One of the biggest things we’ve learned working with schools is that teachers and principals are overwhelmed with the amount of data they have access to. With so many ways to assess the same student, it can be difficult to figure out how to piece the data together in an actionable way. In this blog series, we’ll address some of the basics of using data to drive instruction.
First, in order to use the data, you need to know what kinds of data there are. There are two main types of data: qualitative and quantitative. Quantitative data are things like test scores and counts. It’s a measurement in numbers that you can analyze and compare. Qualitative data are things like observations and interviews. These data cannot be manipulated via statistical methods.
The relationship between quantitative and qualitative data can be complex, but in general, qualitative data helps add context to the quantitative data. For instance, If you see that a student has done poorly on their mathematics benchmark and you notice that this student answers questions correctly in the classroom, unless the question includes a fraction, it is reasonable to assume that this students’ poor test performance is a result of a misconception around fractions. In this way, the quantitative and qualitative work together to help the teacher decide on a plan of action. This process is called triangulation and we will discuss it further in the next post.