Is this an example of good or bad dataviz?
Dec 23, 2020
This chart is giving me feelings:
I first saw it on TV and then a reader submitted it.
Let's apply a Trifecta Checkup to the chart.
Starting at the Q corner, I can say the question it's addressing is clear and relevant. It's the relationship between Trump and McConnell's re-election. The designer's intended message comes through strongly - the chart offers evidence that McConnell owes his re-election to Trump.
Visually, the graphic has elements of great story-telling. It presents a simple (others might say, simplistic) view of the data - just the poll results of McConnell vs McGrath at various times, and the election result. It then flags key events, drawing the reader's attention to those. These events are selected based on key points on the timeline.
The chart includes wise design choices, such as no gridlines, infusing the legend into the chart title, no decimals (except for last pair of numbers, the intention of which I'm not getting), and leading with the key message.
I can nitpick a few things. Get rid of the vertical axis. Also, expand the scale so that the difference between 51%-40% and 58%-38% becomes more apparent. Space the time points in proportion to the dates. The box at the bottom is a confusing afterthought that reduces rather than assists the messaging.
But the designer got the key things right. The above suggestions do not alter the reader's expereince that much. It's a nice piece of visual story-telling, and from what I can see, has made a strong impact with the audience it is intended to influence.
This chart is proof why the Trifecta Checkup has three corners, plus linkages between them. If we just evaluate what the visual is conveying, this chart is clearly above average.
In the D corner, we ask: what the Data are saying?
This is where the chart runs into several problems. Let's focus on the last two sets of numbers: 51%-40% and 58%-38%. Just add those numbers and do you notice something?
The last poll sums to 91%. This means that up to 10% of the likely voters responded "not sure" or some other candidate. If these "shy" voters show up at the polls as predicted by the pollsters, and if they voted just like the not shy voters, then the election result would have been 56%-44%, not 51%-40%. So, the 58%-38% result is within the margin of error of these polls. (If the "shy" voters break for McConnell in a 75%-25% split, then he gets 58% of the total votes.)
So, the data behind the line chart aren't suggesting that the election outcome is anomalous. This presents a problem with the Q-D and D-V green arrows as these pairs are not in sync.
In the D corner, we should consider the totality of the data available to the designer, not just what the designer chooses to utilize. The pivot of the chart is the flag annotating the "Trump robocall."
Here are some questions I'd ask the designer:
What else happened on October 31 in Kentucky?
What else happened on October 31, elsewhere in the country?
Was Trump featured in any other robocalls during the period portrayed?
How many robocalls were made by the campaign, and what other celebrities were featured?
Did any other campaign event or effort happen between the Trump robocall and election day?
Is there evidence that nothing else that happened after the robocall produced any value?
The chart commits the XYopia (i.e. X-Y myopia) fallacy of causal analysis. When the data analyst presents one cause and one effect, we are cued to think the cause explains the effect but in every scenario that is not a designed experiment, there are multiple causes at play. Sometimes, the more influential cause isn't the one shown in the chart.
Finally, let's draw out the connection between the last set of poll numbers and the election results. This shows why causal inference in observational data is such a beast.
Poll numbers are about a small number of people (500-1,000 in the case of Kentucky polls) who respond to polling. Election results are based on voters (> 2 million). An assumption made by the designer is that these polls are properly conducted, and their results are credible.
The chart above makes the claim that Trump's robocall gave McConnell 7% more votes than expected. This implies the robocall influenced at least 140,000 voters. Each such voter must fit the following criteria:
- Was targeted by the Trump robocall
- Was reached by the Trump robocall (phone was on, etc.)
- Responded to the Trump robocall, by either picking up the phone or listening to the voice recording or dialing a call-back number
- Did not previously intend to vote for McConnell
- If reached by a pollster, would refuse to respond, or say not sure, or voting for McGrath or a third candidate
- Had no other reason to change his/her behavior
Just take the first bullet for example. If we found a voter who switched to McConnell after October 31, and if this person was not on the robocall list, then this voter contributes to the unexpected gain in McConnell votes but weakens the case that the robocall influenced the election.
As analysts, our job is to find data to investigate all of the above. Some of these are easier to investigate. The campaign knows, for example, how many people were on the target list, and how many listened to the voice recording.
There are gridlines, they're just very subtle, which is good in its own way.
I agree about the scale, redundant when all the data points are individually labelled. Also, even if there were no data labels, there would still be too many labels in the vertical scale. My rule of thumb is that there should be substantially fewer labels in a vertical scale than there are data points. Here there are eleven levels from 0 to 100%, for only sixteen data points. Not as bad as cases I've seen where the vertical regularly spaced labels outnumber the data points!
Posted by: derek | Dec 23, 2020 at 05:12 PM