## Disaggregating Data

Data-literate educators combine and analyze many forms of data to help make inferences about student understanding and behaviors.
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### Key Method

To effectively make data-driven decisions, educators must first be able to disaggregate, or separate, data in meaningful ways (e.g., “How did students perform, on average, on each standard?” or “Do students who demonstrate greater grit also do better on more difficult items?”). To act effectively on data, educators must separate data into more meaningful and actionable forms.

### Method Components

Educators often have questions or hunches about why data are the way they are. They analyze the data to explore those hunches. This work often demands cutting, or disaggregating, data in particular ways. For example, educators may disaggregate data to help answer the following sorts of questions:

• The class average was 76% on a particular assessment. But did students, on average, perform equally well across all learning standards on that assessment?
• The class average for word problems is 82%. I wonder if my students who are classified as English Language Learners systematically perform differently on these problems than do students without that classification.
• It seems like my female students don’t give up as quickly on math problems. Do my female students have higher grit scores (a measure of perseverance) than my male students?

In each case, educators start with a question or hunch and disaggregate the data, or cut it in intentional ways, to better understand whether there are systematic trends that are not immediately obvious when looking at the data as a whole set.

When disaggregating data, data-literate teachers keep two key points in mind: (1) small differences may not, in fact, be true differences from a statistical point of view (i.e., the differences are not statistically significant), and (2) any possible relationships are often associative and not causal—and usually require additional investigation and unpacking. Disaggregating data is a critical step in determining what data-driven action might be appropriate and where further inquiry may be productive.

## Research & Resources

### Supporting Research

• Confrey, J., Makar, K., & Kazak, S. (2004). Undertaking data analysis of student outcomes as professional development for teachers. ZDM, 36(1), 1–9.

### Resources

• Lachat, M. A., Williams, M., & Smith, S. C. (2006). Making sense of ALL your data. Principal Leadership, 7(2), 16–21.

## Submission Requirements

### Submission Guidelines & Evaluation Criteria

To earn the micro-credential for Disaggregating Data, please disaggregate data from the sample dataset at http://bit.ly/1KaPXwI to answer the following questions:

1. On average, who performed better on this assessment, boys or girls?
2. On average, did students do equally well on each learning standard?
3. Do students struggle more with item type (MC vs. CR) or item rigor?
4. Given data on grit score (perseverance), attendance, feeder school, and homeroom, which factors, if any, seem most associated with student performance on the assessment?

For each question, please submit the result of the data analysis (e.g., provide a table displaying how the class performed on each learning standard, on average) and describe, in fewer than 100 words, the confidence in the answer and what additional information or data would help improve the confidence.

Your submission will be assessed on the following rubric. You must earn a (3) Proficient or (4) Exemplary on this portion of the submission in order to earn the micro-credential.