Qualitative_data_analysis

Chapter 5
Finding a focus
A Zen story tells of an American professor interested in Zen who was once visiting
Nan-in, a Japanese master. Nan-in invited the professor to take tea. He filled the
professor’s cup; but instead of stopping when the cup was full, he carried on
pouring. The tea overflowed; but Nan-in continued to pour. When the professor
remonstrated, Nan-in said: ‘Like this cup, you are full of your own opinions and
speculations. How can I show Zen unless you first empty your cup?’ (Zukav quoted
in Praverand 1984:14).
In less dramatic form, the injunction of the Zen master is commonplace in the
literature on qualitative analysis: beware of bias! Do not let assumptions blind you
to the evidence of your data. Avoid preconceived ideas. Before you start to analyse
your data, make sure your cup is empty.
These exhortations seem eminently reasonable. To produce an account, we have
to search, select, and summarize data. We also have latitude in choosing which
analytic procedures to use as well as what problems to address and how to interpret
results. There is no lack of opportunities for bias in selecting and interpreting our
data. All the more important, therefore, that in qualitative analysis we do not
‘impose’ our ideas upon the data.
However, the exhortation to beware of bias should not be interpreted as an
injunction against prior thought. The scientist and the Zen master in fact follow
different routes to different kinds of knowledge. In Zen, religious revelation is the
aim, intuition and insight the path to it. In science, systematic knowledge is the aim,
and observation and inference the way to achieve it. While the world as experienced
by the mystic may perhaps accord with the world as analysed by the modern
scientist (Capra 1983), their purposes and paths differ. The scientist who ‘empties
his cup’ is a scientist no longer. He may be more open to religious experience; but
he is no longer equipped for scientific analysis.
In short, there is a difference between an open mind and empty head. To analyse
data, we need to use accumulated knowledge, not dispense with it. The issue is not
whether to use existing knowledge, but how. Our problem is to find a focus, without
committing ourselves prematurely to a particular perspective and so foreclosing