CONTENT ANALYSISgroups respondents into clusters based on word-
use co-occurrences within responses and then maps
these clusters into a two-dimensional grid that uses
colors to represent each cluster group. If such an
automated inductive procedure can produce addi-
tional valid insights that other techniques are like-
ly to overlook, then why not use it too?
Pioneering work in inductive automatic cate-
gorizing, such as Iker’s (1969), usually enlisted
procedures based upon correlation matrixes, such
as factor analysis. These procedures tended not to
be particularly suited to analyzing text both be-
cause of the shape of word-usage frequency distri-
butions as well as the limited number of words that
a correlation matrix could feasibly handle. TextSmart,
based upon a word-distance measure, provides
much more suitable solutions. Further automatic
categorizing procedures may be expected from
artificial intelligence, as well as from categorizing
techniques being developed for Internet search
engines.
Content-analysis research strategies can thus
now easily be multipronged, spanning from com-
pletely automatic inductive procedures to manual
coding. But even manual coding these days is likely
to utilize computer software to help coders mana-
ge information. Consider these changes in costs
and convenience: Unlike mainframe computing of
the 1960s and 1970s, when the cost of an hour of
computer time was about the same as a coder’s
wage for several weeks, the marginal cost of using
a desktop computer is essentially the electricity it
uses. Today’s desktop computer is likely to be
more than fivefold faster at content-analysis cod-
ing than those mainframe computers ever were.
They also can access much larger dictionaries and
other information in their RAM than was ever
feasible on a user partition of a mainframe com-
puter, thus making their coding more accurate
and comprehensive. Moreover, a single CD-ROM
full of text to be analyzed is easily popped into a
desktop computer, whereas in the days of main-
frame computing, a comparable amount of text
would have to be keypunched on over 3,000 boxes
of IBM cards.
Given today’s convenience and low cost of
computer-based procedures, there is no reason to
limit an analysis to one approach, especially if
insights gained from one approach will differ and
often complement those gained from another.
Instead of being limited by technology, the limits
now may lie in the skills, proclivities, and comfort
zones of the researchers. Research teams, rather
than individual researchers, may prove the best
solution, for only in a team made up of people with
complementary strengths is one likely to find the
full range of statistical, conceptual, intuitive, expe-
riential, and perhaps clinical strengths needed to
carry out penetrating, comprehensive content-analy-
sis projects. Moreover, some researchers will pre-
fer to learn from the main trends while others will
learn more from studying outlying cases. Some
will learn from bottom-line numbers while others
will learn more from innovative graphics that high-
light information patterns. Some will focus on
current data while others will contextualize data
historically by comparing them with data in ar-
chives. Data that has been gathered and assembled
at considerable cost, especially data-gathering that
imposed on many respondents, merit as thorough
and comprehensive analyses as these various pro-
cedures collectively offer.Unfortunately, however, an ‘‘either-or’’ assump-
tion about how to do content analysis has contin-
ued to be supported both by books and computer
software. Authors who do an excellent job of
describing one approach to content analysis, such
as Boyatzis (1998), give an impression that an
either-or decision has to be made about which
approach to use. Some software—especially that
ported from mainframe computers or developed
for early desktop computers—still may steer or
even limit researchers who use it to just one ap-
proach. For example, some software packages cre-
ate specialized data formats such as ‘‘classification
trees’’ that then in effect constrain the user to
analyses that can be readily derived from that
format. Software reviews such as Lewis’s (1998)
excellent comparison of ATLAS/ti and NUD-IST
software have been explicit about what assump-
tions a researcher buys into when utilizing each
package.Additional leverage in analyzing qualitative
information has stemmed from computer-based
tools, such as newer versions of HyperResearch and
ATLAS/ti, that integrate the handling of multiple
media (text, illustrations, and video). Especially as
more software comes from countries where there
are expert programming skills and programming