Adding Variables

Two surveys recently looked at public acceptance of evolution in a range of countries. This one arranged the data in order of decreasing acceptance.


Note the pretty good color choice: red for rejection, something wishy-washy for “don’t know.” I would have gone with a dark green for acceptance, if only because the red/blue contrast is so over-used. The white spaces between the bars are a little obtrusive. Here’s what the New York Times did with it in the August 15th issue. They dropped the numbers, and left the “don’t know” category the same color as the white space between bars, which breaks up those obtrusive horizontal lines. Fading back the colors also helped.


How would the graph have been different if the bars had been sorted by increasing rejectionof evolution? Greece would bump lower, Japan higher, and there’d be some shuffling in the middle, but the key point I think the authors wanted to make—that the USA is way down with Turkey—would have been preserved. But perusing the list a little raises all sorts of questions. Why are the Czechs and Slovaks more likely to reject evolution than the Bulgarians? Why are the Finns twice as likely as the Swedes and Danes? Poland and Ireland are two of the most religious countries in Europe, but they’re near the middle of this list. The graphic raises more questions than it answers. Part of the problem is that it’s only depicting three variables (not four; one category is just the remainder of the other two, as the Times recognized). And two of those are not really independent, having a roughly inverse relationship. So we don’t have enough information to do any analysis ourselves.

Here’s another presentation of similar data (I’ve added a key).


Now, this is not a slick graphic. I would certainly have played with the spacing, font size, alignment, ALL CAPS labels, and colors. But by choosing a scatter plot instead of a bar chart, and making the country name into the marker, seven variables are being depicted in the same space the previous graph could only manage two-and-a-bit. Moreover, we’re now equipped to evaluate trends ourselves, look at outliers (the churchgoing Irish, the oddly evolution-rejecting Dutch), and examine our preconceptions (there may be very few atheists in the USA, but that alone doesn’t account for the lack of acceptance of evolution). And note that the simple take-home message of the first graph is not being sacrificed, either: the USA stands out each time.

(The site itself has another 8 sets of charts, each with a different variable on the y-axis, and you can click through to see them appear one at a time in the same space, for easier comparison.)

We sometimes forget just how much information can be depicted in a data graphic. In a previous post I noted that exploratory and educational graphics are the extremes of a single axis. But surely a worthy goal is to do both: make your point, and include enough information for the reader to do their own hypothesis testing. Adding some variables is a good way to start.

Jon D. Miller, Eugenie Scott, and Shinji Okomoto. Public Acceptance of Evolution. Science, August 11. 765–766. PDF online.
Paul, Gregory S. 2005. Cross-National Correlations of Quantifiable Societal Health with Popular Religiosity and Secularism in the Prosperous Democracies. Journal of Religion and Society, vol. 7. Online.