Qualitative_data_analysis

28 QUALITATIVE DATA ANALYSIS
into different categories by ‘years’ may be very hard to interpret, Therefore we may
prefer to reclassify ‘years’ data in terms of age groups e.g. ‘under twenty-fives’ etc.
which are more meaningful, even though we cannot then measure just how old
those in each age group are.
Problems of interpretation are pervasive in any science, whether we are thinking
of ‘strange attractors’ in physics, ‘black holes’ in astronomy or the ‘nuclear family’ in
social science. Numbers are never enough: they have to refer to concepts established
through qualitative analysis. While quantities are powerful precisely because of the
complex mathematical operations they permit, they mean nothing in themselves
unless they are based on meaningful conceptualizations. In other words, social
science (and science for that matter) without qualitative data would not connect up
with the world in which we live.
If it is folly to disregard the problems of meaning in science, it is also folly to
discount the contribution of numbers in analysing qualitative data. When
A.E.Maxwell, the Senior Lecturer in Statistics at the Institute of Psychiatry in the
University of London, wrote about ‘Analysing Qualitative Data’ three decades ago,
he explained to his readers that his book could have been called ‘Chi-Square tests’
(Maxwell 1961). At that time, it was taken for granted that qualitative analysis
meant the statistical analysis of variables which were not amenable to more
quantitative measurement. Now, we take for granted precisely the opposite: that no
book on qualitative data analysis will be concerned with the statistical analysis. This
shift in paradigm has had some virtue, for it has placed more emphasis on the
meaning and interpretation of data through the processes of description and
classification. However, this emphasis also exaggerates distinctions between
alternative methods which ought more properly to be viewed as partners than as
competitors.
It may indeed be difficult if not irrelevant to count examples, if our data is
entirely descriptive and we are analysing singularities rather than looking for
patterns within our data. However, as I suggested earlier, even ‘singularities’ are
likely to be embedded in a language full of more or less implicit comparisons and
classifications. My daughter cannot tell me what happened at school today without
them. Classification is not just a product of structured interview schedules; it is the
stuff of everyday thinking. One of the main aims of analysis may be to recognize
and make explicit the classifications used by subjects. Another may be the
development of the analyst’s own classification of the data. But once data has been
classified, it can be counted.
Enumeration is implicit in the idea of measurement as recognition of a limit or
boundary. Once we recognize the boundaries to some phenomenon, we can
recognize and therefore enumerate examples of that phenomenon. Once we know