RESEARCH METHOD COHEN ok

184 NATURALISTIC AND ETHNOGRAPHIC RESEARCH
explaining the data; in short, making sense of
data in terms of participants’ definitions of the
situation, noting patterns, themes, categories and
regularities. Typically in qualitative research, data
analysis commences during the data collection
process. There are several reasons for this, and
these are discussed below.
At a practical level, qualitative research rapidly
amasses huge amounts of data, and early analysis
reduces the problem of data overload by selecting
out significant features for future focus. Miles
and Huberman (1984) suggest that careful data
display is an important element of data reduction
and selection. ‘Progressive focusing’, according
to Parlett and Hamilton (1976), starts with the
researcher taking a wide-angle lens to gather
data, and then, by sifting, sorting, reviewing and
reflecting on them, the salient features of the
situation emerge. These are then used as the
agenda for subsequent focusing. The process is
like funnelling from the wide to the narrow.
At a theoretical level a major feature of
qualitative research is that analysis commences
early on in the data collection process so that
theory generation can be undertaken (LeCompte
and Preissle 1993: 238). LeCompte and Preissle
(1993: 237–53) advise that researchers should set
out the main outlines of the phenomena that are
under investigation. They then should assemble
chunks or groups of data, putting them together
to make a coherent whole (e.g. through writing
summaries of what has been found). Then they
should painstakingly take apart their field notes,
matching, contrasting, aggregating, comparing
and ordering notes made. The intention is to
move from description to explanation and theory
generation.
For clarity, the process of data analysis can be
portrayed in a sequence of seven steps which are
set out here and addressed in subsequent pages.
Step 1: Establish units of analysis of the data,
indicating how these units are similar to and different
from each other
Step 2: Create a ‘domain analysis’
Step 3: Establish relationships and linkages between
the domains
Step 4: Making speculative inferences
Step 5: Summarizing
Step 6: Seeking negative and discrepant cases
Step 7: Theory generation
Step 1: Establish units of analysis of the data,
indicating how these units are similar to and different
from each other. The criterion here is that each
unit of analysis (category – conceptual, actual,
classification element, cluster, issue) should be
as discrete as possible while retaining fidelity to
the integrity of the whole, i.e. that each unit must
be a fair rather than a distorted representation of
the context and other data. The creation of units
of analysis can be done by ascribing codes to the
data (Miles and Huberman 1984). This is akin
to the process of ‘unitizing’ (Lincoln and Guba
1985: 203).
Step 2: Create a ‘domain analysis’. A domain
analysis involves grouping together items and
units into related clusters, themes and patterns,
a domain being a category which contains several
other categories. We address domain analysis in
more detail in Chapter 23.
Step 3: Establish relationships and linkages
between the domains. This process ensures
that the data, their richness and ‘contextgroundedness’
are retained. Linkages can be
found by identifying confirming cases, by seeking
‘underlying associations’ (LeCompte and Preissle
1993: 246) and connections between data subsets.
Step 4: Making speculative inferences. This is
an important stage, for it moves the research from
description to inference. It requires the researcher,
on the basis of the evidence, to posit some
explanations for the situation, some key elements
and possibly even their causes. It is the process
of hypothesis generation or the setting of working
hypotheses that feeds into theory generation.
Step 5: Summarizing. This involves the
researcher in writing a preliminary summary
of the main features, key issues, key concepts,
constructs and ideas encountered so far in the