Now, before you try to fit all your data into Marimekko charts, do read up on some valid criticism out there (here, here, here, and here). This quote from Stephen Few is particularly apt:
"In addition to realizing that it isn’t necessary to force everything into a single graph, it’s also important to realize that no single view of data will ever answer every question. This is an underappreciated fact of visual analysis."
In that light, here are some alternatives:
Don’t try to create a continuous x-axis and build out a full Marimekko. Instead, use discrete headers and the measure on Size. This can be a good starting place before building a Marimekko because this is entirely created via drag-and-drop:
Or make the bars 100% stacked bars in a couple more clicks using a quick table calculation (this is described in Tableau’s original Marimekko KB article):
Both of the above views are very busy with the number of Applicants on the Size shelf. Another alternative that shows the effect of that variable is to avoid using the Size shelf and add properly-weighted reference lines using the calculations described above for adding reference lines to the Marimekko:
Here’s another couple of views using the four-sets-of-bars idea suggested by Stephen Few. I don’t think either quite gives the sense of proportion that the Marimekko or the above views do since this data set has the additional Admission Status dimension. But given how fast they are to build, they are certainly worth checking out:
One question that remains is: What was going on back in 1973 that made for such large differences between genders in the number of applicants per department and created Simpson’s Paradox for acceptance rates? To answer that, here’s an extended quote from the concluding summary of the original paper:
If the data are properly pooled, taking into account the autonomy of departmental decision making, thus correcting for the tendency of women to apply to graduate departments that are more difficult for applicants of either sex to enter, there is a small but statistically significant bias in favor of women. The graduate departments that are easier to enter tend to be those that require more mathematics in the undergraduate preparatory curriculum.
The bias in the aggregated data stems not from any part of discrimination on the part of admissions committees, which seem quite fair on the whole, but apparently from prior screening at earlier levels of the educational system. Women are shunted by their socialization and education toward fields of graduate study that are generally more crowded, less productive of completed degrees, and less well funded, and that frequently offer poorer professional employment prospects.
Drawing more complete conclusions
So back in 1973, women applying to UC Berkeley Graduate School tended to apply to departments that had lower acceptance rates such as in the liberal arts. Men, on the other hand, tended to apply to departments that had higher acceptance rates such as engineering and sciences that also required more mathematics.
The authors of the paper identified that bias existed in the socialization and education of the time, and as a parent of a young girl who likes mathematics, my question 43 years later is: How much have those factors truly changed?
Thanks for reading! And thanks to Bora Beran, Anya A’Hearn, and especially the Tableau dev team for the new mark-sizing and table-calculation features! Last but not least, thanks to the Marimekko company for the design inspiration.
If you’d like to build a Marimekko/mosaic plot yourself, all of the screenshots above are views in the workbook and the raw data is available as well (UCBAdmissions, UCBAdmissions by Department).
When he’s not writing Tableau tutorials, Jonathan Drummey offers Tableau consulting and training at DataBlick. He is also a Tableau Zen Master and authors the @helpmedatablick Tableau tip of the day.