Qualitative_data_analysis

Table 13.7 Recoding data to express more meaningful values
Table 13.8 Analysing subcategories as separate variables
OF MAPS AND MATRICES 209
data, rather than as a result of how we have observed and analysed the data for our
own purposes.
As well as recoding values, another path to data reduction is through reducing the
number of variables. This can also improve the intelligibility as well as the economy
of our analysis. For example, we can see from Table 13.6 that most of the values
assigned for ‘suffering’ are exclusive i.e. for most cases, only one value has been
assigned to that case. The table would be more intelligible and meaningful if we could
name these values. However, we can only do so if the values are exclusive. Suppose
we have subcategorized ‘suffering’ into three subcategories, ‘discomfort’, ‘disability’,
and ‘disfigurement’. Because these subcategories are not exclusive (all three occur in
the first letter) we cannot analyse them as separate values of the variable ‘suffering’.
We would have to analyse them as separate variables, as in Table 13.8.
This is hardly the most economical or intelligible way to present the data. As
there is only one case where more than one subcategory has been assigned, we could
accommodate this exception by introducing an additional subcategory, such as
‘multiple’. Then we could treat each of the subcategories as values of the
overarching variable ‘suffering’ (Table 13.9).
This is not ideal, for we are assigning values in terms of more than one dimension,
something all the books warn us not to do! We are distinguishing between those
cases where only one aspect of ‘suffering’ is apparent, and those where more than one