C h a p t e r O v e r v i e w
Qualitative data analysis is the
nonnumerical assessment of
observations made through
participant observation, content
analysis, in-depth interviews,
and other qualitative research
techniques. Although qualitative
analysis is as much an art as a
science, it has its own logic and
techniques, some of which are
enhanced by special computer
programs.
Introduction
Linking Theory and Analysis
Discovering Patterns
Grounded Theory Method
Semiotics
Conversation Analysis
Qualitative Data Processing
Coding
Memoing
Concept Mapping
Computer Programs
for Qualitative Data
QDA Programs
Leviticus as Seen
through Qualrus
NVivo
The Qualitative Analysis
of Quantitative Data
Evaluating the Quality
of Qualitative Research
Ethics and Qualitative
Data Analysis
C HAPTER 1 3
Qualitative Data Analysis
Introduction
Later chapters in Part 4 of this book will deal
with the quantitative analysis of social research
data, sometimes called statistical analysis. Recent
decades of social science research have tended to
focus on quantitative data analysis techniques.
This focus, however, sometimes conceals another
approach to making sense of social observations:
qualitative analysis—methods for examining
social research data without converting them to a
numerical format. This approach predates quantitative
analysis. It remains a useful approach
to data analysis and is enjoying a resurgence of
interest among social scientists.
Learning qualitative analysis techniques
requires a different approach than learning
quantitative ones. Although statistical analyses
may intimidate some students, the steps involved
can sometimes be learned in a rote manner. That
is, with practice, the rote exercise of quantitative
skills can produce an increasingly sophisticated
understanding of the logic that lies behind those
techniques.
It’s much more difficult to teach qualitative
analysis as a series of rote procedures. In this
case, understanding must precede practice. In
this chapter, we begin with the links between
research and theory in qualitative analysis. Then
we examine some procedures that have proved
useful in pursuing the theoretical aims. After
considering some simple manual techniques,
we’ll take some software programs out for a spin.
LinkingTheory and Analysis
As suggested in Chapter 10 and elsewhere in
this book, qualitative research methods involve
a continuing interplay between data collection
and theory. As a result, I’ve already talked
about qualitative data analysis in earlier discussions
of field research and content analysis. In
quantitative research, it’s sometimes easy to get
caught up in the logistics of data collection and in
the statistical analysis of data, thereby losing sight
of theory for a time. This is less likely in qualitative
research, where data collection, analysis, and
theory are more intimately intertwined.
In the discussions to follow, we’ll use the
image of theory offered by Anselm Strauss
and Juliet Corbin (1994: 278) as consisting of
“plausible relationships proposed among concepts
and sets of concepts.” They stress “plausible”
to indicate that theories represent our best
understanding of how life operates. The more
our research confirms a particular set of relationships
among particular concepts, however, the
more confident we become that our understanding
corresponds to social reality.
Whereas qualitative research is sometimes
undertaken for purely descriptive purposes—
such as the anthropologist’s ethnography
detailing ways of life in a previously unknown
tribe—the rest of this chapter focuses primarily
on the search for explanatory patterns. As we’ll
see, sometimes the patterns occur over time, and
sometimes they take the form of causal relations
among variables. Let’s look at some of the ways
qualitative researchers uncover such patterns.
Discovering Patterns
John Lofland and his colleagues (2006: 149–65)
suggest six different ways of looking for patterns
in a particular research topic. Let’s suppose you’re
interested in analyzing child abuse in a certain
neighborhood. Here are some questions you
might ask yourself to make sense out of your data:
1.	 Frequencies: How often does child abuse
occur among families in the neighborhood
under study? (Realize that there may be a
difference between the frequency and what
people are willing to tell you.)
2.	 Magnitudes: What are the levels of abuse?
How brutal are they?
3.	 Structures: What are the different types of
abuse: physical, mental, sexual? Are they
related in any particular manner?
qualitative analysis  The nonnumerical examination
and interpretation of observations, for the
purpose of discovering underlying meanings and
patterns of relationships. This is most typical of
field research and historical research.
Linking Theory and Analysis ■ 383
4.	 Processes: Is there any order among the elements
of structure? Do abusers begin with mental
abuse and move on to physical and sexual
abuse, or does the order of elements vary?
5.	 Causes: What are the causes of child abuse? Is
it more common in particular social classes or
among different religious or ethnic groups?
Does it occur more often during good times
or bad?
6.	 Consequences: How does child abuse affect the
victims, in both the short and the long term?
What changes does it cause in the abusers?
For the most part, in examining your data
you’ll look for patterns appearing across several
observations that typically represent different
cases under study, an approach called cross-case
analysis. A. Michael Huberman and Matthew
Miles (1994: 435f) offer two strategies for crosscase
analysis: the variable-oriented and the caseoriented
analysis. Variable-oriented analysis is
similar to a model we’ve already discussed from
time to time in this book. If we were trying to
predict the decision to attend college, Huberman
and Miles suggest, we might consider variables
such as “gender, socioeconomic status, parental
expectations, school performance, peer support,
and decision to attend college” (1994: 435). Thus,
we would determine whether men or women
were more likely to attend college. The focus of
our analysis would be on interrelations among
variables, and the people observed would be
primarily the carriers of those variables.
Variable-oriented analysis may remind you of
the discussion in Chapter 1 that introduced the
idea of nomothetic explanation. The aim here
is to achieve a partial, overall explanation using
relatively few variables. The political pollster who
attempts to explain voting intentions on the basis
of two or three key variables is using this approach.
There is no pretense that the researcher
can predict every individual’s behavior nor even
explain any one person’s motivations in full.
Sometimes, though, it’s useful to have even a partial
explanation of overall orientations and actions.
You may also recall the introduction to idiographic
explanation in Chapter 1, wherein we
attempt to understand a particular case fully.
In the voting example, we would endeavor to
learn everything we could about all the factors
that came into play in determining one person’s
decision on how to vote. This orientation lies
at the base of what Huberman and Miles call a
case-oriented analysis.
In a case-oriented analysis, we would look
more closely into a particular case, say,
Case 005, who is female, middle-class, has
parents with high expectations, and so on.
These are, however, “thin” measures. To do a
genuine case analysis, we need to look at a full
history of Case 005; Nynke van der Molen,
whose mother trained as a social worker but
is bitter over the fact that she never worked
outside the home, and whose father wants
Nynke to work in the family florist shop.
Chronology is also important: two years ago,
Nynke’s closest friend decided to go to college,
just before Nynke began work in a stable
and just before Nynke’s mother showed her
a scrapbook from social work school. Nynke
then decided to enroll in veterinary studies.
(1994: 436)
This abbreviated commentary should give you
some idea of the detail involved in this type of
analysis. Of course, an entire analysis would
be more extensive and pursue issues in greater
depth. This full, idiographic examination, however,
tells us nothing about people in general. It
offers nothing in the way of a theory about why
people choose to attend college.
Even so, in addition to understanding one
person in great depth, the researcher sees the
critical elements of the subject’s experiences as
instances of more-general social concepts or variables.
For example, Nynke’s mother’s social work
training can also be seen as “mother’s education.”
Her friend’s decision can be seen as “peer
influence.” More specifically, these could be seen
as independent variables having an impact on
the dependent variable of attending college.
Of course, one case does not a theory make—
hence Huberman and Miles refer to cross-case
cross-case analysis  An analysis that involves an
examination of more than one case; this can be
either a variable-oriented or case-oriented analysis.
variable-oriented analysis  An analysis that
describes and/or explains a particular variable.
case-oriented analysis  An analysis that aims to
understand a particular case or several cases by
looking closely at the details of each.
384 ■ Chapter 13: Qualitative Data Analysis
analysis, in which the researcher turns to other
subjects, looking into the full details of their
lives as well but paying special attention to the
variables that seemed important in the first case.
How much and what kind of education did other
subjects’ mothers have? Is there any evidence of
close friends attending college?
Some subsequent cases will closely parallel
the first one in the apparent impact of particular
variables. Other cases will bear no resemblance
to the first. These latter cases may require the
identification of other important variables, which
may invite the researcher to explore why some
cases seem to reflect one pattern while others
reflect another.
