Analyzing Data
There is no single method for analyzing data, but here are some suggestions whether you are a parent/family member, teacher, staff member, or educational leader:
Work as a Team
Take the data for a test drive by getting in the learning zone
Take Time
Give yourselves time to analyze, reflect, and inspect the data and try to avoid jumping to conclusions
Look for Trends
Find consecutive data points heading in the same direction (up, flat, or down)
Develop Questions
Discuss why these outcomes might exist
Create Context
Look for Differences
When analyzing data in our schools it is easy to form simple opinions without considering the complexity of the data. For example, if the student achievement results decreased, it might be easy to simply say the results were bad this year. However, no matter the results there are always areas to celebrate and areas to improve. Consider the following questions when analyzing data:
What are the bright spots or areas for celebration?
Are the results improving, staying the same, or getting worse?
Where is our system supporting students successfully and where do we need to work differently to support students who are struggling to meet our expectations?
Which practices, structures, or systems created the conditions that led to these results?
What could we change to get different results in the future?
There are many tools or “data protocols” for analyzing data, and while no single tool is best, it is helpful to have a structured data analysis process, especially when a group of people are analyzing data. Here are some simple data analysis protocols that can be used by parents/family members, teachers, staff, and other educators.
This is another good place to collect qualitative data because after an analysis of quantitative data is completed, people may have questions about why the scores improved or why the attendance decreased. In other words, it might be helpful to go back and talking with the students, teachers, parents or other people involved to understand the story behind the quantitative data.