Qualitative_data_analysis

210 QUALITATIVE DATA ANALYSIS
Table 13.9 Recategorizing variables as values of ‘suffering’
Table 13.10 Frequencies for the variable ‘suffering’
aspect is in evidence. And where only one aspect is evident, we are also
distinguishing the type of ‘suffering’ which is inflicted on the patient. If at all
possible, we should avoid such composites and analyse variables in terms of a singe
dimension. Logically, we could do so in this case by adding values for all possible
combinations, such as ‘discomfort and disfigurement’, ‘disfigurement and disability’
and so on. But the conceptual complexity this produces is not commensurate with
the data, as only very few cases have been assigned to more than one subcategory. In
short, it is not worth the conceptual effort. It is better to be forbearing, to accept the
compromise of a composite variable, and use a dustbin category such as ‘multiple’
for the residual values which don’t fit the main dimension.
The virtue of reducing values and variables is that we can increase the focus of
our analysis. It is a bit like a drawing. By eliminating detail, the artist can render more
effectively and dramatically the main features of his subject. The emerging image
may also clarify the relationship between different elements in the picture. We can
use our matrix to explore these relationships by visual inspection, but a further
process of abstraction may also prove invaluable. For any variable, we can analyse
the frequencies with which values occur, as in Table 13.10.
We can also cross-tabulate variables to identify possible relationships between
them (Table 13.11).
We can use frequencies and cross-tabulations of variables to identify patterns
holding in the data. For example, we might be tempted to conclude from