Grounded Theory Method
The cross-case method just described should
sound somewhat familiar. In the discussion of
grounded theory in Chapter 10, we saw how
qualitative researchers sometimes attempt to establish
theories on a purely inductive basis. This
approach begins with observations rather than
hypotheses and seeks to discover patterns and
develop theories from the ground up, with no
preconceptions, though some research may build
and elaborate on earlier grounded theories.
Grounded theory was first developed by the
sociologists Barney Glaser and Anselm Strauss
(1967) in an attempt to come to grips with their
clinical research in medical sociology. Since then,
it has evolved as a method, with the cofounders
taking it in slightly different directions. The following
discussion will deal with the basic concepts
and procedures of the Grounded Theory
Method (GTM).
In addition to the fundamental, inductive
tenet of building theory from data, GTM employs
the constant comparative method. As Glaser and
Strauss originally described this method, it involved
four stages (1967: 105–13):
1.	 “Comparing incidents applicable to each category.”
As Glaser and Strauss researched the
reactions of nurses to the possible death of
patients in their care, the researchers found
that the nurses were assessing the “social
loss” attendant upon a patient’s death. Once
this concept arose in the analysis of one case,
they looked for evidence of the same phenomenon
in other cases. When they found
the concept arising in the cases of several
nurses, they compared the different incidents.
This process is similar to conceptualization
as described in Chapter 5—specifying
the nature and dimensions of the many concepts
arising from the data.
2.	 “Integrating categories and their properties.”
Here the researcher begins to note relationships
among concepts. In the assessment of
social loss, for example, Glaser and Strauss
found that nurses took special notice of a
patient’s age, education, and family responsibilities.
For these relationships to emerge,
however, it was necessary for the researchers
to have noticed all these concepts.
3.	 “Delimiting the theory.” Eventually, as the
patterns of relationships among concepts become
clearer, the researcher can ignore some
of the concepts that were initially noted but
are evidently irrelevant to the inquiry. In
addition to the number of categories being
reduced, the theory itself may become simpler.
In the examination of social loss, for
example, Glaser and Strauss found that the
assessment processes could be generalized beyond
nurses and dying patients: They seemed
to apply to the ways all staff dealt with all
patients (dying or not).
4.	 “Writing theory.” Finally, the researcher
must put his or her findings into words to be
shared with others. As you may have already
experienced for yourself, the act of communicating
your understanding of something actually
modifies and even improves your own
grasp of the topic. In GTM, the writing stage
is regarded as a part of the research process.
A later section of this chapter (on memoing)
elaborates on this point.
This brief overview should give you an idea
of how grounded theory proceeds. The many
techniques associated with GTM can be found
GroundedTheory Method (GTM)  An inductive
approach to research, introduced by Barney
Glaser and Anselm Strauss, in which theories are
generated solely from an examination of data
rather than being derived deductively.
constant comparative method  A component
of the Grounded Theory Method in which
observations are compared with one another and
with the evolving inductive theory.
Linking Theory and Analysis ■ 385
both in print and on the web. One key publication
is Anselm Strauss and Juliet Corbin’s Basics
of Qualitative Research (1998), which elaborates
on and extends many of the concepts and techniques
found in the original 1967 Glaser and
Strauss volume. On the web, you can search for
“grounded theory” to see a wealth of articles.
GTM is only one analytic approach to qualitative
data. In the remainder of this section, we’ll
take a look at some other specialized techniques.
Semiotics
Semiotics is commonly defined as the “science of
signs” and has to do with symbols and meanings.
It’s commonly associated with content analysis,
which was discussed in Chapter 11, though it can
be applied in a variety of research contexts.
Peter Manning and Betsy Cullum-Swan
(1994: 466) offer some sense of the applicability
of semiotics, as follows: “Although semiotics
is based on language, language is but one of the
many sign systems of varying degrees of unity,
applicability, and complexity. Morse code, etiquette,
mathematics, music, and even highway
signs are examples of semiotic systems.”
There is no meaning inherent in any sign,
however. Meanings reside in minds. So, a particular
sign means something to a particular
person. However, the agreements we have about
the meanings associated with particular signs
make semiotics a social science. As Manning and
Cullum-Swan point out
For example, a lily is an expression linked
conventionally to death, Easter, and resurrection
as a content. Smoke is linked to cigarettes
and to cancer, and Marilyn Monroe to
sex. Each of these connections is social and
arbitrary, so that many kinds of links exist
between expression and content.
(1994: 466)
To explore this contention, see if you can link
the signs with their meanings in Figure 13-1.
I’m confident enough that you know all the
“correct” associations that there’s no need for me
to give the answers. (OK, you should have said
1c, 2a, 3b, 4e, 5d.) The point is this: What do any
of these signs have to do with their “meanings”?
Draft an e-mail message to a Martian social
scientist explaining the logic at work here.
(You might want to include some “emoticons”
like : ) —another example of semiotics.)
There is no doubt a story behind each of
the linkages in Figure 13-1, and the meanings
you and I “know” today have been socially constructed.
Semiotic analysis involves a search for
the meanings intentionally or unintentionally
attached to signs.
Consider the sign shown in Figure 13-2, from
a hotel lobby in Portland, Oregon. What’s being
communicated by the rather ambiguous wording?
The first sentence seems to be saying that
the hotel is up-to-date with the current move
away from tobacco in the United States. Guests
who want a smoke-free environment need look
no farther: This is a healthy place to stay. At
the same time, says the second sentence, the
hotel would not like to be seen as inhospitable
to smokers. There’s room for everyone under
this roof. No one need feel excluded. This sign is
more easily understood within a marketing paradigm
than one of logic.
The “signs” examined in semiotics, of course,
are not limited to this kind of sign. Most are
quite different, in fact. Signs are any things that
are assigned special meanings. They can include
logos, animals, people, and consumer products.
Sometimes the symbolism is a bit subtle. A classic
analysis can be found in Erving Goffman’s Gender
Advertisements (1979). Goffman focused on advertising
pictures found in magazines and newspapers.
The overt purpose of the ads, of course, was
to sell specific products. But what else was communicated,
Goffman asked. What in particular
did the ads say about men and women?
Analyzing pictures containing both men and
women, Goffman was struck by the fact that
men were almost always bigger and taller than
semiotics  The study of signs and the meanings
associated with them. This is commonly associated
with content analysis.
1. Poinsettia
2. Horseshoe
3. Blue ribbon
4. “Say cheese”
5. “Break a leg”
a. Good luck
b. First prize
c. Christmas
d. Acting
e. Smile for a picture
SIGN MEANING
FIGURE 13-1
Matching Signs andTheir Meanings
© Cengage Learning®
386 ■ Chapter 13: Qualitative Data Analysis
the women accompanying them. (In many cases,
in fact, the picture managed to convey the distinct
impression that the women were merely
accompanying the men.) Although the most
obvious explanation is that men are, on average,
heavier and taller than women, Goffman suggested
the pattern had a different meaning: that
size and placement implied status. Those who
were larger and taller presumably had higher
social standing—more power and authority
(1979: 28). Goffman suggested that the ads communicated
that men were more important than
women.
In the spirit of Freud’s comment that “sometimes
a cigar is just a cigar” (he was a smoker),
how would you decide whether the ads simply
reflected the biological differences in the average
sizes of men and women or whether they
sent a message about social status? In part, Goffman’s
conclusion was based on an analysis of
the exceptional cases: those in which the women
appeared taller than the men. In these cases, the
men were typically of a lower social status—the
chef beside the society matron, for example. This
confirmed Goffman’s main point that size and
height indicated social status.
The same conclusion was to be drawn from
pictures with men of different heights. Those
of higher status were taller, whether it was the
gentleman speaking to a waiter or the boss guiding
the work of his younger assistants. Where
actual height was unclear, Goffman noted the
placement of heads in the picture. The assistants
were crouching down while the boss leaned over
them. The servant’s head was bowed so it was
lower than that of the master.
The latent message conveyed by the ads,
then, was that the higher a person’s head appeared
in the ad, the more important that person
was. And in the great majority of ads containing
men and women, the former were clearly portrayed
as more important. The subliminal message
in the ads, whether intended or not, was
that men are more powerful than women and
enjoy a higher status.
Goffman examined several differences besides
physical size in the portrayal of men and
women. As another example, men were typically
portrayed in active roles, women in passive ones.
