Annu. Rev. Sociol. 1998. 24:477-498.
Daniel Dohan and Martín Sánchez-Jankowski
Robert Wood Johnson Foundation Scholars in
Health Policy Research Program, School of Public Health, University of
California, Berkeley, California 94720; e-mail: dohan@uclink.berkeley.edu
Department
of Sociology, University of California, Berkeley, California 94720; e-mail: sanjan@socrates.berkeley.edu
KEY WORDS: qualitative
methods, ethnography, computer software, data analysis
ABSTRACT
Computer-assisted data analysis is
usually associated with the analysis of aggregate data according to
the tenets of logical positivism. But there are more than twenty
computer programs designed to assist researchers analyzing
ethnographic data, and these programs may be used by researchers
with a variety of epistemological orientations. Some computer-assisted
qualitative data analysis (CAQDA) programs automate analysis
procedures that have been used by generations of ethnographers.
Others open up new directions through the use of linked coding
schemes, hypertext, and case-based hypothesis testing. Ethnographers
interested in computer assistance must acquaint themselves with
the variety of capabilities and programs available because no one
program dominates the CAQDA field. In this article, we provide an
overview of the theoretical and practical considerations bearing on
the choice of CAQDA software.
INTRODUCTION
Computer-assisted analysis in sociology
is currently associated with the profession's constructed category
of "quantitative research" rather than its constructed
category of "qualitative research."1
Statistical procedures available in mainstream software packages
such as SAS and SPSS facilitate the analysis of aggregate data, and
most sociologists using these data have adopted an orientation of
logical positivism. Thus, computer-assisted analysis carries
connotations of hard data, computation, and objectivity. On the
other hand, sociologists associated with qualitative research have
generally held that aggregate data analysis using statistical
procedures either misses important sociological causes of social
action or emphasizes explanation (the hallmark of logical
positivism) at the expense of understanding. The general
overstatement of the differences between quantitative and
qualitative research has meant that qualitative researchers have
shown a slowness, if not reluctance, to use computer assistance in
data analysis.
Just as it can with aggregate data,
computer assistance can facilitate systematic computational research
with qualitative data. In addition, CAQDA (computer-assisted
qualitative data analysis) technologies can be useful for researchers
who place themselves outside the positivistic research tradition.
For example, within participant-observation research, there are
three epistemological traditions: positivism, symbolic-interactionism,
and ethnomethodology. While symbolic-interactionism and ethnomethodology
appear antithetical to the use of computer-assisted analysis, a
close look at the capabilities of current CAQDA software suggests
these packages could be useful for research in these traditions and
could become even more useful in the near future.
Software for the analysis of qualitative
data has appeared relatively recently, and although qualitative
sociologists have been slow to adopt this software, at present there
are more than twenty packages available. No one package dominates,
however, so the qualitative analyst interested in computer
assistance must decide which package to use. The amount and kind of
ethnographic data on hand or to be collected, the purpose of the
research, the epistemological framework of the researcher, and the
goodness of fit between the research project and the capabilities of
available software all figure into this decision. In the end, the
analyst may decide that no CAQDA software is called for. A review of
ethnographies in journals and books published in the last five years
suggests that many ethnographers either do not use any CAQDA
software or consider their use of it so unobtrusive that they fail
to mention it at all in their ethnographic reports.2
In this article, we discuss the
capabilities and limitations of CAQDA in general, the factors that
distinguish one CAQDA software package from another, and specific
qualities of a number of software packages. We intend this
discussion to serve as a theoretical and practical introduction to
CAQDA use. In the article's conclusion, we return to the methodological
issues broached above to consider if the gap between quantitative and
qualitative research might be bridged by current and future CAQDA
packages. We do not endorse any particular analytical strategy nor
do we recommend or review any particular software package in this
article.3
QUALITATIVE DATA ANALYSIS AND THE PERSONAL COMPUTER From QDA to CAQDA
Ethnographers have been using computers for decades. Interviews and
fieldnotes have been transcribed into word processors (Kirk
1981), and many ethnographers now carry portable lap-top computers
into the field. The use of computers for the analysis (rather than
the gathering) of ethnographic data is a more recent development.
Many CAQDA software packages facilitate
data analysis from the grounded-theory perspective codified by
Glaser & Strauss (Lonkila
1995).Grounded theorists advocate close contact with raw data,
the emergence of analytical categories from the data through memo
writing, and comparison as the primary analytical tool (Glaser
& Strauss 1967). Elements of grounded theory are common in
CAQDA in part because Glaser & Strauss are explicit about the
principles and procedures involved in this kind of analysis (see
especially Strauss 1987).
Several general approaches to qualitative
data analysis (QDA) incorporate elements of grounded theory or are
consistent with that perspective. For example, while Pfaffenberger
does not explicitly embrace the grounded-theory method of analysis,
his three fundamentals for the analysis of qualitative data (rewriting,
coding, and comparison) are consistent with grounded-theory methods
(Pfaffenberger 1988:26–30). Huberman & Miles propose
a three-part conceptualization of the analysis process: data
reduction, data display, and conclusion drawing/verification. As in
grounded-theory approaches, data reduction, display, and conclusion
drawing are causally and temporally intertwined (Huberman
& Miles 1994:429). Huberman & Miles expand these
abstract procedures into a concrete set of thirteen "tactics" for
undertaking qualitative analysis (Huberman
& Miles 1994:432). These range from noting patterns and
themes, clustering, and counting to making contrasts and
comparisons, shuttling between data and categories, building a
logical chain of evidence, and making conceptual/theoretical
coherence. Tesch distills ten "principles and practices"
in the analysis of many types of qualitative data. While the
principles (data analysis is concurrent with collection, analysis is
not rigid, and the result of analysis is a higher-level synthesis)
outnumber the practices (data are segmented, data are categorized,
the main intellectual tool is comparison), Tesch's approach follows
the general thrust of grounded-theory analysis (Tesch
1990:95–97).
QDA that is consistent with grounded
theory uses a sequential style of analysis that is highly
data-intensive. Advocates of these methods urge the analyst to begin
data analysis while collection is under way, to reduce the data
using codes or categories, to shuttle between data and codes, and to
compare coded and raw data to make tentative and ultimate
conclusions. This analytical strategy returns the analyst to the
database over and over again, and each step of analysis is readily
translated into computer modules and procedures. Because grounded-theory
and similar analytical strategies are consistent with logical positivism,
they present practical challenges to computer programs but few
epistemological challenges.
