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Relay Data Protocol

Equity-Centered Data Principles and Practices

The principles and practices below can help ensure that data analysis is centered around equity.

Conscious Analysis: Examine Bias when Analyzing Data

Biases are a function of automaticity, so slowing down and using debiasing strategies when analyzing data helps to counter biases we have.* 

Types of Bias Definition What's the Impact Debiasing Strategies
Negativity Bias Focusing mostly or solely on what didn't go well Focusing only on what didn't go well prevents you from leveraging strengths and understanding what actions were effective in the teaching and learning process

Embrace an asset-based mindset. Ask: 

  • Where are there bright spots in the data?
  • Where is there evidence of student growth?
Availability Bias Only using the information readily available to make a key decision Using only the information that is readily available to you to make a key decision causes you to develop uninformed solutions that may not address the underlying issue

Look for data within a broader context. Ask:

  • What does the data not show?
  • Whose voices and experiences are not represented within the data?
  • What questions about the data might you want to investigate to gain more context?
Confirmation Bias Interpreting new evidence as confirmation of one's existing beliefs Looking only for those data that support your hypothesis or preconceived notions limits your ability to synthesize and reflect on the data that is in front of you

Look for data that confirms and challenges your assumptions. Ask:

  • Which students do you tend to gravitate toward and why?
  • What assumptions, stereotypes, or deficit-based scripts are running in your brain about particular students, groups of students, or families?
  • To what extent does the data confirm and/or challenge your assumptions?
Hindsight Bias Seeing results and assuming they were predictable Seeing results and assuming they were predictable leads you to be overconfident in your abilities and judgments, prevents you from learning from your experiences, and downplays the role of luck

To counteract the sense of inevitability, ask:

  • What other outcomes could have happened? How could those outcomes have happened?

* Kahneman, D. (2011). Thinking, fast and slow. Farrar Straus Giroux.

Inclusive Analysis: Attend to the Learning Needs of ALL Students

  • Review student data by subgroups to identify and respond to any disparities—i.e., by gender, race, ability, language needs
  • Ground data analysis and action planning in thoughtful attention to the needs of most vulnerable students through questions such as:
    • Based on the data, who is being served well? Who isn’t?
    • Will the action plan serve all kids? Who may be left out?
    • What additional support may be needed?
    • Who are the right people to intervene?

Contextualized Analysis: Take Context into Account When Analyzing Data

  • Ask yourself: What does the data show? What doesn’t it show?  
  • Generate investigative questions about the data
  • Use multiple data points—including, assessment data, attendance data, student work, surveys, observation of student actions, and oral responses
  • Talk and listen to students. Ask them:
    • How are they feeling about their learning environment and themselves? 
    • What might be impeding their potential?

Collaborative Analysis: Work Together to Meet the Needs of ALL Students

  • Collaborate with general educators, special educators, language teachers, families, and other stakeholders to ensure high-quality instruction across settings
  • Schedule routine opportunities to analyze data together using data analysis protocols like Relay GSE’s Equity-Centered Data Analysis Protocol

Resources Referenced and Consulted