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COMPUTER USAGE IN CONTENT ANALYSIS 489
Diction. Some of these are reviewed by Prein et al.
(1995: 190–209). These do not actually perform
the analysis (in contrast to packages for quantitative
data analysis) but facilitate and assist it. As
Kelle (2004: 277) remarks, they do not analyse
text so much as organize and structure text for
subsequent analysis.
These programs have the attraction of coping
with large quantities of text-based material
rapidly and without any risk of human error
in computation and retrieval, and releasing
researchers from some mechanical tasks. With
respect to words, phrases, codes, nodes and
categories they can:
search for and return text, codes, nodes and
categories
filter text
return counts
present the grouped data according to the
selection criterion desired, both within and
across texts
perform the qualitative equivalent of statistical
analyses, such as:
Boolean searches (intersections of text
which have been coded by more than one
code or node, using ‘and’, ‘not’ and ‘or’;
looking for overlaps and co-occurrences)
proximity searches (looking at clustering of
data and related contextual data either side
of a node or code)
restrictions, trees, cross-tabs (including and
excluding documents for searching, looking
for codes subsumed by a particular node,
and looking for nodes which subsume
others)
construct dendrograms (tree structures) of
related nodes and codes
present data in sequences and locate the text
in surrounding material in order to provide the
necessary context
select text on combined criteria (e.g. joint
occurrences, collocations)
enable analyses of similarities, differences and
relationships between texts and passages of text
annotate text and enable memos to be written
about text.
Additionally, dictionaries and concordances of
terms can be employed to facilitate coding,
searching, retrieval and presentation.
Since the rules for coding and categories are
public and rule-governed, computer analysis can
be particularly useful for searching, retrieving and
grouping text, both in terms of specific words and in
terms of words with similar meanings. Single words
and word counts can overlook the importance
of context. Hence computer software packages
have been developed that look at Key-Words-In-
Context. Most software packages have advanced
functions for memoing, i.e. writing commentaries
to accompany text that are not part of the original
text but which may or may not be marked
as incorporated material into the textual analysis.
Additionally many software packages include
an annotation function, which lets the researcher
annotate and append text, and the annotation is
kept in the text but marked as an annotation.
Computers do not do away with ‘the human
touch’, as humans are still needed to decide and
generate the codes and categories, to verify and
interpret the data. Similarly ‘there are strict limits
to algorithmic interpretations of texts’ (Kelle 2004:
277), as texts contain more than that which can
be examined mechanically. Further, Kelle (2004:
283) suggests that there may be problems where
assumptions behind the software may not accord
with those of the researchers or correspond to
the researcher’s purposes, and that the software
does not enable the range and richness of analytic
techniques that are associated with qualitative
research. Kelle (2004) argues that software may
be more closely aligned to the technique of
grounded theory than to other techniques (e.g.
hermeneutics, discourse analysis) (Coffey et al.
1996), that it may drive the analysis rather than
vice versa (Fielding and Lee 1998), and that it has
a preoccupation with coding categories (Seidel
and Kelle 1995). One could also argue that
software does not give the same added value that
one finds in quantitative data analysis, in that the
textual input is a highly laborious process and that
it does not perform the analysis but only supports
the researcher doing the analysis by organizing
data and recording codes and nodes etc.
Chapter 23