Researchers relying on context-dependent
methods of analysis such as the extended-case method (Burawoy
1998, Burawoy et al 1991), symbolic-interactionism (Blumer
1969:Ch.1), and ethnomethodology (Garfinkel
1967) may also find software designed around the grounded-theory
principles helpful. But they are less likely to be able to take
advantage of all the built-in features of these packages. The
analytical principles of these context-dependent methods are more
difficult to codify than those of grounded theory. So, while
grounded theorists may find themselves able to take advantage of a
wide variety of computer resources as they move from QDA to CAQDA,
ethnographers working in other traditions may find that computer
assistance limits their analyses unless they limit the extent to
which they make use of computers.
First Steps and Basic
Capabilities of CAQDA
Fortunately for ethnographers working outside of the grounded-theory tradition,
computer assistance is not an all-or-nothing affair. Some features
of contemporary CAQDA may be used and others may be ignored. We
organize our discussion of the practicalities of CAQDA analysis
around seven tasks performed by the user of CAQDA software (Weitzman
& Miles 1995:Ch.3). Analysts, whether positivists or not,
may find some of the tasks required by CAQDA to be theoretically or
practically onerous. But different software packages require
different tasks, so analysts can pick and choose software that
facilitates the tasks they are interested in without requiring those
they find objectionable.
ENTERING DATA
Seemingly mundane decisions made early in the ethnographic project
may have significant practical, methodological, and theoretical
consequences. How to enter data into the computer is a seemingly
mundane decision with enormous consequences. Which data are entered
into the computer, how they are entered, and which remain outside
the computer shape all further analyses of the data. Fischer notes
that data can enter a computer in a myriad of forms, from the
"beginning" methods of text processing on a word processor
to "advanced" methods of digital signal processing of
videotape (Fischer 1994:15–21). For the moment, we confine
ourselves to issues related to the entry of text; we address audio,
visual, and graphic forms of data in later sections of this article.
A primary consideration for researchers entering text data into the
personal computer is the size of the textual unit of analysis. Notes
entered into a dedicated CAQDA package are divided into analysis
"chunks"—which can be single words, lines of text,
paragraphs, hypertext note cards, or larger files. Especially
important is the size of chunks—the indivisible units that are de-contextualized
and re-contextualized during the analysis process (Tesch
1991b). For example, larger chunks of text are more likely to
contain data falling into several analytical categories, and this
may complicate positive correlational analysis. But for analysts interested
in context-dependence, smaller chunks may prove worthless unless the
CAQDA software contains elaborate coding or linking procedures.
Practical issues also arise at the
data-entry stage. Should the qualitative database include expanded
and annotated fieldnotes, interview transcripts, and memos? Memos
can be electronically linked to existing data via hypertext
connections or in situ "pop-up" notes. What are the
advantages of integrating memos into the qualitative database? Are
the advantages of an all-inclusive database worth the costs of
greater storage overhead and slower processing times? Removing memos
from the CAQDA software environment may hamper the goal of
comprehensive analyses of ethnographic data, but it may bolster a
sociological imagination that extends beyond the parameters of a
particular software package.
ORGANIZING DATA
Cases and variables organize quantitative datasets. The organization of
ethnographic data varies depending on the research project at hand.
The number of ethnographers involved in the project, the number of
field sites, the variety of data types, and the theoretical
orientation of the researchers all influence how the ethnographic
dataset is organized. Researchers should at least familiarize
themselves with basic database-management principles (Tesch
1990:199–210; Winer
& Carrière 1991) to be sure that early decisions about the
structure of the qualitative database do not create insurmountable
data-management problems later in the project.
SEARCHING FOR AND RETRIEVING DATA
Computers increase the ethnographer's ability to search for and
retrieve text. For some ethnographers, search and retrieval represents
the end of the computer's usefulness as a qualitative data analyst
assistant, and several CAQDA packages are designed for this kind of
analysis. At the least, searching for and retrieving data involves
the ability to find and display a string of text characters that has
been entered into the database (Tesch
1990:181–94). CAQDA software usually allows ethnographers to
search for root forms of words or synonyms, to use wildcard characters,
and to mount combination searches such as those based on word
proximity or word order. Boolean-defined searches for multiple items
round out the menu for searches. Retrieval of searched-for items is
governed, again, by the size of text chunks and the flexibility of
the package in retrieving consecutive or proximate chunks.
CODING DATA
Coding, also referred to as indexing (Richards
& Richards 1991a, 1994:457)
or content analysis (Berg
1995:Ch. 9), is a central feature of much CAQDA. The use of the
computer need not affect the fundamentals of data coding. Weaver
& Atkinson coded their illustrative fieldnote material manually
before entering the data into their CAQDA package (1994:52–53).
Most discussions of computer-assisted coding reinforce what Weaver
& Atkinson learned through practical experience: The hard work
in coding data is intellectual, not mechanical. Computer assistance
does not relieve the ethnographer of the need to spend many hours
devising, revising, and applying an indexing system that is reliable
and valid (a general approach is Werner
& Schoepfle 1987). Moreover, computer assistance can impose
limitations or restrictions on the coding process that can create
problems for ethnographers (Weaver
& Atkinson 1994:38–42). Coding should be driven by the
theoretical orientations that inspired the original research.
Analysts must be confident that using the computer facilitates their
work. They must remain alert to the possibility that coding data
with a well-designed computer program can become an end unto itself;
highlighting sections of text with combinations of on-screen colors
or sorting and re-sorting half-coded notes can easily create the
comforting appearance of progress.
ANALYZING CODES
Analysis of codes begins as soon as the first data are coded. Codes
are defined in relationship to each other, so their application to a
set of data implies theory. CAQDA software can make this implicit
theory explicit by generating a list or map of codes and their
relationships. Some packages constrain the development of a coding
scheme to encourage the analyst to make positive connections between
codes, such as hierarchical connections between more and less
inclusive ones (Richards
& Richards 1995) or sequential connections between coded events
(Carsaro & Heise 1990). Analyzing codes is thus simultaneous
with the coding process.
Once sufficient data have been coded,
other analytical possibilities develop. In most CAQDA packages,
analysts search for codes as easily as they explore raw data.
Boolean capabilities are useful here, particularly for analysts
interested in computation, because they allow the ethnographer to
count instances of codes or conjunctions of codes. Alternatively,
packages that retrieve text associated with particular codes or
conjunctions of code may be useful for analysts interested in
interpretational analysis.
Aside from data entry, the analysis of
codes is the area of the computer's greatest influence on theory and
methods. Software design may force the analyst to consider the
previously unexamined relationship between concepts in the research
project. The flip side of the coin is that software may limit the
ability of the analyst to develop theory in desired directions. The
ability to mount comprehensive searches for codes and sets of
codes means that ethnographic analysis may benefit from less bias.
But large-scale searches can also bury the analyst in chaotic
results. In short, the computer-assisted analysis of codes has
theoretical and methodological implications surpassed only by those
taken during the first steps of data entry.
