causal analysis


By Mary Anne Mather, Managing Editor
TERC’s Using Data for Meaningful Change Blog

Any parent or teacher can identify with the “why” inquisition. For a parent, it’s those moments when your child questions an observed phenomenon like “Why is the sky blue?” You give an explanation, and then the child asks why again…and again. For a teacher, it’s the proverbial student the word why and many question marks on wooden type-face blocksquestion, “Why do I need to know THIS?” You explain, and then you hear, “But why is that important to ME?” At times, the whole experience, for certain, can test one’s level of knowledge, not to mention your patience. And yet, we all understand that learning is about inquiry and discovery. Asking “why” should be something to rejoice in! And asking “why” should remain a lifelong learning tool. It doesn’t stop after childhood.

I recently came across a list at the Teach Thought website that caused me to reflect again on a very essential phase of the Using Data process—Causal Analysis. Informally, we call it why-why-why. The list posted at Teach Thought, 10 Silent Disruptors Of Student Academic Performance aligned pretty closely to some of the causes that we help teachers to explore during causal analysis in order to justify phenomenon existing in their schools that can be altered, and by doing so, leads to increased student achievement. In fact, we offer a set of cause cards that scaffold the reflection process.

In order to use data well, the why-why-why phase of data analysis through collaborative inquiry is as essential as the air we breathe if, as educators, we are going to get beyond the surface of things and accurately pursue meaningful differences that will impact student achievement. We can have mountains of data, and we can even take a step further and analyze those mountains. But until we connect the dots among multiple data sources, discover specific student learning challenges, and then take the time to ask “why-why-why” in order to verify the root causes of these challenges, selecting solutions is premature.

For sure, the data consulted must reach beyond numbers and test scores because the causes can reside in scheduling, misalignment of curriculum and assessments, mismatched vocabulary between what is taught and what is tested, lack of rigor or teacher content knowledge, low expectations for selected student groups, and more. The causes might be any or all of the 10 disrupters on the Teach Thought graphic. The good news is that verified causes can be addressed, and the results can be astounding!

For a step-by-step description of one why-why-why activity, check out Using Data’s Tip #6, When Analyzing Causes, Ask “Why? Why? Why?”.

GUEST BLOGGER: Mary Anne Mather, Using Data Senior Facilitator & Social Media Liaison on Twitter & FaceBook

“Learn from yesterday, live for today, hope for tomorrow. The important thing is not to stop questioning.” Albert Einsteinmagnifying glass trained on the word why in red text

Once a school or grade-level data team has analyzed several data sources to pinpoint a student learning problem, they often feel ready to leap into action and solve it. To ensure that the solution pursued produces the hope-for results, it’s essential to engage in a collaborative process of causal analysis to identify the “root” cause of the problem.

There are many tools that support root cause analysis, one of them is referred to as Why-Why-Why—a question-asking technique used to explore cause and effect relationships. Why-Why-Why helps a group look beyond symptoms to underlying causes by taking the identified problem and asking why it exists at least three times—each time probing more deeply. (more…)