The (male) doctor examined the child while the
(female) nurse or mother looked on, often admiringly.
A man guided a woman’s tennis stroke
(all the while keeping his head higher than
hers). A man gripped the reins of his galloping
horse, while a woman rode behind him with her
arms wrapped around his waist. A woman held
the football, while a man kicked it. A man took a
photo, which contained only women.
Goffman suggested that such pictorial patterns
subtly perpetuated a host of gender stereotypes.
Even as people spoke publicly about
gender equality, these advertising photos established
a quiet backdrop of men and women in
the “proper roles.”
Conversation Analysis
Ethnomethodology, as you’ll recall, aims to uncover
the implicit assumptions and structures
in social life. Conversation analysis (CA) seeks
to pursue that aim through an extremely close
scrutiny of the way we converse with one another.
In the examination of ethnomethodology
in Chapter 10, you saw some examples of conversation
analysis. Here we’ll look a little more
deeply into that technique.
David Silverman (1999), reviewing the work
of other CA theorists and researchers, speaks
of three fundamental assumptions. First, conversation
is a socially structured activity. Like
other social structures, it has established rules
conversation analysis (CA)  A meticulous analysis
of the details of conversation, based on a
complete transcript that includes pauses, hems,
and also haws.
FIGURE 13-2
Mixed Signals?
© Cengage Learning®
EarlBabbie
Qualitative Data Processing ■ 387
of behavior. For example, we’re expected to
take turns, with only one person speaking at a
time. In telephone conversations, the person
answering the call is expected to speak first (e.g.,
“Hello”). You can verify the existence of this
rule, incidentally, by picking up the phone without
speaking. You may recall that this is the sort
of thing ethnomethodologists tend to do.
Second, Silverman points out that conversations
must be understood contextually. The
same utterance will have different meanings in
different contexts. For example, notice how the
meaning of “Same to you!” varies if preceded
by “I don’t like your looks” or by “Have a
nice day.”
Third, CA aims to understand the structure
and meaning of conversation through excruciatingly
accurate transcripts of conversations.
Not only are the exact words recorded, but all
the uhs, ers, bad grammar, and pauses are also
noted. Pauses, in fact, are recorded to the nearest
tenth of a second.
The practical uses of this type of analysis are
many. Ann Marie Kinnell and Douglas Maynard
(1996), for example, analyzed conversations
between staff and clients at an HIV-testing clinic
to examine how information about safe sex
was communicated. Among other things, they
found that the staff tended to provide standard
information rather than try to speak directly to
a client’s specific circumstances. Moreover, they
seemed reluctant to give direct advice about safe
sex, settling for information alone.
These discussions should give you some
sense of the variety of qualitative analysis
methods available to researchers. Now let’s
look at some of the data-processing and
data-analysis techniques commonly used in
qualitative research.
Qualitative Data Processing
Let me begin this section with a warning. The
activity we’re about to examine is as much art as
science. At the very least, there are no cut-anddried
steps that guarantee success.
It’s a lot like learning how to paint with watercolors
or compose a symphony. Education in
such activities is certainly possible, and university
courses are offered in both. Each has its own
conventions and techniques as well as tips you
may find useful as you set out to create art or
music. However, instruction can carry you only
so far. The final product must come from you.
Much the same can be said of qualitative data
processing.
At the same time, researchers have developed
systematic and rigorous techniques for this
type of research. We’ll examine some of those
here. You can gain a more in-depth view of these
techniques from Constructing Grounded Theory:
A Practical Guide through Qualitative Research,
an excellent book by social researcher Kathy
Charmaz (2006).
This section presents some ideas relating to
the coding of qualitative data, writing memos,
and mapping concepts graphically. Although
far from a “how-to” manual, these ideas give
a useful starting point for finding order in
qualitative data.
Coding
Whether you’ve engaged in participant observation,
in-depth interviewing, collecting biographical
narratives, doing content analysis, or some
other form of qualitative research, you’ll now
be in the possession of a growing mass of data—
most typically in the form of textual materials.
Now what do you do?
The key process in the analysis of qualitative
social research data is coding—classifying or categorizing
individual pieces of data—coupled with
some kind of retrieval system (see Chapter 11).
Together, these procedures allow you to retrieve
materials you may later be interested in.
Let’s say you’re chronicling the growth of
a social movement. You recall writing up some
notes about the details of the movement’s earliest
beginnings. Now you need that information.
If all your notes have been cataloged by topic,
retrieving those you need should be straightforward.
As a simple format for coding and
retrieval, you might have created a set of file
folders labeled with various topics, such as
“History.” Data retrieval in this case means
pulling out the “History” folder and rifling
through the notes it contains until you find
what you need.
As you’ll see later in this chapter, there are
now sophisticated computer programs that allow
for a faster, more certain, and more precise
388 ■ Chapter 13: Qualitative Data Analysis
retrieval process. Rather than looking through a
“History” file, you can go directly to notes dealing
with the “Earliest History” or the “Founding”
of the movement.
Coding has another, even more important
purpose. As discussed earlier, the aim of data
analysis is the discovery of patterns among the
data, patterns that point to theoretical understandings
of social life. The coding and relating
of concepts is key to this process and requires a
more refined system than a set of manila folders.
In this section, we’ll assume that you’ll be
doing your coding manually. A later section of
the chapter will illustrate the use of computer
programs for qualitative data analysis.
Coding Units
As you may recall from the earlier discussion
of content analysis, for statistical analysis it’s
important to identify a standardized unit of
analysis prior to coding. If you were comparing
American and French novels, for example, you
might evaluate and code sentences, paragraphs,
chapters, or whole books. It would be important,
however, to code the same units for each
novel analyzed. This uniformity is necessary in
a quantitative analysis, as it allows us to report
something like “23 percent of the paragraphs
contained metaphors.” This is only possible if
we’ve coded the same unit—paragraphs—in
each of the novels.
Coding data for a qualitative analysis, however,
is quite different. The concept is the organizing
principle for qualitative coding. Here the
units of text appropriate for coding will vary
within a given document. Thus, in a study of
organizations, “Size” might require only a few
words per coding unit, whereas “Mission” might
take a few pages. Or, a lengthy description of a
heated stockholders meeting might be coded as
“Internal Dissent.”
Realize also that a given code category may
be applied to textual materials of quite different
lengths. For example, some references to the
organization’s mission may be brief, others
lengthy. Whereas standardization is a key principle
in quantitative analysis, this is not the case
in qualitative analysis.
Coding as a Physical Act
Before continuing with the logic of coding, let’s
take a moment to see what it actually looks like.
Lofland and his colleagues offer this description
of manual filing:
Prior to the widespread availability of personal
computers beginning in the late 1980s,
coding frequently took the specific physical
form of filing. The researcher established an
expanding set of file folders with code names
on the tabs and physically placed either the
item of data itself or a note that referenced
its location in another file folder. Before
photocopying was easily available and cheap,
some fieldworkers typed their fieldnotes with
carbon paper, wrote codes in the margins of
the copies of the notes, and cut them up with
scissors. They then placed the resulting slips
of paper in corresponding file folders.
(2006: 203)
As Lofland and his colleagues point out, personal
computers have greatly simplified this task.
However, the image of slips of paper that contain
text and are put in folders representing code categories
is useful for understanding the process of
coding. In the next section, when I suggest that
we code a textual passage with a certain code,
imagine that we have the passage typed on a slip
of paper and that we place it in a file folder bearing
the name of the code. Whenever we assign
two codes to a passage, imagine placing duplicate
copies of the passage in two different folders
representing the two codes.
Creating Codes
So, what should your code categories be? Glaser
and Strauss (1967: 101f) allow for the possibility
of coding data for the purpose of testing hypotheses
that have been generated by prior theory. In
that case, then, the codes would be suggested by
the theory, in the form of variables.
In this section, however, we’re going to focus
on the more common processes of open coding,
axial coding, and selective coding. Strauss
and Corbin (1998: 102) describe open coding as
follows:
open coding  The initial classification and labeling
of concepts in qualitative data analysis. In open
coding, the codes are suggested by the researchers’
examination and questioning of the data.
Qualitative Data Processing ■ 389
To uncover, name, and develop concepts,
we must open up the text and expose the
thoughts, ideas, and meanings contained
therein. Without this first analytic step, the
rest of the analysis and the communication
that follows could not occur. Broadly speaking,
during open coding, data are broken
down into discrete parts, closely examined,
and compared for similarities and differences.
