Qualitative_data_analysis

Chapter 6
Managing data
Piles of papers spilling from the desk across the floor and strewn over every available
surface: such is the archetypal image of the qualitative analyst at work. Of course,
this shrewd paragon of academic virtue knows exactly where everything is and can
always find a particular paper within moments: the supposed chaos is more
apparent than real. This image is comforting, but hardly credible. In the real world,
a chaotic mass of papers spread across the room is a recipe for confusion, error and
frustration; finding that particular paper may take not moments but hours or even
days. In practice, therefore, the analyst depends on storing and filing data in an
organized and systematic way. In any case, the image of the paper-strewn room has
been superseded by that of the desktop computer. The data which once occupied a
room now occupies a small disk no larger than a thick notepad. The scramble
through endless papers is replaced by the search through innumerable files held on
the computer.
Good analysis requires efficient management of one’s data. The opportunities for
error are, in Gibbon’s words, ‘various and infinite.’ Data must be recorded fully and
accurately, and that may be easier said than done, as anyone who has tried
transcribing audio tapes can verify. For example, try to decipher a group interview
with a dozen very animated respondents, all speaking rapidly in broad dialect and
sometimes several speaking at once! Apart from ‘technical’ difficulties, there are
problems in ensuring data is of consistently high quality where observations have
been directed at more subtle aspects of social interaction.
Take the sketch in Illustration 6.1 for example. The dialogue can be recorded
with relative ease, but we may also want to record other details, such as what Thelm
and Pat are wearing (bikini ‘overalls’ over their everyday clothes), what they are
doing (chain-smoking while supervising the baths) and other aspects of their
interaction apart from the dialogue. The more complex the data, the more risk that
some information will be overlooked or not recorded accurately and that as a result
the quality of the data will be uneven. Our first concern, therefore, is to check the
data for accuracy and to look for possible gaps and inconsistencies. Where the
data is of uneven quality, it may be possible to ‘repair the damage’ by further