RESEARCH METHOD COHEN ok

468 APPROACHES TO QUALITATIVE DATA ANALYSIS
may be unresponsive to additional relevant
factors that could emerge responsively in the
data. It is akin to lowering a magnet onto
data – the magnet picks up relevant data for
the issue in question but it also leaves behind
data not deemed relevant and these risk being
lost. The researcher, therefore, has to trawl
through the residual data to see if there are
other important issues that have emerged that
have not been caught in the pre-ordinate
selection of categories and issues for attention.
The researcher, therefore, has to be mindful
of the strengths and weaknesses not only of
pre-ordinate categorization (and, by implication,
include responsive categorization), but also the
researcher must decide whether it is or is not
important to consider the whole set of responses
of an individual, i.e. to decide whether the data
analysis is driven by people/respondents or by
issues.
A fourth method of organizing the analysis is
by research question. This is a very useful way
of organizing data, as it draws together all the
relevant data for the exact issue of concern to
the researcher, and preserves the coherence of
the material. It returns the reader to the driving
concerns of the research, thereby ‘closing the
loop’ on the research questions that typically were
raised in the early part of an inquiry. In this
approach all the relevant data from various data
streams (interviews, observations, questionnaires
etc.) are collated to provide a collective answer
to a research question. There is usually a degree
of systematization here, in that, for example, the
numerical data for a particular research question
will be presented, followed by the qualitative data,
or vice versa. This enables patterns, relationships,
comparisons and qualifications across data types to
be explored conveniently and clearly.
A fifth method of organizing the data is by
instrument. Typically this approach is often used
in conjunction with another approach, e.g. by
issue or by people. Here the results of each
instrument are presented, e.g. all the interview
data are presented and organized, and then all the
data from questionnaires are presented, followed
by all the documentary data and field notes, and
so on. While this approach retains fidelity to
the coherence of the instrument and enables the
reader to see clearly which data derive from which
instrument, one has to observe that the instrument
is often only a means to an end, and that further
analysis will be required to analyse the content of
the responses – by issue and by people. Hence if
it is important to know from which instrument
the data are derived then this is a useful method;
however, if that is not important then this could be
adding an unnecessary level of analysis to the data.
Further, connections between data could be lost if
the data are presented instrument by instrument
rather than across instruments.
In analysing qualitative data, a major tension
may arise from using contrasting holistic and
fragmentary/atomistic modes of analysis. The
example of teaching English in Macau is clearly
atomistic, breaking down the analysis into smaller
sections and units. It could be argued that
this violates the wholeness of the respondents’
evidence, and there is some truth to this, though
one has to ask whether this is a problem or not.
Sectionalizing and fragmenting the analysis can
make for easy reading. On the other hand, holistic
approaches to qualitative data presentation will
want to catch the wholeness of individuals and
groups, and this may lead to a more narrative,
almost case study or story style of reporting
with issues emerging as they arise during the
narrative! Neither approach is better than the
other; researchers need to decide how to present
data with respect to their aims and intended
readership.
Systematic approaches to data analysis
Data analysis can be very systematic. Becker and
Geer (1960) indicate how this might proceed:
comparing different groups simultaneously and
over time
matching the responses given in interviews to
observed behaviour
analysing deviant and negative cases
calculating frequencies of occurrences and
responses