Qualitative_data_analysis

CLASSIFICATION
WHAT IS QUALITATIVE ANALYSIS? 41
Interpretation and explanation are the responsibility of the analyst, and it is his or
her task to develop a meaningful and adequate account; the data merely provide a
basis for the analysis, they do not dictate it (Burgess 1982). This requires the
development of a conceptual framework through which the actions or events we are
researching can be rendered intelligible. To interpret is to make action meaningful
to others, not just or even necessarily within the terms used by the actors
themselves. To explain is to account for action, not just or necessarily through
reference to the actors’ intentions. It requires the development of conceptual tools
through which to apprehend the significance of social action and how actions
interrelate.
We can grasp the nature of this task more readily if we imagine it is like the
completion of a jigsaw puzzle. (A more apt analogy might be a three-dimensional
puzzle, such as one of those wooden blocks which come apart with deceptive ease
and fit together with frustrating difficulty, since we can represent time and process
through the third dimension.) The only point of taking the puzzle apart, of course,
is to find a way of putting it together again. The finished puzzle represents the
results of our research, and through it we can identify different facets of social action
and their mutual connection. Qualitative analysis involves more than fitting the bits
together, however. Our data start as a seamless sequence, from which we ourselves
must first of all cut out all the bits of the puzzle. We must cut them out in ways
which correspond to the separate facets of the social reality we are investigating, but
which also allow us to put them together again to produce an overall picture.
How do we do jigsaws? There are some pieces of puzzle which are so unique that
we can see straight away their place in the picture. These exceptions apart,
classification is the key to the process. Before I can fit a piece into the puzzle, I have
to assess its characteristics and assign it to some category or another. This bit is a
corner, that’s an edge, this blue bit is sky, that brown bit is earth, and so on. The
categories we use are organizing tools which allow us to sort out the heap of bits
according to relevant characteristics. Gradually, all the blue bits together may make
the sky, the brown bits the earth, the green bits a forest, and so on until we have
built up a complete picture. The categories through which I initially organize the
bits—flat-edged, blue, brown and green—lead on towards a new classification—
sky, earth, forest—in terms of which I can finally describe the picture.
Without classifying the data, we have no way of knowing what it is that we are
analysing. Nor can we make meaningful comparisons between different bits of data.
It would be wrong to say that before we can analyse data, we must classify it, for
classifying the data is an integral part of the analysis: it lays the conceptual
foundations upon which interpretation and explanation are based. This process is
not unfamiliar, for classification is part and parcel of the processes of practical