LINKING DATA
The most recent development in the analysis of qualitative data
requires computer assistance (Coffey
& Atkinson 1996:181–87). Software available in the last
decade allows analysts to create hypertext links between combinations
of data, codes, memos, and research reports. Graphics, sound, and
video may also be incorporated into "hyperspace"
databases; (Weaver & Atkinson 1994:Ch. 5). Analysis based on
data linking may prove a boon for ethnographers who collect
non-textual data, especially if hypertext moves out of the
researcher's office and becomes a medium for the distribution of
research reports. Even for ethnographers who rely exclusively on
text, the metaphor and activity of creating links in the
ethnographic database have potential for generating innovative
results. For researchers working outside of the positivistic
tradition, linking data may be particularly valuable. Hyperlinks
concretize nonlinear data-analysis techniques and free the
researcher from reliance on computation. Reports that incorporate
graphics, sound, and video can more readily make the case for the
significance of context.
But hypertext technology also imposes
special limitations on analysts. At present, the incorporation of
text into hypertext "spaces" is inevitably fraught with
more burdensome formatting limitations than those imposed by
traditional text databases. Integrating sound or video into an
ethnographic database involves technological expertise beyond the
use of the word processor. In addition, the publication of materials
using sound or video technology may introduce new ethical
considerations, such as the protection of research subjects'
confidentiality.
ANALYZING LINKS
Analyzing links within the database is a more general form of
analyzing codes. As in the analysis of codes, links may be analyzed
only after a certain number have been established in the data. Once
established, the links may be abstracted from the original data and
analyzed as a system or network of their own. Compared to the
analysis of codes, the analysis of links is more flexible and
general. Greater complexity is possible in hypertext links than in
coding schemes, so the representation of linked data may
consequently be more complicated. At the same time, the ability to
grasp at a glance a properly abstracted set of links allows analysts
to bring "right brain" analysis to ethnographic analysis
even when coding and linking have produced a complex data structure
(Agar 1991). Similar to linking technologies, the
computer's ability to analyze links may be especially appreciated by
those working in symbolic-interactionist, ethnomethodological, and
other nonpositivist traditions. The challenges and drawbacks of
linking data, codes, and memos apply equally to their analysis.
Summary
Computer-assisted qualitative data analysis does not differ fundamentally,
for the most part, from the nonmechanical qualitative analysis
traditions from which it has developed. Most computers ease the
labor burden and broaden the scope of common analysis tasks such as
typing up field data and memos, searching for text, coding data, and
sorting and comparing codes. Hypermedia is an unique contribution of
computer technology to the analysis of qualitative data. Linking
text, analysis, and non-text materials (graphics, sound, and video)
in a single analytical space outside of the mind's eye is not
possible manually.
Computer assistance is not
free—theoretically or methodologically. The design of most CAQDA
software after the metaphors and practices of grounded-theory
analysis means that ethnographers who are working outside of that
tradition may have to coax a recalcitrant software package into
aiding their preferred style of analysis. Naturally, the less
assistance the ethnographer requires from the computer, the less
intrusive the grounded-theory perspective is likely to be. The computer
also makes demands on the form of ethnographic data collected. At
present, the computer still favors word-processed text over other
forms of data such as sketches, maps, photographs, video images, or
recorded sound. But as computers increase in power, analysts can
look forward to gaining greater digital control over non-text data.
The experience of flipping through pages of fieldnotes—sketches, diagrams
and coffee stains—will never be replicated on the computer monitor.
But if knotty problems such as protecting the identity of research
subjects can be overcome, computers may soon provide a compelling
auditory and visual alternative to this tactile experience.
CAQDA SOFTWARE
Computers can be programmed to accomplish four different kinds of
analysis: numerical/arithmetic analysis, writing and document processing,
data organization, and symbolic manipulation (Fielding
& Lee 1991:2–3). Ethnographers use computers for all
these kinds of analysis. Our overview of contemporary CAQDA software
is organized around major distinctions in how data are organized and
how symbols are manipulated by different packages. This overview is
not meant as a thorough guide for the prospective purchaser of CAQDA
software. That reader should read reviews of programs (Prein
et al 1995;, Tesch
1990;, Tesch
1991a, especially volume 2; Weitzman
& Miles 1995), consult published discussions of researchers'
experiences with CAQDA software (cited passim below), and try out
different software with his or her own data. In preparing this
overview, we have drawn especially on Computer Programs for Qualitative
Data Analysis (Weitzman
& Miles 1995), which contained the most thorough and
up-to-date reviews available at press time.
Document Processing:
Searching and Retrieving
Word processing is the bread and butter of computer assistance for
the ethnographer. The only computer assistance many ethnographers require
is searching with a word processor. Basic searches retrieve a text
string from a single computer file. More advanced searches count the
occurrences of a string, and stand-alone search engines can search
multiple files and produce extracts of search "hits" in
context. The General Inquirer, the first CAQDA package, produced
lists of word counts from a selected file as a preface to content
analysis (Stone et al 1966). This ability is no longer considered
the province of CAQDA packages, and for many ethnographers, text
searching within a word-processing file is sufficient (Stanley
& Temple 1995). Specialized programs developed for both
CAQDA and commercial uses enhance the search and retrieval process.
Many of these programs are designed for what Tesch called
descriptive-interpretive work rather than theory building (Tesch
1990, 1991a). For searching and retrieving, packages
including GOFER, Metamorph, Orbis, Sonar Professional, The Text
Collector, WordCruncher, ZyINDEX, and FYI3000PLUS expand on the
capacities of word processors in several ways (for FYI3000PLUS, see Weaver
& Atkinson 1994).
First, these packages create and manage
the ethnographic database. Some of these packages manage files
off-line (data remain in separate, unaltered text files); others
manipulate the data directly. Usually, document processors work on
documents that have already been produced in a word-processing
package. Orbis manages files produced in XyWrite or NotaBene;
MetaMorph and WordCruncher are particularly adept with WordPerfect documents.
Others read files produced by a variety of word-processing, database,
spreadsheet, and even drawing programs. Nearly all can manage plain
text files, and some packages require files to be in this format
before they can work with them.
The second value-added feature of
document processors is their search features. As part of their
management of the qualitative dataset, document processors allow the
analyst to specify a variety of computer files in which to conduct a
single search. ZyIndex, for example, searches documents that remain
in their native format off-line, allows the analyst to keep track of
changes to documents through several revisions, and indexes files
so they can be readily included or excluded from particular searches.
Document processors can mount complex searches: combinations or
sequences of text strings; strings within specified proximity of
each other; word synonyms, stems, and roots; and searches defined
through Boolean, fuzzy, or set logic. Some display the results of
searches interactively so that analysts can see how the addition or
deletion of certain search terms in a complex search affects the
number of hits produced.
