Qualitative_data_analysis

CONCLUSION 275
empirical analysis of the data tends to discount the conceptual significance of the
ideas we bring to the analysis, and the wider ideas we have to relate it to.
This view is based in part on a practical orientation to analysis. If we think in
terms of an analytic toolkit, it makes more sense to consider all the available tools
and not leave one half of the toolbox locked. It is only fair to add that this approach
may seem heretical in some quarters, where purity of procedure takes precedence
over a more pragmatic perspective. My own view is that epistemological and
ontological arguments are more useful if they examine knowledge as a practical
accomplishment—how research works in practice—than if they indulge in
prescriptive wrangles about how we really ought to proceed. Bryman (1988:124)
likewise questions ‘the role of programmatic statements in relation to the pursuit of
good social research’. He suggests that research methods are probably much more
autonomous and adaptable than some epistemologists would like to believe.
The computer itself may fall foul of such prescriptive perspectives on what
qualitative data analysis ‘ought’ to be. The advent of the computer has produced
partisan responses, with some regarding it as a panacea for all ills and others
castigating it as a dehumanizing threat to ‘all that is warm and cuddly in human
nature’ (Pfaffenberger 1988:10). These responses feed off each other, as claims that
the computer can do everything encourage scepticism amongst those who think it
can do nothing—or at least, nothing worthwhile. Neither view is appropriate in the
context of qualitative analysis. The more extravagant claims for the computer relate
to its ability to replace human enquiry and analysis with artificial intelligence. The
advent of ‘expert systems’ has given a new lease of life to these ambitions, as has
research on neuron networks. However, expert systems depend upon the existence of
stable knowledge systems governed by identifiable rules—two characteristics notably
lacking in relation to qualitative data analysis (Pfaffenberger 1988:64–77). Research
on neuron networks looks more promising as a means of emulating human pattern
recognition, but it may be some years (or decades) before its potential is realized,
and even then it remains doubtful whether computers based on neuron networks
can emulate the insights and intuitions characteristic of consciousness and
judgement (Penrose 1990:507–514).
Meantime, the current generation of software provides a set of procedures which
can replace or facilitate the mechanical tasks involved in analysing data, but not the
creative and conceptual tasks that this requires. In this respect, the concerns of those
analysts wary of the quantitative and mechanical character of the computer seem
somewhat misplaced. The ability to handle these aspects of analysis more efficiently
and effectively can only enhance qualitative analysis. Traditional methods of
handling data created a yawning gap between ideals and practice, simply because the
procedures involved were so cumbersome, tedious and time-consuming. The advent
of fast, efficient and eminently manageable techniques for handling data facilitates