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SYSTEMATIC APPROACHES TO DATA ANALYSIS 469
assembling and providing sufficient data that
keeps separate raw data from analysis.
In qualitative data the analysis here is almost
inevitably interpretive, hence the data analysis
is less a completely accurate representation (as
in the numerical, positivist tradition) but more
of a reflexive, reactive interaction between the
researcher and the decontextualized data that
are already interpretations of a social encounter.
Indeed reflexivity is an important feature of
qualitative data analysis, and we discuss this
separately (Chapter 7). The issue here is that
the researcher brings to the data his or her
own preconceptions, interests, biases, preferences,
biography, background and agenda. As Walford
(2001: 98) writes: ‘all research is researching
yourself’. In practical terms it means that the
researcher may be selective in his or her focus,
or that the research may be influenced by
the subjective features of the researcher. Robson
(1993: 374–5) and Lincoln and Guba (1985:
354–5) suggest that these can include:
data overload (humans may be unable to
handle large amounts of data)
first impressions (early data analysis may affect
later data collection and analysis)
availability of people (e.g. how representative
these are and how to know if missing people
and data might be important)
information availability (easily accessible
information may receive greater attention than
hard-to-obtain data)
positive instances (researchers may overemphasize
confirming data and underemphasize
disconfirming data).
internal consistency (the unusual, unexpected
or novel may be under-treated).
uneven reliability (the researcher may overlook
the fact that some sources are more reliable or
unreliable than others).
missing data (that issues for which there
is incomplete data may be overlooked or
neglected)
revision of hypotheses (researchers may overreact
or under-react to new data)
confidence in judgement (researchers may have
greater confidence in their final judgements
than is tenable)
co-occurrence may be mistaken for association
inconsistency (subsequent analyses of the same
data may yield different results); a notable
example of this is Bennett (1976) and Aitkin
et al.(1981).
The issue here is that great caution and selfawareness
must be exercised by the researcher
in conducting qualitative data analysis, for the
analysis and the findings may say more about the
researcher than about the data. For example, it is
the researcher who sets the codes and categories
for analysis, be they pre-ordinate or responsive
(decided in advance of or in response to the data
analysis respectively). It is the researcher’s agenda
that drives the research and the researcher who
chooses the methodology.
As the researcher analyses data, he or she will
have ideas, insights, comments, reflections to make
on data. These can be noted down in memos
and, indeed, these can become data themselves
in the process of reflexivity (though they should
be kept separate from the primary data themselves).
Glaser (1978) and Robson (1993: 387)
argue that memos are not data in themselves
but help the process of data analysis. This is debatable:
if reflexivity is part of the data analysis
process then memos may become legitimate secondary
data in the process or journey of data
analysis. Many computer packages for qualitative
data analysis (discussed later) have a facility not
only for the researcher to write a memo, but also
to attach it to a particular piece of datum. There
is no single nature or format of a memo; it can
include subjective thoughts about the data, with
ideas, theories, reflections, comments, opinions,
personal responses, suggestions for future and new
lines of research, reminders, observations, evaluations,
critiques, judgements, conclusions, explanations,
considerations, implications, speculations,
predictions, hunches, theories, connections, relationships
between codes and categories, insights
and so on. Memos can be reflections on the
past, present and the future, thereby beginning to
Chapter 22