Qualitative_data_analysis

Chapter 13
Of maps and matrices
A novelist can take ten pages to describe a scene which a film can convey in a single
image. Text is a useful vehicle for presenting information, but often pictures can
perform the same task more succinctly. Moreover, pictures may correspond more
closely to how we actually think (Buzan 1989). Where we are dealing with complex
and voluminous data, diagrams can help us to disentangle the threads of our
analysis and present results in a coherent and intelligible form. We may not want to
accept the claim that ‘you know what you display’ (Miles and Huberman 1984:79);
but we can readily recognize the virtues of displaying what we do know in the most
effective manner. Text can be a tedious and tiresome way of expressing information
which could be encapsulated in a few lines and boxes. This is especially so when we
are trying to convey sequentially, through text, information which is more easily
grasped simultaneously through diagrams.
Diagrammatic displays are not just a way of decorating our conclusions; they also
provide a way of reaching them. By contrast with the flat and linear trajectory of
text, diagrams provide us with a multi-dimensional space in which to think about
our data. Because this space is multi-dimensional, information can be summarized
within it which would otherwise be dispersed across a long sequence of statements.
In Figure 13.1, for example, we can only see one bit of the textual information, and
none of the connections between the dozen different bits of information. But we
can see all the information distributed spatially at a glance, and also see some of the
connections between the different bits of data.
Diagrams are especially useful when we have to think through such complexities
as the relationships between categories (or variables) and the ways in which
processes permeate the data. By trying to construct diagrams, we can force ourselves
to clarify the main points in our analysis and how these interrelate. But diagrams
can also help with more mundane tasks, such as making comparisons between
categories, or identifying gaps in the data. This is because they can allow us—or
perhaps, oblige us—to think more systematically, more logically, and even more
imaginatively about our data.