RESEARCH METHOD COHEN ok

480 CONTENT ANALYSIS AND GROUNDED THEORY
another; they can be logically related; and they
can be empirically related (found to accompany
each other) (Krippendorp 2004: 296).
One has to be aware that the construction
of codes and categories might steer the research
and its findings, i.e. that the researcher may
enter too far into the research process. For
example, a researcher may have been examining
the extra-curricular activities of a school and
discovered that the benefits of these are to be
found in non-cognitive and non-academic spheres
rather than in academic spheres, but this may
be fallacious. It could be that it was the codes
and categories themselves rather than the data
in the minds of the respondents that caused
this separation of cognitive/academic spheres and
issues from the non-cognitive/non-academic, and
that if the researcher had specifically asked about or
established codes and categories which established
the connection between the academic and nonacademic,
then the researcher would have found
more than he or she did. This is the danger of
using codes and categories to predefine the data
analysis.
Step 8: Conduct the coding and
categorizing of the data
Once the codes and categories have been decided,
the analysis can be undertaken. This concerns
the actual ascription of codes and categories to
the text. Coding has been defined by Kerlinger
(1970) as the translation of question responses
and respondent information to specific categories
for the purpose of analysis. As we have seen, many
questions are precoded, that is, each response can
be immediately and directly converted into a score
in an objective way. Rating scales and checklists
are examples of precoded questions. Coding is the
ascription of a category label to a piece of data;
which is either decided in advance or in response
to the data that have been collected.
Mayring (2004: 268–9) suggests that summarizing
content analysis reduces the material to
manageable proportions while maintaining fidelity
to essential contents, and that inductive category
formation proceeds through summarizing content
analysis by inductively generating categories from
the text material. This is in contrast to explicit content
analysis, theoppositeofsummarizingcontent
analysis, which seeks to add in further information
in the search for intelligible text analysis and
category location. The former reduces contextual
detail, the latter retains it. Structuring content
analysis filters out parts of the text in order to construct
a cross-section of the material using specified
pre-ordinate criteria.
It is important to decide whether to code simply
for the existence or the incidence of the concept.
This is important, as it would mean that, in the
case of the former – existence – the frequency of
a concept would be lost, and frequency may give
an indication of the significance of a concept
in the text. Further, the coding will need to
decide whether it should code only the exact
words or those with a similar meaning. The former
will probably result in significant data loss, as
words are not often repeated in comparison to
the concepts that they signify; the latter may
risk losing the nuanced sensitivity of particular
words and phrases. Indeed some speechmakers
may deliberately use ambiguous words or those
with more than one meaning.
In coding a piece of transcription the researcher
goes through the data systematically, typically line
by line, and writes a descriptive code by the side
of each piece of datum, for example:
Text
The students will undertake
problem-solving in science
Iprefertoteachmixedabilityclasses
Code
PROB
MIXABIL
One can see that the codes here are abbreviations,
enabling the researcher to understand immediately
the issue that they denote because they resemble
that issue (rather than, for example, ascribing
a number as a code for each piece of datum,
where the number provides no clue as to what
the datum or category concerns). Where they are
not abbreviations, Miles and Huberman (1994)
suggest that the coding label should bear sufficient
resemblance to the original data so that the
researcher can know, by looking at the code,
what the original piece of datum concerned.