Qualitative_data_analysis

WHAT IS QUALITATIVE DATA? 21
home; where we work, not play. For most purposes, we do not bother to make rigid
and complete distinctions, so long as we can make some reasonably rough and ready
decisions about what ‘belongs’ where. When Eve said ‘here’s an apple’ to Adam, she
wanted him to recognize it for what it is: an apple. She named the fruit an ‘apple’ to
signify that it has certain characteristics: according to my dictionary it is a ‘rounded
firm edible juicy fruit of a tree of the genus Malus’ or ‘any of various similar fleshy
many-celled fruits’. ‘Edible and juicy’ was probably enough for Adam. The category
‘apple’ signifies these characteristics, more or less vaguely defined. The categories we
use may be vaguely defined, but we don’t worry unduly so long as they ‘work’ for
us. We want to know that the apple is juicy and edible, not dry and inedible.
Our categories can be ‘fuzzy’ and overlapping. For most purposes, we may think
of schools as a set of purpose-built buildings. But a school can also double as a
community centre, a sports facility, and during elections as a voting centre. For
parents educating their children at home, part of the house may function as ‘school’
for part of the day. And there may be little agreement on what a school does. For
some it may an institution for imparting skills and certifying achievement, for
others it may be little more than a giant child-minding institution. A concept can
convey very different connotations. So the distinctions we draw in describing
something as a school may vary according to context.
Categorizing at this level therefore involves an implicit and loosely defined
classification of observations. Categorizing brings together a number of observations
which we consider similar in some respects, by implied contrast with other
observations. But the boundaries are not tightly defined, and we are typically vague
about the precise respects in which we differentiate our observations. This means
that in assigning something to one category, we do not automatically exclude it from
others. We discount other possibilities, rather than exclude them altogether. For
example, in counting certain observations as ‘schools’, we discount other categories
such as ‘community centres’, but we do not explicitly exclude them as possibilities.
So counting how many schools there are tells us nothing about how many
community centres there may be. In this sense, our categories are inclusive rather
than exclusive. We focus on whether or not to include an observation within the
category (e.g. to count it as a school) rather than whether in doing so we exclude the
observation from other categories. In Figure 2.3, for example, our observations are
related to two different categories, ‘schools’ and ‘community centres’.
At a more sophisticated level of classification, we can differentiate more explicitly
between observations. Typically, we can do this where we can identify some
characteristics which observations have in common, the better to understand what
distinguishes them. We may want to distinguish clearly between ‘apples’ and ‘pears’,
for example, as different varieties of fruit. Here the concept ‘fruit’ becomes a
variable, whose values are ‘apples’ and ‘pears’. A variable is just a concept which