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.

the word why and many question marks on wooden type-face blocksFor example, your team learns that math scores on the state test noticeably improved, except for students in the bottom quartile. On the first round of “why” team members respond that many of these students are special education or Title I. On the second round they speculate that the new math curriculum, which is closely aligned with the state test, is too hard for some students. On the third round they consider that often the special needs students are pulled out of class for individual instruction and may not be getting access to the new curriculum. This could be a root cause!

Are you ready to give Why-Why-Why a try?

Action Steps

  1. Pinpoint a student learning problem. Be sure to analyze at least three data sources for confidence that the problem is valid. Clearly state the student learning problem in writing on chart paper (or use the Why-Why-Why Form at under the heading Are You Using Your Data Well?).
  2. Engage in collaborative dialogue with your data team. Ask “why” do we have this problem, and record one response beginning with “because…”
  3. IMPORTANT: Then discuss if this “because” needs confirmation. What other data needs to be consulted to be sure?
  4. Continue this same process three or more times. In business, five is recommended.
  5. Discuss the data-confirmed causes. Which one seems to be the “root” cause—the one that will get results if changed? This is your “solutions” starting point. But be careful, the Why-Why-Why process has some limitations.


The “Whys” process is not scientific. It can’t be repeated, and different groups might uncover different potential root causes based on the limitations of their current knowledge or experiences. That’s why Step #4 is important. Think of Why-Why-Why as an easy and effective starting point for launching the dialog that will move you from problem to targeted solution as you collaboratively engage with data-driven decision-making.

To help data teams think about causes beyond what might be immediately identified, TERC Using Data has developed a set of Causal Analysis Cards that suggest causes grounded in research. You might consider using these as your next step toward discovering, confirming, and discussing solutions that will improve student achievement.

Website: TERC Using Data
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