Qualitative_data_analysis

148 QUALITATIVE DATA ANALYSIS
The most straightforward example of splicing categories is simply the reverse of
splitting them. Suppose we had begun with a fine-grained analysis involving the
categories ‘disability’ ‘discomfort’ and ‘disfigurement’. Then we could splice these
categories together by integrating them under the over-arching category ‘suffering’.
Like splitting categories, splicing is likely to be an increasingly focused activity. We
do not want to include every strand in our analysis. We want to concentrate our
efforts on the central categories emerging from our preliminary analysis. How do we
decide what is central? As always, we have to pay heed to both the conceptual and
empirical relevance of the categories we have employed so far. Conceptual relevance
can be established in terms of our main interests and objectives as these emerge from
our preliminary analysis of the data. By this stage our ideas may be taking shape and
we may be able to identify the main directions in which we want the analysis to go.
Ideas which seemed interesting at first may no longer seem so; while other issues,
apparently marginal at first, may now assume centre stage. The data provides the
anvil upon which we can shape and sharpen our ideas. Some categories may apply to
the data much more effectively than others. This is likely to be evident in the
amount of data which is encompassed by our categories. Those which become
central are likely to encompass most of the data, while those which become
marginal may be weakly represented in the data.
Likely—but not necessarily. We have to avoid a mechanical approach and allow
empirical relevance pride of place over empirical instantiation. We must judge
whether the extent to which a category is represented in the data indicates its
relevance to our understanding of the data. A small point may mean a great deal. For
example, imagine we have a scene of two people meeting, and one person is holding
something and pointing with outstretched arm towards the other. What the person
is holding may matter a great deal in our interpretation of the scene. Is it coke—or
cocaine? Some points, apparently small details in themselves, may be pivotal to our
comprehension of the rest of our data.
Nevertheless, it would be a strange analysis which failed to encompass the bulk of
the data upon which it is based. The small point can only be pivotal if we have also
grasped the rest of the scene. Then and only then can we understand its
implications. So although not all our central categories need to be richly represented
in the data, some must. Overall, we can expect the categories which are central to
encompass the bulk of the data.
Once we have selected the main strands of our analysis, we can begin to
interweave them. Here we shift from making comparisons within categories to
making comparisons between them. How can such comparisons be made?
At a conceptual level, when we first create our categories we may already have
identified, implicitly or explicitly, some possible relationships between them. Recall
our discussion of humour, and the logical relationships which we identified between