In the previous post, we talked about the differences between qualitative and quantitative data. In this post, we’ll talk about how to use these data types together to form a full picture of student performance.
Different data can tell you different things. For instance, you can learn that a student is struggling with reading from an achievement test score, but you cannot learn that the student is dyslexic from that score. That requires the triangulation of both quantitative and qualitative data.
Triangulating data is very important when employing it to drive instruction. Without both quantitative and qualitative data, it’s hard to get a full picture of how students are benefitting or not benefitting from instruction. What data you use depends on the question you are asking (more on forming a good question next week).
If you are asking the question “how many students failed the state achievement test?”, you just need state achievement test passing data. If you are asking the question “why did 10 students fail the state achievement test?”, you will need more than that. You will need state achievement test data, student work, classroom observations, and maybe even student benchmark assessments. Using all of these sources of data will allow you to get a full picture of what each student (or group of students) needs to be successful.