Document processors are designed to make
it easy for ethnographers to investigate data they have collected.
Compared to word processors, document processors do a better job of
placing the complete ethnographic dataset in the hands of the
analyst. They allow the ethnographer to search more easily for
desired pieces of text and to investigate how the text is arranged
in the dataset. But document processors place some limitations on
the format of data, especially on the use of non-text data such as
drawings, figures, or other freehand notes. Although searching and
retrieving text from an ethnographic database is a relatively
non-invasive way of using CAQDA software, ethnographers must not be
lulled into a false sense of security. CAQDA software betters the
odds of finding significant material in the ethnographic database,
but it does not assure it.
Data Organization
Searching and retrieving allows the analyst to inspect but not alter
the ethnographic database. However, CAQDA packages such as askSam,
Folio Views, MAX, Tabletop, HyperQual2, Kwalitan, Martin, QUALPRO,
and The Ethnograph allow the analyst to alter the form of the
ethnographic database by organizing its text.4
Data organization is one of the dominant forms of contemporary CAQDA,
and the packages listed here include some widely discussed in the
literature (see Armstrong
1995;, Mangabeira 1995;, Smith
& Hesse-Biber 1996;, Sprokkereef
et al 1995;, Weaver
& Atkinson 1994, 1995).
Organizers expand on document processors
in two ways. First, organizers allow the ethnographer to attach a
structure to the ethnographic database. Some document processors can
retrieve text chunks in context. Organizers create context by giving
analysts control over the structure of the ethnographic database, and
this structure can be manipulated and analyzed by the researcher.
Organizers can also structure the ethnographic database by adding
database fields for factual information and for memos that are
produced during analysis. The second addition of organizers is the
ability to code ethnographic data according to a theoretical scheme
developed by the analyst. Organizers are designed to tag chunks of
text with analytical codes and to retrieve codes and tagged text.
Retrieval of codes frequently includes the ability to search for
multiple codes, to retrieve the text associated with codes, or to
count codes.
ORGANIZING AND ANNOTATING
Organizing and annotating are two basic tasks of qualitative data
analysis. Some computer applications are designed to translate these
activities with fidelity from hard copy to electronic form. For
example, HyperQual2 and Martin use note cards as an organizing
metaphor. Like their hard-copy counterpart, the note cards of these
CAQDA packages each contain a single chunk of text. Electronic cards
can be replicated and sorted into stacks, and these stacks then
provide the raw materials to write up memos, annotations, and the
ethnographic report. Another way to organize a hard-copy database is
to use database-like fields. Fields can contain a variety of
information including factual information that situates the
ethnographic text to which it is attached (data collector, date of
interview or observation, information about the subject of the note)
or analytical information about the text itself. CAQDA software
such as askSam facilitates the creation, insertion, and organization
of these fields. Once organized, these CAQDA programs can quickly
search and retrieve information from database fields and quickly
count and tabulate the results of these searches.
Note cards, memos, and database fields
are easily grasped metaphors for organizing data; they have been
used by generations of ethnographers. Other CAQDA software draws on
metaphors without long pedigrees in the ethnographic community. Some
of these packages, such as MAX and Tabletop, move the qualitative
researcher closer to a quantitative research style. MAX allows
ethnographers who have also collected quantitative data to integrate
both text and numbers into a single analytical space. Tabletop displays
relationships between previously identified features of the
ethnographic database in graphs such as Venn diagrams and scatter
plots. Finally, packages such as Folio Views provide a menu of
organizational tools that includes outline levels, database fields,
and "pop-up" notes. It is up to the analyst to determine
which tools facilitate appropriate organization of the ethnographic
database and how they should be applied.
CAQDA packages that accommodate
organizing and annotating the ethnographic database are useful in a
variety of situations, but they are particularly useful in research
projects as they expand in size and scope. Multisite or multiyear
ethnographic projects generate a plethora of notes that beg for
efficient organization. Flexible annotations are particularly
valuable in multiresearcher projects in that each researcher
provides her or his own analysis and commentary.
CODING, RETRIEVING, AND COUNTING
Coding and retrieving is one of the central tasks of CAQDA software
packages. Many of the software packages discussed above can code
textual data, retrieve text based on applied codes, and tabulate
which codes have been applied to which text. Most packages discussed
in this section and below use coding and retrieving as their primary
method of analysis or as a preface to other kinds of analysis. There
are many ways to apply codes to text. Software such as Kwalitan,
QUALPRO, or The Ethnograph number each line in the ethnographic
database and apply codes to specific lines. Some packages encourage
coding on the computer screen, whereas others encourage the analyst
to code a numbered print-out of the text for later entry. Multiple
codes can be applied to one line or chunk of text, but some packages
place limits on the number of codes that can be applied (Coffey
& Atkinson 1996). No CAQDA package eases the intellectual
labor involved in coding, but code-and-retrieve software eases the
administrative labor of applying and altering a coding scheme. This
is especially so for software packages that take advantage of the
graphical user interface of the Macintosh or Windows operating
systems; in these, the analyst uses a mouse to highlight a text
passage on the computer screen and then selects the code that
applies to that section.
Once codes are applied to the
ethnographic database, CAQDA software greatly accelerates analysis
based on retrieving codes. Code-and-retrievers find codes using the
same powerful features that document processors applied to the raw
database. Multiple codes may be searched for at once. Hierarchies of
codes can be established so that searches for higher-order terms
also retrieve instances of lower-order terms. Complex searches can
be formulated using Boolean, sequential, and proximity logic.
Retrieval may yield a display of text associated with a code or a
union of codes, or it may yield counts where those codes were
applied. A number of CAQDA packages support cross-tabular displays
of counts.
Organizing with CAQDA alters the
ethnographic database in two ways. First, the database can be
organized using database fields, hierarchical levels, or annotations
so that the analyst has an easier time placing data in context and
moving about in large ethnographic databases. Second, the database
can be organized by applying codes to the text of the database so
that the analyst can retrieve information from the database based on
a theoretical mark-up of the text. CAQDA software facilitates the
administration of both of these activities, but it does little to
guide the intellectual work involved.
Symbolic Manipulation
A fine line separates CAQDA packages that organize data from those
that manipulate symbols. Symbolic manipulation software helps the
analyst develop or test theories about relationships in the
ethnographic database. Like data organizers, symbol manipulators are
a popular form of CAQDA, and software packages such as NUD.IST,
AQUAD, ATLAS/ti, Inspiration, MECA, MetaDesign, SemNet,
HyperRESEARCH, and QCA are widely discussed in published literature
(Hesse-Biber et al 1991, Huber
& García 1991, Muhr
1991, Ragin 1987, Richards
1995, Richards & Richards 1991b).
