Qualitative_data_analysis

MANAGING DATA 85
‘Show the full reference for the selected question’—or words to that effect—
typically menu items are rather less verbose.
Another area where this facility can save work is indexing cases. Rather than
compiling our own index of cases, the computer can do it for us. If we decide to
amend a case reference, the computer can locate the appropriate reference in the index
and amend it accordingly. We don’t have to do a thing. A case index is useful, of
course, for keeping track of data; but with the computer, we can also use it to locate
cases immediately within the filing system. Once again, the computer does the work,
and we no longer have to rummage through the filing cabinet looking for the right
file.
One way of making data more manageable is to reduce it. This is another
procedure which provokes a certain amount of anxiety amongst analysts. If we can
reduce the amount of data we have to work with, then we can concentrate on what
is important and our analysis should become more efficient. There is little point in
reading through more than once any data which is clearly irrelevant to the analysis.
Why not eliminate it—or at least, summarize it? The reason lies in a natural
reluctance to ‘tamper’ with the data, and concern over what may or may not
become relevant as the analysis unfolds. Today’s irrelevant digression may contain
tomorrow’s illuminating insight. This uncertainty encourages a natural caution
when it comes to dispensing with data.
Once again, the computer can come to our rescue. Using the computer, we can
reconcile our interest in efficiency with our concern over relevance; we can reduce
the data without risk. Data which is clearly irrelevant at the outset of the analysis
can be summarized; a page of tangents can be reduced to one pithy synopsis. The
computer allows us to do this ‘without risk’, because we can instantly locate or
restore the original data if we wish. This is possible because the computer can make
a direct connection between our summary and the original data, assuming that we
always work with a copy of the data and keep the original material stored on a
separate disk.
The virtue of summarizing data is not only in the greater efficiency with which
we can subsequently deal with the data. Summarizing is not just an investment: it
can have an immediate pay-off, for it also obliges us to consider the question of
relevance at the very outset of our analysis. In deciding whether or not data is
‘relevant’ we have also to decide what it is (ir)relevant for. The purposes and
parameters of the analysis are implicit in these decisions, and we can clarify them by
considering carefully the criteria upon which decisions about relevance are based.
From this point of view, there may be analytic benefits from summarizing data
irrespective of how much data we have or how relevant it all appears. It is only if we
ignore the underlying analytic issues that summarizing may seem a tedious and
mechanical chore.