Events, happenings, objects, and actions/
interactions that are found to be conceptually
similar in nature or related in meaning
are grouped under more abstract concepts
termed categories.
Although the analysis of data will quickly
advance to an iterative interplay of the three
types of coding, open coding is the logical starting
point. Beginning with some body of text
(part of an interview, for example), you read and
reread a passage, seeking to identify the key concepts
contained within it. Any particular piece
of data may be given several codes, reflecting
as many concepts. For example, notice all the
concepts contained in this comment by a student
interviewee:
I thought the professor should have given
me at least partial credit for the homework
I turned in.
Some obvious codes are “Professor,” “Homework,”
and “Grading.” The result of open coding
is the identification of numerous concepts
relevant to the subject under study. The open
coding of more and more text will lengthen the
list of codes.
Axial coding aims to identify the core concepts
in the study. Although axial coding uses
the results of open coding, more concepts can
be identified through continued open coding
after the axial coding has begun. Axial coding
involves a regrouping of the data, in which the
researcher uses the open-code categories and
looks for more-analytic concepts. For example,
the passage just given also carries the concept of
“perceptions of fairness,” which might appear
frequently in the student interviews, thereby
suggesting that it’s an important element in
understanding students’ concerns. Another axial
code reflected in the student comment might be
“power relationships,” because the professor is
seen to exercise power over the student.
Selective coding seeks to identify the central
code in the study: the one that the other
codes all relate to. Both of the axial codes just
mentioned might be restructured as aspects
of a more general concept: “professor–student
relationships.” Of course, in a real data analysis,
decisions such as the ones we’ve been discussing
would arise from masses of textual data,
not from a single quotation. The basic notion of
the Grounded Theory Method is that patterns
of relationships can be teased out of an extensive,
in-depth examination of a large body of
observations.
Here’s a concrete example to illustrate how
you might engage in this form of analysis. Suppose
you’re interested in the religious bases for
homophobia. You’ve interviewed some people
opposed to homosexuality who cite a religious
basis for their feelings. Specifically, they refer
you to these passages in the Book of Leviticus
(Revised Standard Version):
18:22	 You shall not lie with a male as with a
woman; it is an abomination.
20:13	 If a man lies with a male as with a
woman, both of them have committed
an abomination; they shall be put to
death, their blood is upon them.
Although the point of view expressed here
seems unambiguous, you might decide to examine
it in more depth. Perhaps a qualitative analysis
of Leviticus can yield a fuller understanding
of where these injunctions against homosexuality
fit into the larger context of Judeo-Christian
morality.
Let’s start our analysis by examining the two
passages just quoted. We might begin by coding
each passage with the label “Homosexuality.”
This is clearly a key concept in our analysis.
Whenever we focus on the issue of homosexuality
in our analysis of Leviticus, we want to
consider these two passages.
axial coding  A reanalysis of the results of open
coding in the Grounded Theory Method, aimed at
identifying the important, general concepts.
selective coding  In Grounded Method Theory,
this analysis builds on the results of open coding
and axial coding to identify the central concept
that organizes the other concepts that have been
identified in a body of textual materials.
390 ■ Chapter 13: Qualitative Data Analysis
Because homosexuality is such a key concept,
let’s look more closely into what it means within
the data under study. We first notice the way
homosexuality is identified: a man lying with a man
“as with a woman.” Although we can imagine a
lawyer seeking admission to heaven saying, “But
here’s my point; if we didn’t actually lie down . . .”
it seems safe to assume the passage refers to
having sex, though what specific acts might or
might not be included isn’t clear.
Notice, however, that the injunctions appear
to concern male homosexuality only; lesbianism
is not mentioned. In our analysis, then, each
of these passages might also be coded “Male
Homosexuality.” This illustrates two more aspects
of coding: (1) Each unit can have more than one
code and (2) hierarchical codes (one included
within another) can be used. Now each passage
has two codes assigned to it.
An even more general code might be introduced
at this point: “Prohibited Behavior.” This is
important for two reasons. First, homosexuality
is not inherently wrong, from an analytic standpoint.
The purpose of the study is to examine
the way it’s made wrong by the religious texts
in question. Second, our study of Leviticus may
turn up other behaviors that are prohibited.
There are at least two more critical concepts
in the passages: “Abomination” and “Put to
Death.” Notice that although these are clearly
related to “Prohibited Behavior,” they are hardly
the same. Parking without putting money in
the meter is prohibited, but few would call it
an abomination and fewer still would demand
the death penalty for that transgression. Let’s
assign these two new codes to our first two
passages.
At this point, we want to branch out from
the two key passages and examine the rest of
Leviticus. We therefore examine and code each
of the remaining chapters and verses. In our
subsequent analyses, we’ll use the codes we
have already and add new ones as appropriate.
When we do add new codes, it will be important
to review the passages already coded to see
whether the new codes apply to any of them.
Here are the passages we decide to code
“Abomination.” (I’ve boldfaced the abominations.)
7:18	 If any of the flesh of the sacrifice of
his peace offering is eaten on the
third day, he who offers it shall not be
accepted, neither shall it be credited to
him; it shall be an abomination, and he
who eats of it shall bear his iniquity.
7:21	 And if any one touches an unclean
thing, whether the uncleanness of man
or an unclean beast or any unclean
abomination, and then eats of the
flesh of the sacrifice of the LORD’s
peace offerings, that person shall be cut
off from his people.
11:10	But anything in the seas or the rivers
that has not fins and scales, of the
swarming creatures in the waters and
of the living creatures that are in the
waters, is an abomination to you.
11:11	 They shall remain an abomination to
you; of their flesh you shall not eat,
and their carcasses you shall have in
abomination.
11:12	 Everything in the waters that has
not fins and scales is an abomination
to you.
11:13	 And these you shall have in abomination
among the birds, they shall not
be eaten, they are an abomination: the
eagle, the vulture, the osprey,
11:14	the kite, the falcon according to its
kind,
11:15	every raven according to its kind,
11:16	the ostrich, the nighthawk, the sea
gull, the hawk according to its kind,
11:17	the owl, the cormorant, the ibis,
11:18	the water hen, the pelican, the carrion
vulture,
11:19	the stork, the heron according to its
kind, the hoopoe, and the bat.
11:20	 All winged insects that go upon all
fours are an abomination to you.
11:41	 Every swarming thing that swarms
upon the earth is an abomination; it
shall not be eaten.
11:42	 Whatever goes on its belly, and whatever
goes on all fours, or whatever has many
feet, all the swarming things that
swarm upon the earth, you shall not eat;
for they are an abomination.
11:43	 You shall not make yourselves abominable
with any swarming thing that
swarms; and you shall not defile
Qualitative Data Processing ■ 391
yourselves with them, lest you become
unclean.
18:22	 You shall not lie with a male as with a
woman; it is an abomination.
19:6	 It shall be eaten the same day you offer
it, or on the morrow; and anything left
over until the third day shall be burned
with fire.
19:7	 If it is eaten at all on the third day,
it is an abomination; it will not be
accepted.
19:8	 and every one who eats it shall bear his
iniquity, because he has profaned a holy
thing of the LORD; and that person shall
be cut off from his people.
20:13	 If a man lies with a male as with a
woman, both of them have committed
an abomination; they shall be put to
death, their blood is upon them.
20:25	 You shall therefore make a distinction
between the clean beast and the unclean,
and between the unclean bird and the
clean; you shall not make yourselves
abominable by beast or by bird or
by anything with which the ground
teems, which I have set apart for you to
hold unclean.
Male homosexuality, then, isn’t the only
abomination identified in Leviticus. As you compare
these passages, looking for similarities and
differences, it will become apparent that most
of the abominations have to do with dietary
rules—specifically those potential foods deemed
“unclean.” Other abominations flow from the
mishandling of ritual sacrifices. “Dietary Rules”
and “Ritual Sacrifices” thus represent additional
codes to be used in our analysis.
Earlier, I mentioned the death penalty as
another concept to be explored in our analysis.
