Conceptual Classifications

This is part of the Data Stories Series.

Researchers do this all the time. Children do, too. Just watch any toddler in some fancy Montessori school — or the one on the carpet in your living room — and you’ll notice. A bright blue cube in the chubby hand. A clumsy attempt at fitting the shape through the square. Often much too narrow a square. The fervent activity revolving around some generic wooden box. I’m betting it’s blue, too.

But what makes research so different from the child’s play? I might venture the child knows the shapes will eventually match. Possibly orphan shapes are not at the top of a toddler’s considerations.

Impractical classification, however, is- for the researcher.

You see, there are several ways in which we researchers approach this sorting business. This is the stuff of legendary arguments. Try it over the next Thanks Giving dinner with the colleagues.

At one end of the spectrum are those open-codes, clustering methods and affinity diagrams. Here, every shape finds its match — be it only because, by definition, classes are constructed specifically for these very pieces of data.

At the other end of the spectrum, the land of closed coding, arborescence and constructed matrices. Carefully examined, planned and crafted, methodical and — we hope — exhaustive, this classification seems so much sturdier, and robust.

But oh how it sometimes gives rise to a curious, curious, phenomenon.