RESEARCH METHOD COHEN ok

470 APPROACHES TO QUALITATIVE DATA ANALYSIS
examine the issue of causality. There is no required
minimum or maximum length, though memos
should be dated not only for ease of reference
but also for a marking of the development of the
researcher as well as of the research.
Memos are an important part of the selfconscious
reflection on the data and have considerable
potential to inform the data collection,
analysis and theorizing processes. They should be
written whenever they strike the researcher as important
– during and after analysis. They can be
written any time; indeed some researchers deliberately
carry a pen and paper with them wherever
they go, so that ideas that occur can be written
down before they are forgotten.
The great tension in data analysis is between
maintaining a sense of the holism of the data – the
text – and the tendency for analysis to atomize
and fragment the data – to separate them into
constituent elements, thereby losing the synergy
of the whole, and often the whole is greater than
the sum of the parts. There are several stages in
analysis, for example:
generating natural units of meaning
classifying, categorizing and ordering these
units of meaning
structuring narratives to describe the contents
interpreting the data.
These are comparatively generalized stages. Miles
and Huberman (1994) suggest twelve tactics for
generating meaning from transcribed data:
counting frequencies of occurrence (of ideas,
themes, pieces of data, words)
noting patterns and themes (Gestalts), which
may stem from repeated themes and causes or
explanations or constructs
seeing plausibility: trying to make good sense
of data, using informed intuition to reach
aconclusion
clustering: setting items into categories, types,
behaviours and classifications
making metaphors: using figurative and
connotative language rather than literal
and denotative language, bringing data to
life, thereby reducing data, making patterns,
decentring the data, and connecting data with
theory
splitting variables to elaborate, differentiate
and ‘unpack’ ideas, i.e. to move away from the
drive towards integration and the blurring of
data
subsuming particulars into the general (akin
to Glaser’s (1978) notion of ‘constant comparison’:
see Chapter 23 in this book) – a move
towards clarifying key concepts
factoring: bringing a large number of variables
under a smaller number of (frequently)
unobserved hypothetical variables
identifying and noting relations between
variables
finding intervening variables: looking for other
variables that appear to be ‘getting in the way’
of accounting for what one would expect to be
strong relationships between variables
building a logical chain of evidence: noting
causality and making inferences
making conceptual/theoretical coherence:
moving from metaphors to constructs, to
theories to explain the phenomena.
This progression, though perhaps positivist in its
tone, is a useful way of moving from the specific to
the general in data analysis. Running through the
suggestions from Miles and Huberman (1994) is
the importance that they attach to coding of data,
partially as a way of reducing what is typically
data overload from qualitative data. Miles and
Huberman (1994) suggest that analysis through
coding can be performed both within-site and
cross-site, enabling causal chains, networks and
matrices to be established, all of these addressing
what they see as the major issue of reducing data
overload through careful data display.
Content analysis involves reading and judgement;
Brenner et al. (1985) set out several steps
in undertaking a content analysis of open-ended
data:
briefing: understanding the problem and its
context in detail
sampling: of people, including the types of
sample sought (see Chapter 4)