Thanks to all who offered their help in reply to my last post about visualizing typological data. Most of you agreed that the recent hype about visualization and infographics almost completely neglects qualitative small-n data in favour of quantitative large-n sets. There are various reasons for this state of affairs: for one, much of this hype is driven by the fact that large government statistics are being put in the public domain (or by the availability of equally large private statistics generated through web 2.0). Secondly, small-n data lends itself nicely to narrative writing, rendering visualization a less pressing requirement. Lastly, such qualitative data tends to be far more complex than statistics, and its complexity can not easily be reduced through statistical generalization.

Reducing the complexity of small-n data is not impossible, however - and usually takes the form of typologies. The need to visualize these, and particularly to visualize them in an interactive way, arises from the fact that such typologies often suggest a rigidity which is never there in the data (as I wrote here). The reason for this deception is basically that the underlying cluster analyses - be they statistically aided or intuitive - always generate an x-fold typology if you ask them to so - even if the dissimilarities between types are marginal in comparison to their similarities. The irreducability of original data behind typologies is therefore what I would love to visualize, to give readers of my upcoming book1 a hands-on feeling for the flexibility of the typology of Muslim peace activists which I propose therein.