A core principle of effective data use is that savvy analysts always triangulate the data. They look at multiple measures to confirm what they are finding in any single set of data. A single source of data, as rich and as statistically accurate as it can be, isn’t worth the paper (or screen) it’s printed on, if there aren’t additional sources of data to confirm your inferences. Keep in mind, the results of your analysis are just that – inferences.

Relying on a single research source can be just as misleading. Are the conditions described similar to your own school’s context? Was the purpose of the research to examine similar factors? Data teams greatly increase their knowledge of the contexts and variables that come into play when they triangulate their research. Take for example the following three documents that your Data Team might study if your biggest achievement gap involves kids from high poverty backgrounds:

Dropouts: Finding the Needles in the Haystack by Eric Sparks, Janet L. Johnson, and Patrick Akos published in the February 2010 issue of Educational Leadership;

The Pedagogy of Poverty Versus Good Teaching by Martin Haberman, https://www.det.nsw.edu.au/proflearn/docs/pdf/qt_haberman.pdf and

The Kids Left Behind: Catching up the Underachieving Children of Poverty. Barr, R.D., & Parrett, W.H. (2007). Bloomington, IN: Solution Tree.

What can we learn about the impact of poverty on social, emotional, and intellectual learning? What are the variables we need to watch? What are the Best Practices that have been found to address the learning challenges? To what extent do those best practices exist in our classrooms, our schools?