When we take this avenue, we discover that
many behaviors besides male homosexuality
warrant the death penalty. Among them are
these:
20:2	 Giving your children to Molek (human
sacrifice)
20:9	 Cursing your father or mother
20:10	 Adultery with your neighbor’s wife
20:11	 Adultery with your father’s wife
20:12	 Adultery with your daughter-in-law
20:14	 Taking a wife and her mother also
20:15	 Men having sex with animals (the
animals are to be killed, also)
20:16	 Women having sex with animals
20:27	 Being a medium or wizard
24:16	 Blaspheming the name of the Lord
24:17	 Killing a man
As you can see, the death penalty is broadly
applied in Leviticus: everything from swearing
to murder, including male homosexuality somewhere
in between.
An extended analysis of prohibited behavior,
short of abomination and death, also turns up a
lengthy list. Among them are slander, vengeance,
grudges, cursing the deaf, and putting stumbling
blocks in front of blind people. In chapter 19,
verse 19, Leviticus quotes God as ordering, “You
shall not let your cattle breed with a different
kind; you shall not sow your field with two
kinds of seed; nor shall there come upon you
a garment of cloth made of two kinds of stuff.”
Shortly thereafter, he adds, “You shall not eat
any flesh with the blood in it. You shall not practice
augury or witchcraft. You shall not round
off the hair on your temples or mar the edges
of your beard.” Tattoos were prohibited, though
Leviticus is silent on body piercing. References to
all of these practices would be coded “Prohibited
Acts” and perhaps given additional codes as well
(recall “Dietary Rules”).
I hope this brief glimpse into a possible
analysis will give you some idea of the process
by which codes are generated and applied. You
should also have begun to see how such coding
would allow you to better understand the messages
being put forward in a text and to retrieve
data appropriately as you need them.
Memoing
In the Grounded Theory Method, the coding
process involves more than simply categorizing
chunks of text. As you code data, you should
also be using the technique of memoing—writing
memoing  Writing memos that become part of
the data for analysis in qualitative research such
as grounded theory. Memos can describe and
define concepts, deal with methodological issues,
or offer initial theoretical formulations.
392 ■ Chapter 13: Qualitative Data Analysis
memos or notes to yourself and others involved
in the project. Some of what you write during
analysis may end up in your final report; much
of it will at least stimulate what you write.
In GTM, these memos have a special
significance. Strauss and Corbin (1998: 217)
distinguish three kinds of memos: code notes,
theoretical notes, and operational notes.
Code notes identify the code labels and their
meanings. This is particularly important because,
as in all social science research, most of the terms
we use with technical meanings also have meanings
in everyday language. It is essential, therefore,
to write down a clear account of what you
mean by the codes used in your analysis. In the
Leviticus analysis, for example, you would want
a code note regarding the meaning of “abomination”
and how you’ve used that code in your
analysis of text.
Theoretical notes cover a variety of topics:
reflections of the dimensions and deeper meanings
of concepts, relationships among concepts,
theoretical propositions, and so on. All of us
have ruminated over the nature of something,
trying to think it out, to make sense out of it. In
qualitative data analysis, it’s vital to write down
these thoughts, even those you’ll later discard as
useless. They will vary greatly in length, though
you should limit them to a single main thought
so that you can sort and organize them later. In
the Leviticus analysis, one theoretical note might
discuss the way that most of the injunctions
implicitly address the behavior of men, with
women being mostly incidental.
Operational notes deal primarily with methodological
issues. Some will draw attention
to data-collection circumstances that may be
relevant to understanding the data later on.
Others will consist of notes directing future data
collection.
Writing these memos occurs throughout the
data-collection and analysis process. Thoughts
demanding memos will come to you as you
reread notes or transcripts, code chunks of text,
or discuss the project with others. It’s a good idea
to get in the habit of writing out your memos as
soon as possible after the thoughts come to you.
Notice that whereas we often think of writing
as a linear process, starting at the beginning and
moving through to the conclusion, memoing is
very different. It might be characterized as a process
of creating chaos and then finding order within it.
To explore this process further, you can refer
to the works cited in this discussion. You’ll also
find a good deal of information on the web. Ultimately,
the best education in this process comes
from practice. Even if you don’t have a research
project underway, you can practice now on class
notes. Or start a journal and code it.
Concept Mapping
It should be clear by now that qualitative data
analysts spend a lot of time committing thoughts
to paper (or to a computer file), but this process
is not limited to text alone. Often, we can think
out relationships among concepts more clearly
by putting the concepts in a graphic format, a
process called concept mapping. Some researchers
put all their major concepts on a single sheet
of paper, whereas others spread their thoughts
across several sheets of paper, blackboards,
magnetic boards, computer pages, or other
media. Figure 13-3 shows how we might think
out some of the concepts of Goffman’s examination
of gender and advertising. (This image
was created through the use of Inspiration, a
concept-mapping computer program.)
Incidentally, many of the topics discussed
in this section have useful applications in
concept mapping  The graphic display of concepts
and their interrelations, useful in the formulation
of theory.
Power
Servant/
master
Authority
Social
worth
Social
status
Gender
Physical
location
Active/
passive
roles
FIGURE 13-3
An Example of Concept Mapping
© Cengage Learning®
Computer Programs for Qualitative Data ■ 393
quantitative as well as qualitative analyses.
Certainly, concept mapping is appropriate in both
types of analysis. The several types of memos
would also be useful in both. And the discussion
of coding readily applies to the coding of openended
questionnaire responses for the purpose of
quantification and statistical analysis. (We’ll look
at coding again in the next chapter, on quantifying
data.)
The advent of computers had an immediate
impact on the analysis of quantitative data, because
of their strength in the realm of statistical
computations. As we will see, computers have
been powerfully adapted to the analysis of qualitative
data as well.
Computer Programs
for Qualitative Data
Not so many decades ago, the recording and
analysis of qualitative research data was
paperbound: interview transcripts, observational
notes, official documents, and the like were
all compiled and evaluated by hand. The work
of writing documents by hand was eventually
streamlined by the advent of typewriters. Copying
technologies, first carbon paper and later,
photocopying, made it possible to easily reproduce
duplicate pages of research information.
These printed data could then be cut into slips of
paper, with each strip displaying an individual,
coded item. This procedure allowed researchers
to categorize collected data according to different
themes or concepts. Recalling the discussion of
Leviticus, one pile of paper slips might contain
passages referencing homosexuality, another
pile could contain references to abominations,
and so forth. And, as our earlier discussion mentioned,
a given passage might show up in more
than one pile. Once this coding and sorting was
done, researchers could manually review all the
materials within a particular category, allowing
them to look for and identify common patterns
and important distinctions.
As you can imagine, computers changed
all that. Once information has been entered
into a computer, copying whole documents or
pieces is a trivial matter. Simulating the earlier
use of paper documents, you can copy an interview
comment relevant to, say, discrimination
against women, and paste it into another document
created to hold anything relevant to that
theme. With nothing but the simplest word
processor or text editor, you can streamline the
coding process in any number of ways. Imagine
this paragraph was part of textual materials
you were analyzing in a study of technological
progress. You could augment this document by
adding coding notations such as: <computer>
<qualitative> <coding>.
You could go through this whole chapter,
adding these and other notations wherever
appropriate. And once you were finished, you
could use a simple “Search” function to review
all the materials marked <coding>, for example.
You could augment this process by searching the
chapter for words such as code, coding, category,
classify, or other applicable terms.
I’d like these brief paragraphs to draw attention
and honor to those researchers who could
make sense out of social life with little more than
paper and pencil. And I hope that a review of
past practices will demonstrate what a powerful
tool the simplest, bare-bones computer can represent
for the analysis of qualitative data.
Now, let’s turn 180 degrees from these simple
computer manipulations. As you will soon see,
those methods may seem like crude tools from
the Dark Ages of social research. We are now
going to spend some time with a few of the computer
programs devised precisely for the analysis
of qualitative data.
QDA Programs
Today, qualitative data analysis (QDA) programs
abound. Where the analyst’s problem used to be
merely finding any such program, the problem
now lies in choosing one of so many. Here are a
few commonly used QDA programs with online
sites where you can learn more about them and,
in many cases, download demo copies.
