Data Tip #6: When Analyzing Causes, Ask "Why? Why? Why?"
“Learn from yesterday, live for today, hope for tomorrow. The important thing is not to stop questioning.” Albert Einstein
Once a data team has analyzed several data sources to pinpoint a student learning problem, they often feel ready to leap into action and solve it. But the data team should first engage in a collaborative process of causal analysis to identify the 'root' cause of the problem, to ensure that the solution they propose addresses the true problem and produces the desired results.
One tool data teams can use to support a root-cause analysis is called “Why? Why? Why?”—a questioning technique used to explore cause–and-effect relationships. “Why? Why? Why?” helps a group look deeply, beyond the symptoms of a problem, to find underlying causes by asking “Why?” at least three times. Each time the question is asked, the team is probing more deeply into the root cause.
For example, suppose your team learns that math scores on the state test noticeably improved for all students except those in the bottom quartile. On the first round of “Why?” team members respond that many of the bottom-quartile students are special education students. Asking “Why?” a second time, they speculate that the new math curriculum, which is closely aligned with the state test, is just too hard for some students. When asking “Why?” a third time, 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 lack of access could be the root cause!
Are you ready to give “Why? Why? Why?” a try?
- Define a student learning problem. Be sure to analyze at least three data sources to accurately pinpoint the problem.
- Clearly state the student learning problem in writing on chart paper (or use the Why? Why? Why? form)
- Engage in collaborative dialogue with your data team. Ask “Why” do we have this problem? Then record one response beginning with “Because…”
- IMPORTANT: Next, discuss whether your cause needs confirmation. What other data can be consulted?
- Continue this process by repeating Steps 3 and 4 three or more times.
- Discuss the data-confirmed causes of your learning problem. Which one seems to be the 'root' cause—the one that, if changed, will yield results? Now your team is ready to start generating solutions. But be careful—the “Why? Why? Why?” process has some limitations.
The “Why? Why? Why?” process is not scientific. Different groups might identify different root causes based on only their current knowledge or experiences, which have inherent limitations. That’s why Step 4 is important. Think of “Why? Why? Why?” as a good starting point for launching the dialogue that will move your team toward a better understanding of the problem, before you target a solution.
To help data teams discuss a wide range of possible causes for learning problems, TERC’s Using Data offers a set of Causal Analysis Cards that suggest research-based causes for learning gaps related to content area curricula and/or student subgroups. Click here for more information or to order a set of Causal Analysis Cards.
Written by: Diana Nunnaley, Director
Mary Anne Mather, Facilitator
TERC's Using Data
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