Qualitative_data_analysis

SPLITTING AND SPLICING 141
databits 1 and 6 both refer to Vincent’s mental and physical suffering—headaches,
suffocating, blackouts—which the author ridicules through exaggeration; hence the
absurd image of Vincent waking up on the seashore after a blackout lasting several
days. The other databits refer not to Vincent’s own suffering, but to the suffering he
inflicts on his patients.
We could distinguish between these, for example by distinguishing between
‘dentist suffering’ and ‘patient suffering’. Again we have to be careful how we define
our terms. We could use ‘patient suffering’ to refer to any suffering experienced by
patients, or only to suffering inflicted by Vincent and other dentists upon them. It
is important to determine as clearly as possible how we intend to use our
subcategories.
Even if it makes sense to subcategorize the data, we have to decide whether it is
worthwhile conceptually to do so. Does the distinction relate to or illuminate our
main conceptual concerns? As it happens, we noted earlier some questions about
victims of humour, and whether they are treated with sympathy or subjected to
ridicule. If we wanted to pursue this line of enquiry, then this might justify the
introduction of our subcategories from an analytic point of view. On the other hand,
we may have already categorized the data according to who are the ‘victims’, for
example using the categories ‘dentists’ and ‘patients’. This subcategorization would
then be unnecessary. Instead of subcategorizing the data, we could simply retrieve
all the databits where either dentists or patients were identified as the ‘victims’ who
suffered.
If the subcategories make sense, and seem valuable analytically, we still have to
decide whether it is practically useful to subcategorize the databits. If we really had
only eight databits, of which only two were deviant in terms of our main interest, then
we might simply take note of the point without going to the trouble of actually
subcategorizing the data. There are too few examples to require a formal division of
the data into separate categories. Recalling and applying our distinction between the
databits can be done in a matter of moments. There are always going to be some
distinctions which, though not irrelevant conceptually, are too marginal in terms of
the databits to justify subcategorization. If, on the other hand, there were far more
databits—as would probably be the case in practice—then it might be useful to
assign the databits to subcategories, where the data can be re-examined in a new
context. In other words, it might be useful to take all the databits about ‘patient
suffering’ and look at them separately.
In categorizing and subcategorizing we not only make distinctions, we also
preserve them. The value of subcategorizing databits may depend on what we can
do with the results. In this respect, we may not only want to compare databits
within a subcategory; we may also want to compare databits between subcategories.
Suppose we suspect that much of Woody Allen’s humour relies on some