●● AnSWR: www.cdc.gov/hiv/software/answr
.htm
●● Atlas.ti: www.atlasti.com/index.php
●● Ethno: www.indiana.edu/%7Esocpsy/ESA/
●● Ethnograph: www.qualisresearch.com/
●● HyperResearch: www.researchware.com/
●● HyperTranscribe: www.researchware.com/
●● MAXQDA: www.maxqda.com/
394 ■ Chapter 13: Qualitative Data Analysis
●● NVivo: www.qsrinternational.com
/products_nvivo.aspx
●● QDA Miner: www.provalisresearch.com
/QDAMiner/QDAMinerDesc.html
●● Qualrus: www.qualrus.com/
●● TAMS: sourceforge.net/projects/tamsys
●● Weft: www.pressure.to/qda/
Another excellent resource is “Choosing a
CAQDAS Package” by Ann Lewins and Christina
Silver (2006). This will familiarize you with some
of the key features in such computer programs
and help you choose which one is best suited to
your purposes.
We’ll turn now to a couple of illustrations of
QDA programs at work. Although the available
programs differ somewhat from one another, I
think these illustrations will give you a good sense
of the general use of computers to analyze qualitative
data. We’ll begin by returning to the earlier
example of the Book of Leviticus, with its sexual
and other prohibitions. Let’s see how a computer
analysis might assist the search for patterns.
Leviticus as Seen through Qualrus
We’ll first consider one of the programs just
listed: Qualrus. Although the text to be coded
can be typed directly into Qualrus, usually materials
already in existence—such as field notes or,
in this case, the verses of Leviticus—are imported
into the program. For purposes of this illustration,
I simply copied chapter 20 of Leviticus
from the web and pasted it into the document
section of the layout.
Figure 13-4 shows how the text is presented
within Qualrus. As you can see, although the
text inserted cannot all be displayed in the
window at once, you can easily scroll up and
down through the document.
One of the themes we discussed earlier concerned
the various actions that could result in
death, so let’s create that as a code in Qualrus.
This is most easily done by highlighting it with
our cursor to select a passage we wish to code:
verse 2, for example. Then click the “Code” button
at the top of the page, and you will be presented
with another window for creating codes,
as illustrated in Figure 13-5.
Click the top, leftmost icon in the window
and you will be asked to enter a new code in the
lower left portion of the window, where I have
typed “death.” This is the first in what will become
a list of codes. Notice also that the software has
automatically entered the code “death” in the
upper right portion of the window, indicating
the designated code for this particular passage.
Once you have decided on and inserted additional
codes into the program, you will be able to
select from that list and click the arrow to assign
codes to the passage being coded. Once you’ve
assigned the proper code, click “OK” to return to
the document.
Having created the code, we can proceed
through the text, identifying all the passages
that specify the death penalty for some action.
Figure 13-6 illustrates what this coding looks like.
As you begin building your coding structure,
Qualrus begins to anticipate and suggest appropriate
codes for some of the passages you have
selected. Figure 13-7 gives an example of this.
Qualrus has examined a passage we’ve selected
and has guessed that perhaps it should be coded
“death.”
The suggested code appears in the upper-left
panel of the window. If you choose to accept that
suggestion, you must select the code and transfer
it to the upper-right panel.
Notice in the Figure 13-7 illustration I have
added several additional codes, which might be
useful in our analysis of Leviticus. I’ve provided
for passages that mention adultery, bestiality,
homosexuality, incest, the occult, and sex.
In addition, in my review of the document,
I noticed some passages specified that God
would punish the sinners, while others indicated
the community should mete out the punishment.
Let’s see what the document looks like
when all these codes have been employed. See
Figure 13-8.
As you add more codes to your analysis,
the display on the right side of the figure may
become very complex. However, programs
like Qualrus help you make sense of it all.
Moreover, as you add more structure to your
coding scheme, Qualrus can ease the process. For
example, you can tell the program that adultery,
bestiality, incest, and homosexuality are subsets
of the code “sex.” Once you have informed
Qualrus of that, every time you code a passage
for “adultery,” for example, it will automatically
code that passage as “sex,” also.
Computer Programs for Qualitative Data ■ 395
FIGURE 13-4
LeviticusText Displayed in Qualrus
© 2012 The Idea Works, Inc.
The purpose of all this coding is to let you
bring together all the instances of a particular
topic. For example, let’s review all the actions
that can result in death. Click the “QTools” button
and you will find yourself in a new window,
illustrated in Figure 13-9. I have instructed
Qualrus to locate all the passages that were
coded “death.”
By scrolling up and down through the
main panel that contains the passages, you can
review all the relevant passages and look for
patterns. Let’s say you are particularly interested
in cases where sexual behavior warrants death.
Figure 13-10 illustrates that search. Now we
have a shorter, more focused set of passages
to study.
This has been a brief look into some of the
ways you could use the Qualrus program to
analyze the prohibitions detailed in Book of
Leviticus. I have not attempted to offer a theoretical
framework for your analysis, I merely want
to illustrate the most elementary techniques that
396 ■ Chapter 13: Qualitative Data Analysis
FIGURE 13-5
Creating a Code for Death
© 2012 The Idea Works, Inc.
could be used in your research into the topic.
Moreover, as you can see at the top of the last
two figures, the QTools that are available go
well beyond the reviewing of one or two codes.
To learn more about the additional analytic
tools, you should visit the Qualrus web page
www.qualrus.com/.
At this point, I will shift to another of the
listed QDA programs. Although NUD*IST was
one of the first such programs to be popularly
employed by social researchers, it has evolved
through many forms over time. At this writing,
QSR International’s latest tool is NVivo 9, and it
is the program we will discuss next.
NVivo
To get started in our introduction to this program
for the analysis of qualitative data, Figure 13-11
shows a sample of how NVivo 9 might display
the data we saw earlier in Figure 13-8, when
we were analyzing the Book of Leviticus with
Qualrus. NVivo refers to established codes as
nodes.
While the formats of different programs vary,
of course, the fundamental process of coding and
analyzing the coded data is the bedrock of all
QDA programs. However, we are going to shift
gears at this point.
Thus far, I have limited discussion to the
coding of a written document, because it offers
a clear example of coding. Written documents
are hardly the materials appropriate to such an
analysis. You’ve probably already anticipated
that these programs would be especially valuable
for the analysis of in-depth interviews, and you
would be right. At this point, I want to make use
of a research example used to introduce NVivo
on their website.
“Environmental Change Down East” is a sample
project provided with NVivo 9 that explores
environmental change in a coastal area of North
Carolina. Part of the research for this project
involved qualitative interviews with residents of
the communities in the area. Figure 13-12 shows
a coded portion of one such interview.
Notice the colored coding strips to the right
of the interview transcript. The red strip, for
example, represents the node, “Jobs and cost of
living.” If you read the passages marked with red
strips, you can see why the researchers coded
those passages as they did. Note also that a given
passage may reflect more than one of the themes
being examined, and so it has been assigned
more than one node.
Thus far, we have seen that QDA programs
can be used to organize documents and interview
transcripts in preparation for analysis.
Computer Programs for Qualitative Data ■ 397
FIGURE 13-6
Displaying the Coding of“Death”Passages
© 2012 The Idea Works, Inc.
That’s only the beginning, however. Whereas
the Down East project we are now examining
contains the interviews of a number of residents,
the researchers also collected data about physical
locations in the study area, such as communities.
Figure 13-13 illustrates some of those data.
In a record such as this one, the textual
material and/or the photograph (or a portion
of the photograph) can be coded by assigning
nodes. Later on, a review of materials relating
to a particular theme will bring together the
relevant interviews, documents, photographs,
and anything else that has been assigned the
node in question.
Visual data for analysis are not limited to
photographs. Perhaps your project has collected
some of its data in a video format. Figure 13-14
illustrates this possibility.
Once the video files have been imported into
the NVivo project, they can be coded, retrieved,
and played within the program. Audio recording
can, of course, be treated similarly.
398 ■ Chapter 13: Qualitative Data Analysis
FIGURE 13-7
Suggesting a Code
© 2012 The Idea Works, Inc.
In these few illustrations, I have intended
to give you an idea of the variety of data that
can be amassed and organized within NVivo. If
you have used a bibliographic program such as
EndNote, you can import those materials into
the project as well. Your own imagination will
expand the list of possibilities further.
I’ll conclude this discussion by touching on
one further element that we mentioned in our
earlier discussions of qualitative research: memos.