There are three kinds of CAQDA software
for symbol manipulation. Some symbol manipulators begin where
code-and-retrievers leave off. These packages focus analysts'
attention on the coding process, encouraging them to create positive
links between codes and to develop theory as they create a coding
scheme. A second form of symbol manipulation is done by
theory-building software. These packages take material that has been
abstracted from the ethnographic database through coding or other
means and analyze relationships between codes or concepts. The final
kind of CAQDA software that facilitates symbolic manipulation is
hypothesis testers. These packages facilitate the advancement and
testing of causal statements about relationships between codes or
concepts in multiple cases in the ethnographic database.
VALUE-ADDED CODERS
The coding process already contains the seeds of symbol manipulation. Value-added
coders add additional coding and analysis features to allow the
analyst to move closer to the manipulation of concepts—usually by
moving further from the ethnographic text. Software packages such as
AQUAD allow the analyst to search purposefully through the
ethnographic database for combinations of codes. The analyst can
look for theoretically significant combinations of codes, tabulate
the number of instances, and compare them to counts for combinations
of codes that represent competing theories. Value-added coders consider
the ethnographic database on a case-by-case basis so the counts and
cross-tabulations they produce are a case-based numerical summary in
contrast to the variable-based summaries provided by quantitative
analysis.
A second way of transforming coding into
symbol manipulation is to involve the computer in the construction
of the coding scheme. NUD.IST and other packages force analysts to
develop hierarchical relationships between codes as they apply them
to the ethnographic database (Richards
& Richards 1995). The construction of hierarchical
categories theoretically concretizes the codes used and makes the
logic of the coding scheme explicit as it is developed and applied.
Hierarchical coding schemes are particularly useful for
grounded-theory analysis, where new codes and elaboration of
existing codes occur continuously as the analyst works with the
ethnographic database.
Other value-added coders involve the
computer in the coding process without imposing hierarchical
constraints on the coding scheme. In ATLAS/ti, for example, the
coding scheme is not constrained by the software but is retained to
manipulate and analyze on its own. Text, codes, and memos can be
linked in the program and these links later inspected and
manipulated in conjunction with the original ethnographic text. Maps
of relationships between elements in the database provide an analytical
metaphor distinct from quantitative summary statistics or
cross-tabulations.
THEORY BUILDERS
Compared to value-added coders, theory-building CAQDA software moves
the analyst a step further from the ethnographic text. Software
packages such as ETHNO, Inspiration, MECA, and MetaDesign are
designed to facilitate the conceptual manipulation of ethnographic
data. Theory-building CAQDA software packages do not actually
construct theory, of course. They construct a graphical map (node
and links) of ethnographic data. Nodes represent data (fieldnotes,
memos, codes, etc), and links represent relationships between data.
Maps may help the analyst picture the project's theoretical shape,
the concepts in use, the relationship between those concepts, and
the ethnographic data that have been collected regarding each of
those concepts and links. Theory-building software facilitates
experiments with different concepts and links within the research
project.
But theory-building CAQDA packages need
not be reserved for the armchair ethnographer idly speculating on
abstract relationships in field data. Theory builders can also
incorporate links to the original ethnographic text that encourage
grounding in the original data and checks on concept validity. In
addition, theory builders need not be reserved for analyses of a
nearly finished research project (nor need they be the exclusive
province of the principal investigator). Theory builders can aid
ethnographers who are mapping complex empirical concepts or events
during the course of fieldwork.
HYPOTHESIS TESTING
Some value-added coders such as HyperRESEARCH and AQUAD as well as
stand-alone packages such as QCA use hypothesis testing, the third
form of symbol manipulation. Hypothesis-testing software bridges the
gap between qualitative and quantitative analysis by facilitating
case-based analysis of qualitative data. These packages allow the
analyst to specify hypotheses based on codes applied to text (in
HyperRESEARCH and AQUAD) or based on a descriptive matrix of cases
(in QCA). Hypothesis testers determine how causally antecedent
features of cases are related to outcomes. Boolean algebra is used
to define the antecedent conditions for each case in the database.
CAQDA software reduces large numbers of cases into statements that
identify under what conditions the outcome of interest prevails.
Qualitative hypothesis testing determines
what qualities of cases are crucial for a specified outcome. In
contrast, quantitative hypothesis testing focuses on the
contribution of different variables to the outcome. Aside from this
difference, CAQDA packages that include hypothesis-testing features
are similar to statistics software that dominates computer-assisted
analysis of quantitative data. Hypothesis testers encourage the
analyst to develop ideas in the form of equations (Boolean rather
than arithmetic) and to investigate how different terms (binary codes
rather than multivalue variables) in the equation affect its ability
to accurately explain outcomes.
Stand-alone hypothesis testers remove the
analyst from the original ethnographic database. These software
packages are useful in the analysis of data from a variety of
sources and not only from ethnographic field studies. Hypothesis
testers that include search-and-retrievers or data organizers may
encourage the analyst to remain in contact with the ethnographic
database even as analysis proceeds along more abstract and
quasi-quantitative avenues. Ideally, hypothesis-testing software
allows the analyst to ensure reliability through hypothesis checking
and to maintain validity by returning frequently to re-examine the
original ethnographic database and the codes, memos, and annotations
that have accumulated over the course of the research project.
Symbol manipulation includes a variety of
techniques for analyzing ethnographic data in ways that take
advantage of microcomputers. Value-added coders encourage the
analyst to develop explicit links between codes and data as the
analysis proceeds. The software keeps track of the relationships
between codes as they develop and then makes them available for
later re-inspection and analysis. Theory builders facilitate
exploration of concepts in ethnographic research projects through
graphical displays and the ability to quickly move between different
levels of detail. Finally, hypothesis testers move CAQDA closer to
the practices of quantitative research by embracing the goals of
reliability and explanation. Hypothesis-testing packages may even
allow analysts to strive for reliability and causal explanation
without losing the traditional advantages of qualitative data with
respect to validity.
FUTURE DIRECTIONS AND RELEVANCE OF
CAQDA
The current state of computer-assisted
data analysis among qualitative researchers resembles the proverbial
water glass that may be either half full or half empty. The large
number of CAQDA software programs available suggests that we are in
a preliminary stage of computer entry into the qualitative field.
With time, the computer will do for qualitative data analysis what
it has done in the quantitative realm: reduce labor, regularize
procedures for data gathering and analysis, and establish conventions
for the reporting of results. Moreover, the diversity of program
options will allow these advances to occur along parallel
methodological lines so that regularizing data-handling procedures
will not require homogeneous epistemological stances. On the other
hand, the still infrequent mention of CAQDA in ethnographic writing
means that the expansion of software choices has not yet influenced
the course of ethnographic research. CAQDA may be a significant
advance for positivist ethnographers, but its potential for
regularizing analysis in the qualitative field has not been reached.
