Qualitative_data_analysis

Notice how this cross-tabulation compares with that in Table 12.2, where we had
no figure for the final cell—you may recall that when analysing the data as a whole,
there can be no retrieval for data which has not been assigned to one or other of our
categories. When analysing variables across cases, though, we can complete the final
cell, which gives the number of cases where neither variable has been assigned to the
data. Now we can complete totals and proportions for all the rows and columns in
our cross-tabulation. We can compare the proportion of cases where ‘temperament’
and ‘suffering’ concur with the proportion where they are assigned separately, or are
not assigned at all. If we have satisfied the conditions for statistical tests—which
may require a minimum number of randomly selected cases—we may even conduct
tests of the significance of any association we observe between the variables. Essentially
this involves matching the observed values in cells with the values we would expect
if there were no association between the variables.
In looking for associations, we may be interested in a number of different
possibilities, and not just evidence of a high positive correlation between two
variables. We may look for precisely the opposite—a high negative correlation, so
that high values for one variable, are associated with low values for another. We may
also be interested in changes in values—whether more or less of one value for a
variable raises or lowers the values for another variable.
X high Y high
X high Y low
X higher Y higher
X higher Y lower
MAKING CONNECTIONS 187
These are only some of the associations we may be able to identify (cf. Miles and
Huberman 1984:225–6).
Our analysis need not be confined to categories regarded as variables whose
values express the number of times the category has been assigned to each case. We
can incorporate variables giving background characteristics of the case—often called
‘facesheet’ variables because such background data may be recorded on the front
page of an interview or set of fieldwork notes. These variables, expressing perhaps the
age and gender of a respondent, the size and location of a site, or the type and
functions of an agency, may then be related to the variables emerging from the
categorical analysis. The latter may also assume a more sophisticated form, if we can
identify connections between categories and integrate them in terms of some
underlying variable.
For example, suppose we have analysed the data in terms of three subcategories of
‘suffering’—‘discomfort’, ‘disfigurement’ and ‘disability’—and we find that
(contrary to the evidence of the first letter) these are rarely if ever assigned to the