At every turn, NVivo allows you to record your
own notes in the context of other materials—
assigning those memos to the appropriate nodes.
As you begin to recognize a certain pattern emerging
in the intersection of two themes, for example,
you can jot down notes about what you’ve
observed so that you don’t forget the details.
In the discussion of Qualrus, we saw a couple
of examples of preliminary analyses. You’ll
recall that we retrieved all the passages from
Leviticus that dealt with the death penalty; then
we searched for passages prescribing death for
sexual behavior. NVivo offers a variety of analytical
tools under the heading of “Queries.” The
NVivo “Getting Started Guide” is available as a
pdf on the QSR website. This guide offers some
examples of queries posed in the Down East
project:
●● Gather material coded at combinations
of nodes—for example, gather content
coded at water quality and recreational fishing
and explore the associations.
●● Gather material from classified nodes
with specific attribute values—for
example, what do fishermen think about the
rise of tourism?
●● Search for content coded at multiple
nodes and use operators to further refine
the query—for example, gather content
coded at community change where it
overlaps with content coded at real estate
development.
●● Search for content that is not coded at a
specific node—find content coded at environmental
impacts but not coded at negative
attitude.
(QSR 2011: 31)
Today, QDA programs can also provide for far
more sophisticated displays of analytical results.
Figure 13-15, for example, offers a summary of
positive, negative, and mixed comments made
about commercial fishing in the several Down
East communities.
This bar graph might make you curious about
the positive attitudes toward commercial fishing
among residents of Harkers Island. Using QDA
software, it would be a simple matter to retrieve
those specific comments in full.
The QDA programs also offer a graphic presentation
of your coding structure, supporting
the development of a coherent, theoretical
understanding of the subject under study. See
Figure 13-16 for an example.
I hope this brief overview of two QDA programs,
Qualrus and NVivo, will give you a good
idea of the powerful tools available to qualitative
data analysts. Please realize that I have only
selected a few of the features available in the
The Qualitative Analysis of Quantitative Data ■ 399
FIGURE 13-8
Display of Many Codes
© 2012 The Idea Works, Inc.
two programs I’ve illustrated here, and also realize
that the other programs I listed earlier in the
chapter have many of the same, or similar, capabilities.
Most programs provide comprehensive
tutorials online, so you can explore, compare,
and decide for yourself which program fits your
needs.
Finally, you should be warned that such programs
may be more expensive than the average
student budget can easily accommodate. However,
you may find that your school has licensed
copies available for your use.
The Qualitative Analysis
of Quantitative Data
Figure 13-17 presents FBI data on homicides
committed in the United States. These data are
often presented in a tabular form, but notice
how clearly the patterns of crime appear in this
three-dimensional graph. Even though the graph
is based on statistical data, it conveys its meaning
quite clearly. Although summarizing it in
the form of equations may be useful for certain
400 ■ Chapter 13: Qualitative Data Analysis
FIGURE 13-9
Reviewing All the Passages Coded“Death”
© 2012 The Idea Works, Inc.
The Qualitative Analysis of Quantitative Data ■ 401
FIGURE 13-10
Reviewing the Passages Coded“Death”and“Sex”
© 2012 The Idea Works, Inc.
402 ■ Chapter 13: Qualitative Data Analysis
FIGURE 13-11
Looking at Leviticus with NVivo 9
Courtesy of Nvivo
purposes, it would add nothing to the clarity of
the picture itself. Thus, the qualitative assessment
of the graph clarifies the quantitative data
in a way that no other representation could.
Here’s a case where a picture is truly worth a
thousand words, or perhaps numbers.
With an even more-striking example of how
to understand quantitative data qualitatively,
Mark Newman at the University of Michigan begins
with a map showing 2012 U.S. presidential
election results. You’ve probably seen something
like this before. (See Figure 13-18.)
The general pattern shows the Democrats
winning the coastal and Northeast states, and the
Republicans winning the Midwest and South.
In terms of the simple number of states, it
looks as though the Republican, Governor Mitt
Romney, surely must have beaten the Democrat,
then Senator Barack Obama. When Newman
adjusts the map to take account of population—
stretching or shrinking states to reflect their
population (and Electoral College votes)—the
Democratic dominance becomes more apparent.
(See Figure 13-19.)
Evaluating the Quality of Qualitative Research ■ 403
In the extended analysis on his website,
Newman also shifts his unit of analysis from
states to counties for a different look, and he
goes beyond the dichotomous blue/red labeling
of states and counties to use purple for those
where the results were close.
Evaluating the Quality
of Qualitative Research
As you’ve seen in earlier chapters, there are
often clear guidelines for evaluating the quality
of quantitative research. In the case of survey
research, for example, we can note the size of
the sample, the manner in which it was selected,
and the completion rate achieved. The questionnaire
items are standardized and open to scrutiny.
And as you’ll see in Chapters 14 and 16,
researchers can use statistical tests to assess
quantitative research findings.
Judging the quality of qualitative research
is more elusive, though no less important.
Because there are many different forms of
qualitative research, we’ll examine some fairly
general guidelines you can use to distinguish
first-rate qualitative investigations from those
not so well done.
In Chapter 5, we looked at two aspects of
measurement quality: validity and reliability.
That’s a reasonable way to start our look at
assessing qualitative research.
Validity, you’ll recall, involves the question
of whether you’re measuring what you say
you’re measuring. Remember, most of the things
social scientists measure are products of human
thought and agreement, not things that exist
independently of human judgment. Prejudice,
for example, isn’t real the way age or weight
are. Nonetheless, we’ve all observed behaviors
and orientations that we’ve gathered under the
umbrella concept of “prejudice.” To a degree, we
FIGURE 13-12
A Coded Interview in NVivo 9
Courtesy of Nvivo
404 ■ Chapter 13: Qualitative Data Analysis
FIGURE 13-13
Data about a Physical Location
Courtesy of QSR International
FIGURE 13-14
UsingVideo Data with NVivo 9
Courtesy of QSR International
Evaluating the Quality of Qualitative Research ■ 405
FIGURE 13-15
Attitudes toward Commercial Fishing by Community
Courtesy of QSR International
mean the same general thing when we use the
term, and we also have a lot of differences in
that regard.
When you design a survey questionnaire
to measure prejudice, it’s important to assess the
extent to which the questions asked and answers
received actually reflect what we can agree to
mean by the term. The same logic applies in
qualitative research projects such as field observations
or historical studies. If field researchers
characterize a subject of observation as “prejudiced,”
you should examine their basis for saying
that. Qualitative researchers, more than quantitative
researchers, pay special attention to understanding
life as the participants see it, so you
may find the researchers in this case reporting
that those who knew the subject in question also
mentioned that he or she was prejudiced.
Some qualitative researchers prefer to use
the term credibility in the place of validity in this
context. This is done as a caution against the
older, positivistic view that social concepts represent
real phenomena that exist objectively and
independently of human thought. Be warned,
however, that some researchers use the term
with other meanings that fall quite far from that
of validity. Also, in this textbook, my use of the
term validity explicitly denies objective reality for
the concepts we use and study.
Reliability is also a reasonable criterion of
quality with the regard to qualitative research,
though it needs to be applied appropriately.
Recall this is a question of whether a measurement
or observation technique would yield the
same data if it were possible to measure or observe
the same thing several times independently. In
the case of categorizing raw data, such as data
that in-depth interviews or even the open-ended
answers to survey questions might produce, we
can ask more than one person to undertake the
coding or categorizing process independently and
see if they all produce the same results. In most
aspects of social research, however, the concept
of reliability is more elusive, because (1) what
we are observing may be constantly changing
and/or (2) the act of measuring (for example,
asking a question) may affect the person being
studied. Still, the basic concept of reliability,
which some qualitative researchers prefer to
call dependability, is meaningful for qualitative
406 ■ Chapter 13: Qualitative Data Analysis
FIGURE 13-16
Mapping Down East Concepts
Courtesy of QSR International
800
700
600
500
400
300
200
100
Number
Victim age
Offenderage
0 20
40
60
0
20
40
60
80
80
100 100
FIGURE 13-17
Number of One-on-One Homicides by Age ofVictim and Age
of Offender, Raw Data
Source: Michael D. Maltz. 1998.“Visualizing Homicide: A Research Note.”Journal
ofQuantitativeCriminology 15 (4): 401.
research. Yvonna Lincoln and Egon Guba (1985),
for example, proposed an “inquiry audit” for
the purpose of assessing the consistency of both
what was observed and the process by which it
was observed.
