Qualitative_data_analysis

ASSIGNING CATEGORIES 125
we ought to follow it as consistently as we can right through our analysis of the
data.
This does not mean, though, that we have to categorize every bit of data. Even if
we have previously summarized our data and eliminated some of ‘the dross’, there may
still be parts of the data which turn out to be less relevant than expected as the aims
and direction of our analysis become more focused. There is no point in
categorizing data which is not clearly relevant to the analysis as it develops.
So far we have only considered how to identify bits of data through judgements by
the analyst which take account of irreducible ‘units of meaning’ in the data. This
approach respects the integrity of the data and also ensures that bits of data are
meaningful both internally and with respect to the analysis. Another approach is to
allow the computer to create bits of data for us. For example, we could simply
divide up the text on an entirely arbitrary basis, asking the computer to demarcate
bits of data by a specified number of characters or lines. This would certainly ensure
consistency in the size of databits, but at the expense of intelligibility. However, we
can use this facility more selectively, by focusing on target keywords or phrases in
the text, and asking the computer to extract all the bits of data which contain the
specified target. The size of these extracts may be specified in different ways,
according to the number of characters or lines before or after the target text, or (less
arbitrarily) to include the sentence or paragraph in which it occurs. For example, the
computer could extract for us all the sentences which contain the word ‘Cézanne’.
This method of generating bits of data is quick, but it also has limitations. The
boundaries which demarcate bits of data are arbitrary, even where the computer
extracts the contextual sentences or paragraphs. The analyst no longer defines the
appropriate ‘unit of meaning’, and has to make do with whatever results the
computer produces. This disadvantage can be offset somewhat, if the computer
allows us to retrieve the original context and modify the boundaries of the bits of
data it has extracted as appropriate. The time gain may depend on how selective we
can be in checking contexts and adjusting the boundaries of bits of data
retrospectively.
Another drawback of this approach is that it identifies ‘units of meaning’ entirely
in terms of key words or phrases in the text. These are unlikely to be the only bits of
data relevant to the analysis as a whole, or even to the particular aspect of the
analysis in which we are interested. Thus the computer cannot pick up any
discussion of Cézanne where he is not explicitly mentioned in the text. Generating
bits of data automatically is unlikely to exhaust all possibilities, and may therefore
be better regarded as a way of complementing rather than substituting for judgements
by the analyst.
Let us now turn to some of the problems of assigning categories. This is likely to
prove the most challenging but also the most rewarding aspect of analysing the data.