Qualitative_data_analysis

140 QUALITATIVE DATA ANALYSIS
voluminous or complex, it will almost certainly be necessary or desirable to develop
further distinctions within the databits themselves. This will be especially true if we
have chosen to undertake a very broad brush analysis on our first sweep through the
data. But even if we have adopted a fine-grained approach, there may be particular
parts of the text which we have deliberately categorized in a general way, leaving
further refinement for a later stage in our analysis.
Not all categories will require or merit subcategorization. Subcategorizing will
depend in part, too, on how far at this point we can identify some central concerns
around which the analysis will revolve. By this stage, it may be possible to identify
some areas where the further analysis is likely to prove most interesting and
rewarding. We may be a bit clearer about the directions in which to go, even though
there may still be some blind bends and cul-de-sacs on the route ahead. It might
still be a matter of following up hunches and hypotheses, but these may by now be
more informed, while some entertained at the outset may by now be discarded.
Subcategorizing may therefore focus on those themes and issues which are emerging
as the most significant for the analysis.
In relation to Vincent’s letters, suppose we have become especially interested in
the categories ‘task’, ‘temperament’ and ‘suffering’. Following our initial
categorization, we have decided that the bulk of our data falls within these
categories, whereas other categories have proved much more marginal to the
analysis. It has become clear that Woody Allen’s humour in these letters relies
mainly on the use of particular forms of incongruity—transpositions of task and
temperament—and cathartic humour related to our fears of the dentist’s chair.
Let us stay with the example of the category ‘suffering’ (Illustration 10.2).
Suppose we want to develop subcategories which permit a more refined analysis of
the cathartic elements this category conveys. Let us look therefore at the databits we
have assigned this category in more detail. To keep things simple, let us cheat a little
and imagine that these are all the databits assigned this category. This is necessary,
for the whole point of categorizing is to ground our conceptualization in an analysis
of all the relevant data.
Where do subcategories come from? As with categories, they express our ideas
about the data. We can therefore look for inspiration to the data, and to the meanings
and significance of the category we are using. The data is now organized in the form
of relevant databits, while our ideas are reflected mainly in our category definition.
But subcategories don’t spring forth fully fledged from the databits, without our
first having to recognize them and accord them significance. And they don’t simply
express ideas about categories, without reflecting also how these can apply to the
data. As with categories, creating subcategories is an interactive process.
The first thing we might notice about these databits is that some refer to
Vincent’s own experiences, while others refer to the experience of his patients. The