Of course, inertia among researchers and peer reviewers may account
for some of the gap between expanding software choices and the
dearth of CAQDA mention in published research (Lee
& Fielding 1991:9). But there are fundamental issues about
qualitative data analysis that inform the half-full and half-empty
perspectives on CAQDA.
Lack of the "Killer
App"
CAQDA software has proliferated in the last decade and a half, but
no "killer app" has emerged from among the ranks of CAQDA software
(Blank 1991). A killer app is a computer application that
makes the use of the computer irresistibly compelling by doing tasks
unmanageable without computer assistance, in the fashion that
spreadsheet programs VisiCalc and Lotus 1-2-3 motivated United
States businesses to place personal computers on employees' desks.
Most CAQDA software diminishes the amount of labor needed to
organize and code ethnographic data but does not fundamentally
change the process of ethnographic analysis. In fact, ethnographers
considering computer use must scale several learning curves (which
programs are available, what are the basics of seemingly appropriate
ones, what is the actual operation of the selected one) and then
shape their data and analysis to the requirements of the chosen
software package. Lacking an irresistibly compelling reason to adopt
CAQDA, ethnographers may forgo computer assistance simply because
the costs outweigh the benefits.
The computer offers three ways of
facilitating qualitative analysis that may lead to, but are no
guarantee of, the enthronement of a CAQDA killer app. First, CAQDA
packages reduce the administrative burdens of ethnographic analysis.
Administrative assistance is a strong reason to climb learning
curves in some research projects, such as those that use
grounded-theory methods or those large projects that involve
multiple sites or multiple researchers. But given the diversity of
techniques for ethnographic analysis, administrative reduction is
compellingly attractive to only a fraction of qualitative
researchers. Second, many CAQDA programs allow the user to analyze
ethnographic materials that are difficult to access without the
computer. These packages integrate text, graphics, sound, and video;
they encourage analysis based on the creation of links between
distinct pieces of the ethnographic database; and they open up new
possibilities for the presentation of ethnographic research.
However, not only do many ethnographers work exclusively with text,
but also text and graphics are the dominant form of the ethnographic
report. Multimedia capability alone does not create a killer app.
Third, some of the features of symbol-manipulation software are not
easily replicated without a computer. Potentially, these packages
contain the seeds of a killer app.
Any CAQDA software that aspires to the
title of killer app must accomplish two tasks. Like
symbol-manipulation software, it must offer analysts the ability to
perform analyses that are unmanageable without a computer. To be
compelling, the CAQDA software package will have to constitute its
own best marketing device. In addition, the methodological and
epistemological diversity of ethnographic data analysis means that
CAQDA software will have to offer different analytical facilities to
different analysts. The challenge of the CAQDA killer app is to
facilitate the analytical strategies of positivists and
non-positivists with diverse analytical goals without
disproportionately imposing barriers to entry on any one group. At
present, popular software packages meet the challenges of one group
or another, but no killer app appears to be on the horizon.
The Crisis of
Representation and CAQDA
Part of the reason that no CAQDA package is poised to become a
killer app is that contemporary methodological discussions in
ethnography are not related to the integration of computers into
qualitative data analysis—a fact our survey of recently published
ethnographic work makes clear (see footnote 1). References, implicit
and explicit, to the double crisis of representation and
legitimation, what Denzin & Lincoln have termed the fifth
"moment" of qualitative research, appear frequently in
published ethnographies, and the crisis is of great concern to
methodologists (Denzin
& Lincoln 1994b, 1995;,
Snow & Morrill 1995a, but see also Snow
& Morrill 1995b).5
In this climate of ferment, the rules for
analysis are open to question, and one of the CAQDA paradigms may
emerge to organize future qualitative work. Users and developers of
hypertext software are particularly excited and optimistic about
this prospect. Hypertext not only makes the case for CAQDA as a
killer app but also addresses the limitations of previous
conceptions of computer use. Hypertext analysis is less rigid, more
susceptible to interpretation, and most importantly, not lineally
descended from the numerical processing paradigm used by
quantitative researchers.
But the crisis in legitimation is a
particularly hostile atmosphere for computer-assisted methods that
are often associated with a positivistic approach to data analysis.
Qualitative researchers have already expressed concerns about the
use of CAQDA in practice. One fear is that the computer will
"take over" qualitative data analysis—turning against the
ethnographer like Frankenstein's monster. Theoretically, this fear
is calmed by the reminder that the real work of qualitative data
analysis lies not in the mechanics of searching for text, applying
codes to data, or testing hypotheses using those codes. Rather, the
work lies in the annotation and rewriting of notes, in the conceptualization
and development of a coding scheme, and in the art of proposing
reasonable hypotheses (Hesse-Biber
1995). Practically, researchers report that the use of CAQDA software
encourages the exact opposite of the Frankenstein scenario. Outside
the computer—in piles and files of note cards, transcripts, and
memos or in boxes of audiotape—the data overwhelm the ethnographer.
The computer allows the ethnographer to manage the overwhelming
amount of data. This encourages the ethnographer to approach the
data and become comfortable "playing" with it and learning
it (Smith & Hesse-Biber 1996). In short, rather than
distancing the ethnographer from the data, the use of the computer
reduces the distance between analyst and data by making the latter
less overwhelming and more approachable. The computer can facilitate
the analyst's movement away from the data, but it does not cause
this movement.
There remains, of course, the issue of
what the ethnographic enterprise is. When it is based solely on
"understanding," as it is for the traditions of
ethnomethodology and symbolic interaction, computer assistance
cannot make up for the shortcomings in the researcher's basic talent
to interpret. However, for ethnomethodologists interested in
analyzing indexical language patterns, for example, CAQDA software
has the potential to be quite helpful (see, for instance, Schegloff
1996). In the case of anthropology, there remains the question
of whether it is necessary for the ethnographer to penetrate the
psychological world of the native or simply to interpret it through
what Geertz identifies as a series of symbolic forms—words, images,
institutions, and behaviors (Geertz
1983:58). CAQDA can help the researcher identify social
patterns, but it cannot substitute for the insight of the
researcher. For example, Geertz found in his study of Moroccan
society that the linguistic concept of "nisba" was
important in separating people from each other and determining what
it meant to be a person. Using suffixes, the people he studied were
able to identify who belonged to what tribe, city, family, etc.
Although Geertz found this pattern without the use of CAQDA,
computer assistance would have increased the probability that a less
insightful analyst would have seen this concept recur in a variety
of contexts and grasped its significance. CAQDA can compensate for
the limitations of the fieldworker by highlighting significant patterns
recorded in the notes, even if the researcher did not recognize the
pattern at the time the notes were recorded.