Follow-up works by the same authors laid
out several ways in which qualitative research
could be assessed. Building on this foundation,
several other researchers offered somewhat
modified schemes for both assessing qualitative
research and increasing its quality. A more recent
effort, by Britain’s National Centre for Social Research,
sought to assist cabinet-level officials in
assessing qualitative research projects that evaluated
government programs. Although the study
focused on the use of qualitative methods for
purposes of evaluation research, the 18 questions
that organized such assessments can be applied
to most forms of qualitative research (Spencer
et al. 2003: 22–28).
Evaluating the Quality of Qualitative Research ■ 407
FIGURE 13-18
Red and Blue States in the 2012 Presidential Election
Source: Figure by Mark Newman, used with permission, http://www-personal.umich.edu/~mejn/election/2012/.
© Cengage Learning®
1.	 How credible are the findings?
2.	 How has knowledge or understanding
been extended by the research?
3.	 How well does the evaluation address its
original aims and purpose?
4.	 How well is the scope for drawing wider
inference explained?
5.	 How clear is the basis of evaluative
appraisal?
6.	 How defensible is the research design?
7.	 How well-defended are the same design/
target selection of cases/documents?
8.	 How well is the eventual sample
composition and coverage described?
9.	 How well was the data collection carried
out?
10.	 How well has the approach to, and
formulation of, analysis been conveyed?
11.	 How well are the contexts of data sources
retained and portrayed?
12.	 How well has diversity of perspective and
content been explored?
13.	 How well has detail, depth and complexity
(i.e., richness) of the data been conveyed?
14.	 How clear are the links between data,
interpretation and conclusions—i.e., how
well can the route to any conclusions be
seen?
15.	 How clear and coherent is the reporting?
16.	 How clear are the assumptions/theoretical
perspectives/values that have shaped the
form and output of the evaluation?
17.	 What evidence is there of attention to
ethical issues?
18.	 How adequately has the research process
been documented?
The attempt to settle on criteria for evaluating
qualitative social research is far from over. For
example, some researchers are wary of the
British effort just delineated: They express concern
about the implications of a government
body specifying research criteria and suggest that
the list grows out of philosophical and political
orientations that have not been made clear
(J. Smith and Hodkinson 2005).
408 ■ Chapter 13: Qualitative Data Analysis
Ethics and Qualitative
Data Analysis
At least two ethical issues cause special concern
in the analysis and reporting of qualitative
research. First, because it calls so directly on
subjective judgments, researchers face an obvious
risk of seeing what they are looking for or
want to find. The risk is increased in the case of
participatory action research or other projects
involving an element of social justice. Researcher
bias is hardly inevitable, however. Experienced
qualitative analysts avoid this pitfall through a
deliberate awareness of their own values and
preferences as well as adherence to established
techniques for data collection and analysis. And
as an additional protection, the peer review inherent
in the scientific research environment
encourages colleagues to point out any failings in
this regard.
Second, protecting subjects’ privacy becomes
a particularly important issue in qualitative
research. The qualitative researcher will often
analyze and report data collected from specific,
identifiable individuals. Earlier, I indicated the
importance of not revealing what we learn about
subjects, though I mostly discussed it in the
context of data collection. When writing up the
results of your analyses, you will often have to
actively conceal identities. Individuals, organizations,
and communities are given pseudonyms
to conceal their identities. Sometimes, you may
even need to suppress details that would let
outsiders figure out who you are talking about.
Thus, it may be appropriate to speak about interviewing
“a church leader” rather than “the head
deacon.” And you may need to suppress or alter
age, race, or gender references if any would give
away a subject’s identity. The key principle is to
respect the privacy of those we study.
FIGURE 13-19
Adjusting State Sizes to Reflect Population
Source: Figure by Mark Newman, used with permission, http://www-personal.umich.edu/~mejn/election/2012/.
© Cengage Learning®
Review Questions and Exercises ■ 409
Main Points
Introduction
●● Qualitative analysis is the nonnumerical examination
and interpretation of observations.
Linking Theory and Analysis
●● Qualitative analysis involves a continual interplay
between theory and analysis. In analyzing
qualitative data, we seek to discover patterns
such as changes over time or possible causal
links among variables.
●● Examples of approaches to the discovery and
explanation of such patterns are Grounded
Theory Method (GTM), semiotics, and conversation
analysis.
Qualitative Data Processing
●● The processing of qualitative data is as much art
as science. Three key tools for preparing data
for analysis are coding, memoing, and concept
mapping.
●● In contrast to the standardized units used in
coding for statistical analyses, the units to be
coded in qualitative analyses may vary within
a document. Although codes may be derived
from the theory being explored, more often
researchers use open coding, in which codes are
suggested by the researchers’ examination and
questioning of the data.
●● Memoing is appropriate at several stages of data
processing to capture code meanings, theoretical
ideas, preliminary conclusions, and other
thoughts that will be useful during analysis.
●● Concept mapping uses diagrams to graphically
explore relationships in the data.
Computer Programs for Qualitative Data
●● Many computer programs, such as Qualrus and
NVivo, are designed specifically to assist researchers
in the analysis of qualitative data. In addition,
researchers can take advantage of the capabilities
of common software tools such as word processors,
database programs, and spreadsheets.
The Qualitative Analysis of Quantitative Data
●● Although qualitative and quantitative methods
of analysis may appear incompatible or in
competition, research often demands that both
kinds be used in the same project.
Evaluating the Quality of Qualitative Research
●● Validity (credibility) and reliability (dependability)
are reasonable criteria for evaluating
qualitative research.
Ethics and Qualitative Data Analysis
●● The subjective element in qualitative data analysis
provides an added challenge to avoiding
bias in the interpretation of data.
●● Since the qualitative data analyst will know
the identity of subjects, protecting their privacy
requires special care.
Key Terms
The following terms are defined in context in the
chapter and at the bottom of the page where the
term is introduced, as well as in the comprehensive
glossary at the back of the book.
axial coding
case-oriented analysis
concept mapping
constant comparative
 method
conversation analysis (CA)
cross-case analysis
Grounded Theory
  Method (GTM)
memoing
open coding
qualitative analysis
selective coding
semiotics
variable-oriented
 analysis
Proposing Social Research:
Qualitative Data Analysis
In this chapter, we’ve seen some of the qualitative
data analysis approaches that social researchers can
use. Since you won’t have analyzed your data when
you write this portion of the proposal, of course,
you can’t say anything about the conclusions you’ll
draw. However, you can describe your initial plans
for the analysis. I say “initial” plans because you
may change directions somewhat as the data accumulate
and patterns begin to emerge. In some cases,
your analysis will begin as observations are being
made and/or other data being gathered, or you may
plan to complete the data-collection phase before
starting your data analysis.
This the place to indicate whether you plan to
employ a particular method of analysis, such as
grounded theory, semiotics, or conversational analysis.
If you’re planning to use one of the computer
programs used for qualitative data analysis, mention
that here as well.
Review Questions and Exercises
1.	 Review Goffman’s examination of gender
advertising, then collect and analyze a set of
advertising photos, from magazines, newspapers,
or the web, that allow you to explore the
relationship between gender and status.
2.	 Review the discussion of homosexuality in the
Book of Leviticus and suggest ways that the
410 ■ Chapter 13: Qualitative Data Analysis
examination might be structured as a cross-case
analysis.
3.	 Imagine you were conducting a cross-case
analysis of revolutionary documents such as the
Declaration of Independence and the Declaration
of the Rights of Man and of the Citizen
(from the French Revolution). Identify the
key concepts you might code in the following
sentence:
When in the Course of human events, it
becomes necessary for one people to dissolve
the political bands which have connected
them with another, and to assume among
the Powers of the earth, the separate and
equal station to which the Laws of Nature
and of Nature’s God entitle them, a decent
respect to the opinions of mankind requires
that they should declare the causes which
impel them to the separation.
4.	 Write one code note and one theoretical note
for Exercise 3.
5.	 Using the library or the web, find a research
report using conversation analysis. Summarize
the main conclusions in your own words.