Interrogate bias in data analysis by unpacking biases with colleagues and building skills to name bias in action¶

Strategy · All strategies
Evidence · no claims cited

Description¶

This strategy addresses the unconscious bias all analysts bring to data interpretation, which the brief says can take many forms, including racial and ethnic bias, bias toward certain behaviors and attitudes, and bias about teaching and learning. The brief recommends "Working with colleagues to unpack biases, creating space to be honest and vulnerable about biases, and building skills to name bias in action" as processes supporting more effective data use, citing Newell (2020).

Design Implications¶

Context¶

Requirements¶

• Colleagues with whom to unpack biases; space for honesty and vulnerability; skill-building to name bias in action

Constraints¶

Target Learners¶

• students affected by biased interpretation of education data

Target Learning Goals¶

• more effective, more equitable data use

Related Strategies¶

• Use data collaboratively through a structured data analysis process among educators
• Awareness of Implicit Biases
• Know Your Own Perspectives
• Understand Your Bias
• Checking Your Blind Spot
• Addressing Implicit Bias
• Keep students' lived experiences at the center of data use and ensure data reflect students' lives

Examples¶

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Key Sources¶

• Jessica Arnold, Julie Webb. (2024). 7 Recommendations for Using Education Data to Support Equitable Learning Outcomes. WestEd. https://eric.ed.gov/?id=ED655653