Systemization of
Ethnographic Methods
For ethnographers not torn by the twin crises of representation and
legitimation, the advent of CAQDA opens a couple of possibilities. First,
the use of CAQDA software makes explicit the methods of analysis
used in converting ethnographic data into ethnographic reports. The
explicit discussion of methods of analysis in the grounded-theory
school midwifed the development of much CAQDA software.
Computer-assisted analysis goes beyond discussion, however, by
allowing ethnographers to share details of their analysis process.
Even when ethical concerns prevent the sharing of raw data, the use
of CAQDA may increase reliability by making explicit the concrete
steps taken in moving from data to conclusion.
Second, the use of computers fosters
increased reliability and generalizability by expanding the amount
of data that can be managed and exhaustively analyzed within a
single ethnographic project. Data expand rapidly in ethnographies
involving multiple sites or multiple researchers. In large-scale
sociological and anthropological studies, the senior researcher
becomes the analytical specialist (examples undertaken without CAQDA
include Lewis
1963, Moore & Garcia 1978, Rainwater
1970, Sullivan
1989, Warner 1963). All members of the research team funnel
data to the leader, who guides the analysis and writes research
reports. Computer assistance makes it possible for researchers to
collaborate more easily as data management devolves to the database
system.
Combined, explicit systems of analysis
and increased ability to generalize reliably suggest the development
of a new way of organizing data, asking research questions, and
systematically developing answers in ethnographic research. CAQDA
software may allow ethnographers to access large ethnographic
databases directly—without the theoretical intermediary of a single
intellectual vision or research goal. The computer can accommodate data
collected by multiple fieldworkers and facilitate coding, re-coding,
linking, and re-linking by multiple investigators. Within this
analytical space, differing understandings of the same database can
be produced and compared, and analysts can examine the procedures
undertaken to produce each account.
CONCLUSION
To date, all that many ethnographers have
had to rely on was their memory of the data they collected and the
meaning of those data in the context of their study. However, the
workings of memory create two potential problems for researchers
analyzing ethnographic field data. First, researchers may use those
data that were most dramatic in the fieldwork and erroneously present
them as being the most significant; second, they may use more data
from the later stages of fieldwork and less of what happened in the
middle or beginning because the later data are fresher and clearer
in their minds. CAQDA can help the careful analyst avoid both of
these problems, but it is no panacea. Researchers who use CAQDA
still face issues related to representation. Data quality is
directly tied to the ability of the researcher to observe
significant phenomena in the course of fieldwork and to recognize
what he or she has seen. While CAQDA can compensate for small
failures of detailed observation or sharp insight, it is no
substitute for either.
The use of CAQDA could stimulate team
approaches in ethnographic research that would generate a wealth of
data and make important analytic contributions (see the examples
cited above), but CAQDA does not eliminate the validity problems
inherent to team ethnography. Because data in ethnographic teams are
gathered by a number of researchers who in many cases have different
degrees of training (as well as different degrees of insight), there
is no way to assure consistency in what each researcher thinks it
important to record. Thus, there are validity problems for which
CAQDA cannot compensate. Ethnographers spend much of their time
engaged in filework rather than fieldwork (Plath
1990), but quality analysis that has a high degree of validity and
reliability remains dependent on the competence and consistency of
fieldworkers.
FOOTNOTES
1 The constructed categories of "quantitative" and
"qualitative" research have led sociologists to
misunderstand the fact that real differences in research method are
due to adherence to different epistemologies and not to the use of
quantitative or qualitative data. For ease of reading, we drop the
use of quotation marks, but the constructed nature of the categories
quantitative research and qualitative research should be borne in
mind.
2 With few exceptions, ethnographic works published in Qualitative
Sociology, Journal of Contemporary Ethnography, and Symbolic Interaction
as well as in the American Sociological Review, the American
Journal of Sociology, Social Forces, and Social Problems
make no mention of the use of CAQDA. Book-length ethnographies in
our areas of expertise (gangs, poverty, urban and community studies)
reviewed in the journals listed above (as well as in Contemporary
Sociology), and those discussed in three recent overviews of
qualitative research (Charmaz
& Olesen 1997, Horowitz
1997, Morrill & Fine 1997) also rarely mention CAQDA.
3 By way of full disclosure, Sánchez-Jankowski is now using
the askSam package, and Dohan
is using the Folio Views package.
4 Most CAQDA packages have capabilities that defy easy
categorization according to the kind of analysis they perform. Among
document processors, Sonar Professional, ZyINDEX, GOFER, and
FYI3000PLUS also include significant data-organizing features.
Similarly, askSam, Folio Views, MAX, and Kwalitan are able to search
for and retrieve text from the ethnographic database.
5 We hope not to open the Pandora's box of ethnography's
decades-long crisis of representation. Consult Denzin
& Lincoln 1994a for a variety of perspectives on this
question.
Annu. Rev. Sociol. 1998.
24:477-498
Copyright © 1998 by Annual Reviews. All rights reserved
0360-0572/98/0815-0477
REFERENCES:
1.Agar M. 1991. The right brain strikes again.
See Fielding & Lee 1991,pp. 181–94
2. Armstrong D. 1995. Finding a "role"
for The ETHNOGRAPH in the analysis of qualitative data. See Burgess
1995,pp. 63–79
3. Berg BL. 1995. Qualitative Research
Methods for the Social Sciences. Boston: Allyn & Bacon
4. Blank G. 1991. Why sociological computing
gets no respect. Soc. Sci. Comput. Rev. 9:593–611 5. Blumer H. 1969. Symbolic
Interactionism: Perspective and Method. Berkeley: Univ. Calif. Press
6. Burawoy M. 1998. The extended case method. Soc.
Theory 16:63–92
7. Burawoy M, Burton A, Ferguson AA, Fox KJ,
Gamson J, et al.1991. Ethnography Unbound: Power and Resistance in the
Modern Metropolis. Berkeley: Univ. Calif. Press
8. Burgess RG, ed.1995. Studies in
Qualitative Methodology, Vol. 5. Greenwich, CT: JAI
9. Carsaro WA, Heise DR. 1990. Event structure
models from ethnographic data. Sociol. Methodol. 20:1–58
10. Charmaz K, Olesen V. 1997. Ethnographic
research in medical sociology: its foci and distinctive contributions. Sociol.
