Qualitative_data_analysis

WHAT IS QUALITATIVE ANALYSIS? 45
view, we are more likely to be interested in the overall picture which emerges of the
dating and mating game. As we shall see, classifying the data therefore lays the basis
for making new connections between different bits of data.
So far we have used inclusive categories: we have created categories to include all
those who are sporting, fun-loving or whatever. For some data we may want to
develop a higher level of classification. Suppose we are particularly interested in how
our advertisers describe their personal appearance. We might begin by using
categories based on these descriptions, such as ‘tall’ or ‘glamorous’. Although these are
both aspects of appearance, they relate to different dimensions: physical and
aesthetic. We can group all the other adjectives which ‘belong’ to these dimensions:
‘short’ is a physical description, ‘gorgeous’ an aesthetic one; and so on. Within each
,dimension, we may begin to sort the categories into groups: for example, ‘gorgeous’
and ‘glamorous’ may be taken as indicators of ‘good looks’, while ‘not bad looking’
may suggest something rather less becoming, perhaps ‘plain’. We may also define
the boundaries between categories more precisely, identifying clearer guidelines for
allocating data to one category or another. Additional data may oblige us to make
further refinements to our categories: for example, ‘fairly attractive’, may not fit any
existing category and require a new one. Logically, I might also identify a category
‘ugly’, though regardless of their physical appearance few advertisers are likely to
present such a personal description! While we would certainly be rash to take an
advertiser’s description at face value, by classifying the data in this way we can begin
to distinguish effectively amongst their subjective aesthetic assessments.
Eventually, through a more rigorous process of conceptualization, we may be able
to classify some of the data at the nominal or ordinal levels. Such variables allow us
to classify data in a more coherent and systematic way, since classification tells us not
only what falls within categories but also something about the boundaries between
them.
Starting with two inclusive categories, the clarification and definition of related
concepts can result in the identification of nominal variables with exclusive and
exhaustive values. As Figure 3.4 suggests, this process is one of distinguishing and
grouping categories. To define the limits of categories more precisely, we must first
conceptualize the relationship between them more clearly. Distinctions between
categories can only be drawn by relating the categories in terms of some underlying
concept. Moving through these different levels of measurement requires increasing
conceptual rigour.
Some data we can immediately classify at a nominal level: gender is an obvious
example. We can treat gender as a nominal variable with the mutually exclusive and
exhaustive values; unless you are hermaphrodite, you cannot be both ‘male’ and
‘female’, at least in a biological if not in a social or psychological sense. Nor can you