View or share this material in a Google Doc: Addressing Bias in Data Analysis
Objective
To determine trends across subgroups or bias toward students by analyzing student data in order to set and meet professional goals related to Culturally Responsive-Sustaining practices.
The estimated time for this activity is 40–45 minutes.
Explore this Website
“If you don’t know where you are going, you will probably end up somewhere else.” — Lawrence J. Peter
Data collection and analysis plays a critical role in students’ academic, social-emotional, and overall achievement. It also plays a large role in the professional goals set within a classroom, in a school, and across a district.
According to The Glossary of Education Reform, “student-level data refers to any information that educators, schools, districts, and state agencies collect on individual students, including:
- personal information (e.g., a student’s age, gender, race, place of residence),
- enrollment information (e.g., the school a student attends, a student’s current grade level and years of attendance, the number of days a student was absent),
- academic information (e.g., the courses a student completed, the test scores and grades a students earned, the academic requirements a student has fulfilled),
- and various other forms of data collected and used by educators and educational institutions (e.g., information related to disciplinary problems, learning disabilities, medical and health issues, etc.).”
In “6 Steps to Equitable Data Analysis,” author Andrew Knips recognizes that “for teachers and administrators, it’s easy to overlook culture and identity when analyzing data. However, ignoring students’ diversity markers means pretending that their identity doesn’t matter. In order to close gaps in student outcomes, we must name equity as an essential component to data analysis.” Read the article and summarize Knips’s 6 Steps for educators:
- Research identity
- Preempt implicit bias
- Frame and challenge
- Set intentions
- Pick the right data
- Strategically sort
Stop & Think
(Key: T — Teachers, SL — School Leaders, DL — District Leaders)
- How would a racial preference impact data analysis in your learning environment? (T, SL)
- How do biases and stereotypes (either yours or others’) affect academic achievement in your classroom/school/district? (T, SL, DL)
- What assessments do you use to collect data? What metrics do you consider other than standardized tests? (T, SL, DL)
- What intentions do you have for analyzing data regularly? How will your intentions affect student achievement? (T, SL, DL)
Take Action
At your next data analysis meeting, either individually or with a team, set a goal using one of the above 6 Steps to Equitable Data Analysis. Write your goal on a piece of paper, at the top of your digital spreadsheet, or on a Post-it.
Before the meeting, agree to norms and protocols for your meeting that will support this step. Refer to the bullets below for ideas.
Define roles that could include, but are not limited to:
- Timekeeper
- Recorder
- Facilitator (keeps the conversation on topic and points out ideas, words, or actions that do not support your goal)
- Synthesizer (synthesizes data analysis into trends and/or insights)
How will you challenge ideas, words, and/or actions that do not support your goal?
- Consider language that will address an idea, word, and/or action and promote inclusive dialogue.
After the meeting, discuss what worked. What didn’t? How can you ensure that this step is followed in future meetings? What step would you like to take next?