Qualitative_data_analysis

208 QUALITATIVE DATA ANALYSIS
data. Only prejudice would prevent us from using this information if it can help us
to identify patterns within the data.
On the other hand, we are faced with a matrix whose meaning has been reduced
to whether or not a category has been assigned to the data. The interpretation of
this information depends entirely upon the decisions we have made in creating
categories and assigning them to the data. We may be wary, therefore, of drawing
conclusions on the basis of such limited evidence. Any inferences we can draw about
regularities and variation in the data must reflect the confidence we can place in our
initial conceptualizations. The matrix can be useful, but we may need to return to
the original data as often as possible, for confirmation of patterns apparent within
the data or to reexamine and modify our earlier judgements. With its facilities for
searching and retrieving data, the computer should make it possible to do so with a
minimum of fuss.
If our variables describe properties of cases, we may be able to ‘recode’ these values
to make a more meaningful classification. Suppose, for example, that it made sense
to think of the degree to which these letters rely upon stereotypes of artistic
temperament. Then we might ‘recode’ our data to discriminate between those
where Woody Allen relies heavily on artistic stereotypes and those where he doesn’t
use them much if at all. We might distinguish two or more values of the variable
‘temperament’, such as ‘high’ and ‘low’. To recode the data in terms of these values,
we would need to identify a cut-off point above which the existing values would be
recoded as ‘high’ and below which they would be recoded as ‘low’. Of course we
also have to decide whether to record values which are at the cut-off point itself as
‘high’ or ‘low’. Deciding where to make a cut-off point is a conceptual and
empirical problem. Empirically, we may be influenced largely by the pattern of
existing values. Is there a natural break in the data? Does some subdivision of the
range of values (e.g. midway) give a useful distinction between ‘high’ and ‘low’
values? Conceptually, we have to be sure that it is meaningful to describe some
values as ‘high’ and others as ‘low’. These are of course relative terms, and we have
to consider the context in which they are made. If stereotypes of artistic
temperament are used five times in the space of a short letter, can this reasonably
described as ‘heavy’ usage?
By reducing the number of values, we not only summarize the data but also
render it more intelligible. We translate numbers into meaningful values. Our
measurement may be less exact, but our classification is more useful. The matrix in
Table 13.7 shows one possible recoding of the values in Table 13.6.
We can see at a glance the patterns of usage for some of the main categories in
our analysis. On the other hand, these patterns are a product of a series of decisions
we have made in interpreting the data through categorizing and recoding it. We
must therefore avoid any presumption that in some sense they really exist in the