RESEARCH METHOD COHEN ok

FIVE WAYS OF ORGANIZING AND PRESENTING DATA ANALYSIS 467
Five ways of organizing and presenting
data analysis
We present five ways of organizing and presenting
analysis as follows: the first two methods are by
people,andthenexttwomethodsarebyissue,and
the final method is by instrument.
One can observe in the example of teaching English
in Macau that the data have been organized
and presented by respondents, in response to particular
issues. Indeed, where the respondents said
the same, they have been organized by groups of respondents
in relation to a given issue. The groups of
respondents were also organized by their membership
of different strata in a stratified sample: teachers
of younger primary children, older primary children,
younger secondary children and older secondary
children. This is only one way of organizing
aqualitativedataanalysis–bygroups.Theadvantage
of this method is that it automatically groups
the data and enables themes, patterns and similar
to be seen at a glance. While this is a useful method
for summarizing similar responses, the collective
responses of an individual participant are dispersed
across many categories and groups of people, and
the integrity and coherence of the individual respondent
risks being lost to a collective summary.
Further, this method is often used in relation to a
single-instrument approach, otherwise it becomes
unwieldy (for example, trying to put together the
data derived from qualitative questionnaires, interviews
and observations could be very cumbersome
in this approach). So, researchers may find it helpful
to use this approach instrument by instrument.
A second way of organizing the data analysis is
by individuals. Herethetotalresponsesofasingle
participant are presented, and then the analysis
moves on to the next individual. This preserves
the coherence and integrity of the individual’s
response and enables a whole picture of that person
to be presented, which may be important for the
researcher. However, this integrity exacts its price,
in that, unless the researcher is interested only in
individual responses, it often requires him or her
then to put together the issues arising across the
individuals (a second level of analysis) in order
to look for themes, shared responses, patterns of
response, agreement and disagreement, to compare
individuals and issues that each of them has raised,
i.e. to summarize the data.
While approaches that are concerned with
people strive to be faithful to those involved
in terms of the completeness of the picture of
them qua people, unless case study approaches are
deemed to be driving the research, they are usually
accompanied by a second round of analysis, which
is of the issues that arise from the people, and it is
to the matter of issues that we turn now.
A third way of organizing data is to present
all the data that are relevant to a particular
issue. Thisisthemethodthatwasusedinthe
example of Chinese students learning English.
While it is economical in making comparisons
across respondents (the issue of data reduction
through careful data display, mentioned earlier),
again the wholeness, coherence and integrity of
each individual respondent risks being lost.
The derivation of the issue for which data are
gathered needs to be clarified. For example, it could
be that the issue has been decided pre-ordinately,
in advance of the data collection. Then all the
relevant data for that issue are simply collected
together into that single basket – the issue in
question. While this is an economical approach
to handling, summarizing and presenting data, it
raises three main concerns:
The integrity and wholeness of each individual
can be lost, such that comparisons across the
whole picture from each individual is almost
impossible.
The data can become decontextualized. This
may occur in two ways: first, in terms of their
place in the emerging sequence and content
of the interview or the questionnaire (e.g.
some data may require an understanding of
what preceded a particular comment or set
of comments), and second, in terms of the
overall picture of the relatedness of the issues,
as this approach can fragment the data into
relatively discrete chunks, thereby losing their
interconnectedness.
Having had its framework and areas of interest
already decided pre-ordinately, the analysis
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