Qualitative_data_analysis

Using Hypertext linking, whenever necessary we can re-examine the databit
within its original context. Suppose, for example, we forget what is meant by ‘it’ in
the databit ‘now she’s upset that it won’t fit her mouth’. We can go directly to the
original text and check what ‘it’ refers to—the ‘billowing bridge’. If we have become
thoroughly familiar with our data, we may find that the occasions when we require
to do so are surprisingly rare. We are not likely to forget that ‘billowing bridge’ in a
hurry!
Nevertheless, in abstracting databits in this way we suffer a significant
information loss. What do we gain by way of compensation? We gain the
opportunity to think about our data in a new way. We can now make comparisons
between all the different databits which we have assigned to a particular category. We
can compare the databits assigned to one category with those assigned to another. On
this basis, we can further clarify our categories and contribute to developing the
conceptual framework through which we can apprehend our data. This process is
likely to involve two main tasks, which I have called ‘splitting’ and ‘splicing’
categories. Splitting refers to the task of refining categories by subcategorizing data.
Splicing refers to combining categories to provide a more integrated
conceptualization. Let us consider each in turn.
SPLITTING CATEGORIES
SPLITTING AND SPLICING 139
I described categorizing as a process of drawing distinctions within the data. This
process is twofold. We divide up the data into bits, distinguishing one bit from
another; and we assign a databit to one or more categories, distinguishing it thereby
from databits assigned to other categories. In other words, categorizing involves
subdividing the data as well as assigning categories.
With subcategorizing, we may no longer need to subdivide our data in quite the
same way. Subcategorizing can be done using the existing databits without further
subdivisions within our databits. We can split up our category into a number of
subcategories which we can then assign to the databits which already belong to that
category. The process of splitting up a category into subcategories is not just
conceptual. It involves assigning the various databits to appropriate subcategories,
and is therefore grounded in our analysis of these databits. However, we do not need
to make any further subdivisions within databits as opposed to distinctions between
them.
On the other hand, it is most unlikely that our initial categorization will have
exhausted the distinctions we can draw within the data. If our data is at all