February 3, 2017

How to mislead with maps. The Gallup State Well-Being Rankings for 2016

Gallup has recently released another population survey, this time it is the 2016 State Well-Being Rankings. Gallup's accompanying map (last page of the rankings) is, as you can see, split into quintiles. If you want, you can go over there and look at their map or look at the first map below. It represents the cut-offs in approximately same colors. (If you mouseover the map, you'll get more information by state.)

State Health Ratings, Colored by Quintile
State Health Ratings, "Squashed" Range
State Health Ratings, Full Range

This map is an excellent example of how data presentation choices mislead. People are supposed to use quintiles, quartiles, percentiles, and other such non-parametric numbers to represent either data that has a long, uneven, and strung-out range (like achievement test scores), or to group a different set of data to show how it is distributed (like wealth per quintile). It just so happens that you can look at the well-being scores for yourself in the linked report. Notice that the data is not strung-out and scattered. In fact, it is very densely-packed. It also is not explicitly linked to some other unevenly-distributed data.

The actual range goes from 58.9 to 65.2. Is a difference of about 6.3 score points worth that much a visual difference?

How else could we represent the difference so people can get an idea of reality instead of a visual trick?? The second, or "squashed scale" map does that. The "worst color" (light gray-green) is matched to score of 58.9. The "best color" is matched to 65.2. The range between is then evenly filled in among the five color points. Look different? It does. There is some rough correspondence between the misleading map that comes from Gallup and the (somewhat) more truthful map I created, but you can now immediately see that the country is not divided into stark and extreme categories. You can also immediately see that the distances between categories are not sharply defined.

But I'm not finished. You see a third map. This is a map where the "best color" corresponds to a score of 100 (maximum theoretical possible score) and "worst color" corresponds to 0 (minimum possible theoretical score) Changes in color now correspond to linear differences along the full possible range. Having a hard time telling the states apart? That is because the differences among them in this index really are tiny. This map shows you what that actual difference looks like in context of the full scale.

So, why does Gallup do this? Why do people eagerly swallow such representation of data? First, explaining Gallup. I don't work there, so this is speculation, but Gallup makes its money off controversy. Anything they publish that will stir the pot will inspire more surveys that they can sell. Likewise, presenting things in extreme ways ensures that there will be more arguments, leading to more survey commissions, leading to similar data presentation, leading to more arguments. It's a lucrative circle for Gallup.

But why do people so eagerly devour this quasi-information? First, it's simple. People like very stark, very simple things to natter on about with each other. People do not like complex and shaded descriptions. They want things to be very neatly pigeonholed, and this comforts them. In addition, people with agendas want things presented as rigidly and extremely as possible to the public, all the better to sound the panic alarm. Finally, we are often taught by society that only rigid and extreme answers can be "true". We are indoctrinated to see the world as "good" and "evil" with nothing in between. We are taught that someone who is able to see gradual differences is a "fence-sitter" or "spineless". We are told that only extremism is good--although it's only actually extremism when it's someone you don't like doing it.

I don't know if this changed the way you see the world, but I hope it helped you understand and be more critical of "studies", "surveys" and "polls" that we are not flooded with.

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