Methods Res. 25:452–94
11. Coffey A, Atkinson P. 1996. Making Sense
of Qualitative Data. Thousand Oaks, CA: Sage
12. Denzin NK, Lincoln YS, eds.1994a. Handbook
of Qualitative Research. Thousand Oaks, CA: Sage
13. Denzin NK, Lincoln YS. 1994b. Introduction:
entering the field of qualitative research. See Denzin
& Lincoln 1994a,pp. 1–19
14. Denzin NK, Lincoln YS. 1995. Transforming
qualitative research methods. J. Contemp. Ethnogr. 24:349–58
15. Fielding NG, Lee RM, eds.1991. Using
Computers in Qualitative Research. Newbury Park, CA: Sage
16. Fischer MD. 1994. Applications in Computing
for Social Anthropologists. London: Routledge
17. Garfinkel H. 1967. Studies in
Ethnomethodology. Englewood Cliffs, NJ: Prentice Hall
18. Geertz C. 1983. Local Knowledge: Further
Essays in Interpretative Anthropology. New York: Basic Books
19. Glaser BG, Strauss AL. 1967. The
Discovery of Grounded Theory: Strategies for Qualitative Research. New
York: Aldine
20. Hesse-Biber S. 1995. Unleashing
Frankenstein's monster? The use of computers in qualitative research. See Burgess
1995a,pp. 25–41
21. Hesse-Biber S, Dupuis P, Kinder TS. 1991.
HyperRESEARCH: a computer program for the analysis of qualitative data with an
emphasis on hypothesis testing and multimedia analysis. Qual. Sociol.
14:289–306
22. Horowitz R. 1997. Barriers and bridges to
class mobility and formation: ethnographies of stratification. Sociol.
Methods Res. 25:495–538
23. Huber GL, García CM. 1991. Computer
assistance for testing hypotheses about qualitative data: the software package
AQUAD 3.0. Qual. Sociol. 14:342–48
24. Huberman AM, Miles MB. 1994. Data management
and analysis methods. See Denzin
& Lincoln 1994a,pp. 428–44
25. Kelle U, ed.1995. Computer-Aided
Qualitative Data Analysis. Thousand Oaks, CA: Sage
26. Kirk RC. 1981. Microcomputers in
anthropological research. Sociol. Methods Res. 9:461–72
27. Lee RM, Fielding NG. 1991. Computing for
qualitative research: options, problems and potential. See Fielding
& Lee 1991,pp. 16–37
28. Lewis O. 1963. Life in a Mexican Village:
Tepoztlan Restudied. Urbana: Univ. Ill. Press
29. Lonkila M. 1995. Grounded theory as an
emerging paradigm for computer-assisted qualitative data analysis. See Kelle
1995,pp. 41–51
30. Mangabeira W. 1995. Qualitative analysis and
microcomputer software: some reflections on a new trend in sociological
research. See Burgess
1995,pp. 43–62
31. Moore JW, Garcia R. 1978. Homeboys:
Gangs, Drugs, and Prison in the Barrios of Los Angeles. Philadelphia, PA:
Temple Univ. Press
32. Morrill C, Fine GA. 1997. Ethnographic
contributions to organizational sociology. Sociol. Methods Res.
25:424–51
33. Muhr T. 1991. ATLAS/ti—a prototype for the
support of text interpretation. Qual. Sociol. 14:349–72
34. Pfaffenberger B. 1988. Microcomputer
Applications in Qualitative Research. Newbury Park, CA: Sage
35. Plath DW. 1990. Fieldnotes, filed notes, and
the conferring of note. In Fieldnotes: The Making of Anthropology, ed. R
Sanjek,pp. 371–84. Ithaca, NY: Cornell Univ. Press
36. Prein G, Kelle U, Bird K. 1995. An overview
of software. See Kelle
1995,pp. 190–210
37. Ragin CC. 1987. The Comparative Method:
Moving Beyond Qualitative and Quantitative Strategies. Berkeley: Univ.
Calif. Press
38. Rainwater L. 1970. Behind Ghetto Walls:
Black Families in a Federal Slum. Chicago: Aldine
39. Richards L. 1995. Transition work!
Reflections on a three-year NUD.IST project. See Burgess
1995,pp. 105–40
40. Richards L, Richards T. 1991a. The
transformation of qualitative method: computational paradigms and research
processes. See Fielding
& Lee 1991,pp. 38–53
41. Richards T, Richards L. 1991b. The NUDIST
qualitative data analysis system. Qual. Sociol. 14:307–24
42. Richards T, Richards L. 1995. Using
hierarchical categories in qualitative data analysis. See Kelle
1995,pp. 80–95
43. Richards TJ, Richards L. 1994. Using
computers in qualitative research. See Denzin
& Lincoln 1994a,pp. 445–62
44. Schegloff EA. 1996. Confirming allusions:
toward an empirical account of action. Am. J. Sociol. 102:161–216
45. Smith BA, Hesse-Biber S. 1996. Users'
experiences with qualitative data analysis software: neither Frankenstein's
monster nor muse. Soc. Sci. Comput. Rev. 14:423–32
46. Snow DA, Morrill C. 1995a. Ironies, puzzles,
and contradictions in Denzin and Lincoln's vision for qualitative research. J.
Contemp. Ethnogr. 24:358–62
47. Snow DA, Morrill C. 1995b. A revolutionary handbook
or a handbook for revolution? J. Contemp. Ethnogr. 24:341–49
48. Sprokkereef A, Lakin E, Pole CJ, Burgess RG.
1995. The data, the team, and The ETHNOGRAPH. See Burgess
1995,pp. 81–105
49. Stanley L, Temple B. 1995. Doing the
business? Evaluating software packages to aid the analysis of qualitative data
sets. See Burgess,pp. 169–93
50. Strauss AL. 1987. Qualitative Analysis
for Social Scientists. New York: Cambridge Univ. Press
51. Sullivan M. 1989. Getting Paid: Youth
Crime and Work in the Inner City. Ithaca, NY: Cornell Univ. Press
52. Tesch R. 1990. Qualitative Research:
Analysis Types and Software Tools. New York: Falmer
53. Tesch R. 1991a. Introduction: computers and
qualitative data II. Qual. Sociol. 14:225–44
54. Tesch R. 1991b. Software for qualitative
research: analysis needs and program capabilities. See Fielding
& Lee 1991,pp. 16–37
55. Warner WL. 1963. Yankee City. New
Haven, CT: Yale Univ. Press
56. Weaver A, Atkinson P. 1994. Microcomputing
and Qualitative Data Analysis. Aldershot, UK: Avebury
57. Weaver A, Atkinson P. 1995. From coding to
hypertext: strategies for microcomputing and qualitative data analysis. See Burgess
1995,pp. 141–68
58. Weitzman EA, Miles MB. 1995. Computer
Programs for Qualitative Data Analysis: A Software Sourcebook. Thousand
Oaks, CA: Sage
59. Werner O, Schoepfle GM. 1987. Systematic
Fieldwork, Vol. 2: Ethnographic Analysis and Data Management.
Newbury Park, CA: Sage
60. Winer LR, Carrière M. 1991. A qualitative
information system for data management. Qual. Sociol. 14:245–62