RESEARCH METHOD COHEN ok

482 CONTENT ANALYSIS AND GROUNDED THEORY
cause redundancy as it may be counter-productive
repetition; constraints on text length may inhibit
reference to the theme; some topics may require
much more effort to raise than others.
(Weber 1990: 73)
The researcher can summarize the inferences
from the text, look for patterns, regularities and
relationships between segments of the text, and
test hypotheses. The summarizing of categories
and data is an explicit aim of statistical techniques,
for these permit trends, frequencies, priorities and
relationships to be calculated. At the stage of
data analysis there are several approaches and
methods that can be used. Krippendorp (2004:
48–53) suggests that these can include:
extrapolations: trends, patterns and differences
standards: evaluations and judgements
indices: e.g. of relationships, frequencies of
occurrence and co-occurrence, number of
favourable and unfavourable items
linguistic re-presentations.
Once frequencies have been calculated, statistical
analysis can proceed, using, for example:
factor analysis: to group the kinds of response
tabulation: of frequencies and percentages
cross-tabulation: presenting a matrix where the
words or codes are the column headings and
the nominal variables (e.g. the newspaper, the
year, the gender) are the row headings
correlation: to identify the strength and
direction of association between words,
between codes and between categories
graphical representation: for example to report
the incidence of particular words, concepts,
categories over time or over texts
regression: to determine the value of one
variable/word/code/category in relationship to
another – a form of association that gives exact
values and the gradient or slope of the goodness
of fit line of relationship – the regression line
multiple regression: to calculate the weighting
of independents on dependent variables
structural equation modelling and LISREL
analysis: to determine the multiple directions
of causality and the weightings of different
associations in a pathway analysis of causal
relations
dendrograms: tree diagrams to show the relationship
and connection between categories
and codes, codes and nodes.
The calculation and presentation of statistics
is discussed in Chapters 24–26. At this stage
the argument here suggests that what starts as
qualitative data – words – can be converted into
numerical data for analysis.
If a less quantitative form of analysis is
required then this does not preclude a qualitative
version of the statistical procedures indicated
here. For example, one can establish linkages and
relationships between concepts and categories,
examining their strength and direction (how
strongly they are associated and whether the
association is positive or negative respectively).
Many computer packages will perform the
qualitative equivalent of statistical procedures.
It is also useful to try to pursue the identification
of core categories (see the later discussion of
grounded theory). A core category is that which
has the greatest explanatory potential and to
which the other categories and subcategories
seem to be repeatedly and closely related (Strauss
1987: 11). Robson (1993: 401) suggests that
drawing conclusions from qualitative data can
be undertaken by counting, patterning (noting
recurrent themes or patterns), clustering (of
people, issues, events etc. which have similar
features), relating variables, building causal
networks, and relating findings to theoretical
frameworks.
While conducting qualitative data analysis
using numerical approaches or paradigms may
be criticized for being positivistic, one should
note that one of the founders of grounded theory
(Glaser 1996) is on record as saying that not only
did grounded theory develop out of a desire to
apply a quantitative paradigm to qualitative data,
but also paradigmal purity was unacceptable in the
real world of qualitative data analysis, in which
fitness for purpose should be the guide. Further,
one can note that Miles and Huberman (1984)
strongly advocate the graphic display of data as