Qualitative_data_analysis

Figure 2.4 Nominal variable with mutually exclusive and exhaustive values
WHAT IS QUALITATIVE DATA? 23
exclude (or subtract) it from another. We can now consider the proportion of our
observations which fall into any particular class. This advance in counting
numerically is only possible, though, because of our advance in what counts
conceptually as belonging to one class or another. We have defined our classes more
comprehensively (so they are exhaustive) and more precisely (so they are exclusive).
Sometimes we can put values into a rank order. For example, we may distinguish
schools in terms of some idea of educational progression, and rank primary schools
as more elementary than secondary schools. If we can order values in this way, we
can convert our nominal variable into an ‘ordinal’ variable, so-called because it
specifies an order between all its values. A common example of ordinal variables in
social research can be found in the ranking of preferences, or where we ask
respondents to identify the strength of their feelings about various options. Ordinal
variables give us still more numerical information about the data, because we can
now indicate how one bit of data is higher or lower in the pecking order than
another (Figure 2.5).
From a quantitative perspective, we can now rank these values along a continuum.
If primary schools fall below middle schools on this continuum, we can infer that they
also fall below upper secondaries. But for this ranking to be meaningful, it must also
make sense from a qualitative perspective. In the case of schools, the idea of
educational progression provides a conceptual rationale for distinguishing an order
in terms of the degree of progression exhibited by different schools.
To progress to higher levels of measurement, we have to improve or refine our
conceptualization of the data. What is a school? Can we classify schools into
primaries and secondaries? Are primaries more elementary than secondaries? Each of