The “Before” Version: Data Table in the Research Report

My paper was filled with these:

A major piece in the data visualization puzzle is understanding your audience. In this case, my audience was my professor and the grad students in my lab. These research-savvy folks wanted and expected the correlation table to be formatted exactly like this in APA format. Case closed.

Another “Before” Version: Data Table in the Presentation

But what if I were presenting a summary of my findings to classmates?

We concluded this year-long research process with class presentations.

My nerdy friends and I took screenshots of our tables and squished them into PowerPoint slides. Then we took turns presenting our squished tables to each other. And then we took turns groaning and complaining and yawning as classmate after classmate squinted their eyes, strained to see the tiny fonts and asterisks in our tables, and consequently learned very little from our presentations.

My unreadable PowerPoint slide probably looked something like this, default slide template and all:

The “After” Versions: Data Table in the Research Report, Heat Table in the Presentation

I’m still obsessed with data. I’m especially obsessed with transforming arcane numbers into information that everyday people can really, truly understand.

Would I have changed the table for my research report? No way. The professor and grad students were expecting the table to be formatted exactly like it was. The grad students even shared templates with me as I was drafting my report so I could make sure the borders and spacing were exactly right. Peer-reviewed journal articles and academic papers have specific formatting guidelines and I don’t mess with those.

Would I have changed the table for my presentation? You bet. 

Nowadays, I would display the correlations in a heat table (a.k.a. density table) where higher numbers get darker colors and lower numbers get lighter colors.

With the original data table, our eyes can’t even see asterisks from the back of the room, and our brains need to spend time reading the table cell by cell by cell to make sense of the asterisks. But with the heat table, our eyes can sense color saturation from the back of the room, and our brains can follow patterns instantly.

I’d also present my table with the storyboarding technique, meaning that I’d break up the content into five separate slides. I’d take a few moments to talk through each portion of the table, glancing around the room and making sure my audience really understood what I was talking about before moving on to the next slide.

Slide 1: Showing outline of table

Sample talking points: “I ran a correlation analysis to see whether any of the 10 constructs were correlated with each other. I aligned the constructs in a table, with the constructs listed down the left side and again across the top.”

Slide 2: Showing table’s full contents in black text

Sample talking points: “This table shows the results of the correlation analysis. For example, in the upper left hand corner, ‘adolescent depression’ and ‘maternal depression’ had an r of just -.04; no association there.”

Slide 3: Talking through the correlations that were significant at p<=.001

Sample talking points: “Let’s take a look at the most promising trends first. Six of the correlations were significant at p less than or equal to .001. For example, check out that -0.61 on the left. Alienation and communication were connected. When the adolescent felt alienated from his or her depressed mother, the adolescent was also really likely to report having communication problems with the mother.”

Slide 4: Talking through the correlations that were significant at p<=.01

Sample talking points: “Eight additional correlations were significant at p less than or equal to .01. For example, check out that .32 in the bottom right. Teens who used marijuana more frequently were also more likely to report using hard drugs.”

Slide 5: Talking through the correlations that were significant at p<=.1 and pausing for discussion

Sample talking points: “Finally, the light green shading shows where constructs were correlated with each other with a p-value less than or equal to .05. There were a bunch of these, like that .24 in the upper left corner. The .24 means that teens with higher levels of depressive symptoms were also a little more likely to report feeling alienated from their mothers. … Any clarification questions? … What additional patterns stand out to you?”

Does it take extra time to transform your traditional table into a heat table? Yes, probably 30 minutes.

Is it worth it? Absolutely! Your up-front planning will save your audience time and energy. You’ll look smart and prepared, and your audience will learn something new from your presentation rather than rolling their eyes at your smushed table.

Want the slides used in this post? Sign up for my newsletter and I’ll send them out next week.

In today’s post I’m teaching you:

• how to explore your data tables with Excel’s conditional formatting;
• how clustered bar charts are the worst charts ever;
• how a small multiples chart can save the day by quickly visualizing an entire table’s worth of details in the same amount of space as the original table; and
• how to create that small multiples chart in Excel (and it takes less than 5 minutes, I promise).

The Original: A Data Table

This example comes from a short and sweet Child Trends brief: www.childtrends.org/wp-content/uploads/2013/07/125_Flourishing_Measures.pdf. This is a pretty typical situation in research and evaluation projects in which we’ve got some interesting variables across the top and multiple demographic breakdowns along the side.

Option A: Conditional Formatting

I wanted to see the patterns and understand what was going on. For starters, I used conditional formatting to color-code any cells that were greater than the overall percentage. For example, in Column B, 93% of children “always” or “usually” were affectionate and tender with their parent. Girls were slightly more likely than boys to be affectionate and tender (95% of girls compared to 93% of children overall) so the 95% is highlighted.

Now what can I see? Something cool is going on with the kids above poverty and whose parents have higher levels of education.

This conditional formatting technique is a good start, but it didn’t seem to show me enough patterns, so I kept going.

Option B: Data Bars

Data bars–within-cell bar charts–are another underused conditional formatting technique for exploring preliminary patterns from the comfort of your spreadsheet. I teach you how to make them here.

What’s happening with column C? Why are those bars so short? Why are the kids less likely to bounce back? Now that I can see what’s going on, my brain’s full of questions.

Option C: Clustered Bar Chart

I made one of these bad boys just for fun, and the result was even more incomprehensible than I could have imagined. The not-so-fun part is that I still see charts like these in research and evaluation reports. If you’re still using these, please, contact me and I’ll help you out.

Option D: Small Multiples Bar Chart

I could’ve visualized this data through a million different individual bar charts: a bar chart showing the overall percentages for each of the four indicators (the option selected by the authors in their original report), a dot plot showing the gender differences for one or two or three or four of the indicators, a bar chart focusing on parental education, etc.

Instead of highlighting one or two patterns, I wanted to see the entire landscape of what was going on. Same real estate as the original data table. The chart could fit on just one page within the body of a report or in an appendix. But now, my brain receives the information through both images and text, rather than just text alone like in the original data table.

And since my primary goal was to see the big picture, I opted for a generic title, generic subtitle, and emphasis-free color scheme. In other words, my title just states the graph’s content rather than putting forth a “so what?” (it’s modeled after a data table from an appendix, after all). My subtitle describes the data source but doesn’t give my interpretation of what’s going on. My colors are matched to Child Trends’ logo rather than Excel’s default color scheme, but the colors don’t emphasize one category or series of data over another. I’ll describe these distinctions in more detail in a future post.

(You can also view the high resolution image here.)