Qualitative_data_analysis

Table 13.11 Cross-tabulating ‘occupation’ and ‘suffering’
Table 13.11 that where letters rely heavily on transpositions of ‘occupation’ patients
tend to experience various forms of ‘suffering’; whereas when reliance on
transposing ‘occupation’ is low, patients tend to experience a particular form of
‘suffering’, i.e. ‘discomfort’. We may be tempted, but of course we would be wrong
to draw any such conclusion. First, there is simply not enough data to merit any
such inference. However, this is a peculiarity of my example, rather than a
characteristic of qualitative analysis, where the problem is usually that we have too
much rather than too little data to work with! Second, any evidence of possible
relationships in the data is at best suggestive rather than conclusive. We still have to
return to the data to examine evidence in detail. Nor should we forget that our
cross-tabulation is an artefact of how we have analysed the data.
Each cell of the matrix results from a complex process of decision-making. This
process includes the creation of memos and categories, the demarcation of databits
and their assignment to categories, the splitting and splicing of categories, and the
translation of categories into variables together with any subsequent recoding. For
example, for the category ‘temperament’ we have memos recording our initial ideas
about the use of stereotypes; we have our initial category definition and how that
evolved over time; we have the databits which have been assigned to that category;
we have the reassessment of the relationship between this category and others. If we
were to include all this detail, then our matrix would quickly become more
meaningful in parts but less intelligible as a whole. The computer can help us to
maintain a reasonable balance between individual and overall intelligibility by
allowing quick access to all the relevant decisions and data which culminate in a
particular value being assigned to a particular cell.
Using matrices to make comparisons
OF MAPS AND MATRICES 211
• Analyse background information, case content and categories
• Reduce data through indices and category assignations
• Recode values and recategorize variables
• Analyse variable frequencies and cross-tabulations