Qualitative_data_analysis

22 QUALITATIVE DATA ANALYSIS
Figure 2.3 Categorizing using inclusive categories
varies in kind or amount. This type of variable is often called a ‘nominal’ variable
because its values (or categories) are ‘names’ rather than numbers.
With nominal variables, the values we use must be mutually exclusive and
exhaustive. ‘Mutually exclusive’ means no bit of data fits into more than one
category. For example, suppose we classified a box of apples according to colour,
and assumed that the apples are either red or green. ‘Colour’ is then our variable,
with two values ‘red’ and ‘green’. What if we encounter some apples which are red
and green? Our values are no longer mutually exclusive. ‘Exhaustive’ means you can
assign all your data to one category or another; there’s nothing that won’t fit
somewhere into a given set of categories. Suppose we encounter some yellow apples
lurking at the bottom of the box. Our categories no longer exhaust all possible values
for the variable ‘colour’. To make our values exhaustive and mutually exclusive, we
would have to add new categories for the yellow and red/green apples.
Classifying in this way adds to our information about the data. For any bit of
data which we assign one value, we can infer that we cannot assign the same bit of
data to other values of the same variable. Our categories have become exclusive. For
example, suppose our categories refer to ‘primary’ and ‘secondary’ schools. ‘Schools’
becomes our variable and ‘primary’ and ‘secondary’ its mutually exclusive values.
The observations can no longer be assigned to either category (Figure 2.4). If we
encountered another bit of data which fits our variable but not our values, such as a
‘middle’ school, then we would have to modify our classification to keep it exclusive
and exhaustive.
At this level of measurement, we have adopted a more rigorous measure of both
the qualitative and quantitative aspects of our data. At a conceptual level, our
criteria for categorizing (or counting) a school as either primary or secondary have to
be clear: they cannot be fuzzy and overlapping. As these categories are both values of
the variable ‘schools’, we are also clear about what they have in common. In terms
of counting numbers, if we add our observation to one category, we automatically