Qualitative_data_analysis

46 QUALITATIVE DATA ANALYSIS
be anything else. Notice, though, that we can treat gender as a nominal variable
only within a given conceptual context.
We may even construct an ordinal variable, distinguishing for example those for
whom a photo is ‘essential’, those for whom it is ‘preferred but not essential’ and
those who do not want one at all. In other words, we can rank the individuals
according to the degree of interest they profess in receiving a photograph; Alistair
expresses more interest than the others in our example. This classification gives us
information about what falls within categories, the boundaries between them, and
how the categories are ordered in relation to each other.
Classification is a conceptual process. When we classify, we do two things. We
don’t just break the data up into bits, we also assign these bits to categories or classes
which bring these bits together again, if in a novel way. Thus all the bits that
‘belong’ to a particular category are brought together; and in the process, we begin
to discriminate more clearly between the criteria for allocating data to one category
or another. Then some cate gories may be subdivided, and others subsumed under
more abstract categories. The boundaries between these categories may be defined
more precisely. Logic may require the addition of new categories, not present in the
data, to produce a comprehensive classification. Thus the process of classifying the
data is already creating a conceptual framework through which the bits of data can
be brought together again in an analytically useful way.
Self-satisfaction apart, there is no point in re-inventing the wheel. If I could bring
an existing classification scheme to bear on this data, for example one based on a
culturally and psychologically rooted theory of beauty, then I would do so.
Conjuring up concepts is challenging work, and there is little point in adding to the
burden by refusing to sharpen existing tools. Naturally, such tools must be
appropriate, or adapted, to the task in hand.
Note that classification cannot be neutral; it is always classification for a purpose.
In classifying this data, I am guided by the practical purpose of finding a prospective
partner. I want to make comparisons which will allow me to select the most
promising amongst these advertisers. As a social scientist, I will be guided by my
research objectives. Since I can only achieve these objectives through analysing the
data, this is (or should be) an interactive process, in which my research objectives are
in turn guided by conceptual clarification I achieve through classifying the data.
Graphic forms of representation can provide an appropriate set of tools for
constructing classification schema, such as those depicting logical relations of
hierarchy and subordination between concepts. Returning to our personal ads, we
can show in this way some of the concepts used in analysing how advertisers present
themselves (Figure 3.5).
The connections between the concepts are ‘formal’ in the sense that they refer to
logical relations of similarity and difference, or inclusion and exclusion, rather than