. Required reporting systems like the Head Start Program Information Report and national data collection efforts like the Head Start Family and Child Experiences Survey (FACES) can provide data users from practitioners to policy makers with needed contextual and demographic information. It is only when we plan to measure, analyze, and interpret data with equity in mind that we can ensure that the needs of all children are met by federal programs.

A focus on racial equity led us to explore current information about preschool boys of color. To paint a national portrait of Black and Latinx boys who participate in Head Start, we analyzed FACES data. The richness of these data offered several advantages. We were able to disaggregate the data by race/ethnicity and gender, and the range of available data enabled us to better understand the strengths of their families and classrooms to support their healthy development and learning. However, there is still more important information to learn about their classroom experiences, especially those relating to classroom disciplinary practices and program policies on expulsion and suspension. More information about teacher anti-bias training would also be helpful.

We know there are inequities in how children are viewed and judged in terms of their behavior and social-emotional functioning along categories of race or ethnicity and gender. Boys of color are more likely to experience inequitable treatment in classrooms, and these differences matter for their development. Unequal treatment can affect their ability to trust, build responsive relationships, and experience continuity of care, which can impact their future opportunities. These are examples of specific metrics by which we can assess, and seek to improve, equitable classroom experiences for historically marginalized groups. Having access to such data through administrative sources or national studies would help expand our understanding of their experiences.

The Head Start program has been at the forefront of addressing inequities in disciplinary practices. For example, the program issued specific guidance limiting suspensions and expulsions to ensure that children are supported in continuing to receive services. Data on specific metrics would inform the program on whether these policies are being implemented on the ground.

Beyond Head Start, these data would be useful for early care and education settings generally. For a more complete picture of how children of color, especially boys, experience their classroom environments, it is crucial to know the following:

  • Whether and how programs provide specific supports for social-emotional development and mental health
  • How teachers view and respond to behavior and whether they receive anti-bias training
  • How programs ensure that children stay enrolled in and receive services

Measures of equity in classroom quality are as important as measures of intentional instruction or classroom management. State data systems that rate early childhood programs should include measures of equity in their reporting of program quality.

We are experiencing a critical historical moment which offers unique opportunities to re-envision our early childhood and data systems. Federal guidance emphasizes the importance of measuring equity in programs, reporting, and research. National and statewide public datawith the types of enhancements outlined abovecan help us explore how well federal programs are meeting these goals for all children. These data can illuminate barriers and opportunities for meeting program goals. They can highlight the program elements and contexts that shape experiences and determine effectiveness for distinct groups.

By disaggregating program data and considering systemic and structural factors that can explain differences, we can move beyond population averages to unpack the particular experiences of historically marginalized and under-resourced communities. We can begin to answer questions about access and quality of services, child and family experiences, and outcomes to better understand the lived experiences of those who continue to face inequities and bias. Only by building these practices into ongoing data systems can we ensure that the current vision of using data as a tool for advancing equity lives on, no matter where the political and cultural winds blow.

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