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 In DataBlog, Projects

Interaction Effects are links between categories of info that can take data to the next level. And speaking of making the links: we need to interact more effectively with Interaction Effects. It’s a mind bender. Explaining Interaction Effects can be tricky at the best of times. Explaining them to time-pressured boards and policymakers is a tough challenge. How can we prep Interaction Effects for mainstream attention?

Making the Interaction very clear is the first step. Example: one of Datassist’s research projects with CARE studies Bangladeshi milk farmers. We found that a group leader’s gender was correlated to income. The basic graph to show off this data might look like this…

Misleading Table

Farm Leader Gender

Income from Milk

Percent Increase

Female Leader

$1,432

8% higher

Male Leader

$1,349

3% higher

… which only shows the data in isolation. Looking at the data this way can lead community organizers to pursue inefficient interventions.

By testing for Interaction Effects, we learn more. Sure enough, a leader’s gender has a strong interaction with their team’s gender. To share this information with the people who need it, it’s usually best to show a Main Effect (i.e. leader gender) with its related Interaction Effect (leader gender/group gender). So our table should look more like this…

Clear and Accurate Table

Group composition

Farm Leader Gender

Income from Milk

Percent Increase

Primarily Female

Female Leader

$1,490

12%

Male Leader

$1,330

2%

Primarily Male

Female Leader

$1,375

5%

Male Leader

$1,368

5%

With a little study, the data is readable.

Tables are an easy way of laying out the results of Interaction Effects if your audience has time to take it in. Fortunately, there are ways to make it comprehensible even at a glance.  Next week, I’ll look at infographics that best showcase this kind of data for the busy stakeholders in your life… that is, all of them.

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