Please see the videoVideo on Codinghttpswww.youtube.com.docx

Please see the video
Video on Coding:
https://www.youtube.com/watch?v=DRL4PF2u9XA
DIRECTIONS
The first step of the EBP process is to develop a question from
the nursing practice problem of interest.
Select a practice problem of interest to use as the focus of your
research.
Start with the patient and identify the clinical problems or
issues that arise from clinical care.
Following the PICOT format, write a PICOT statement in your
selected practice problem area of interest, which is applicable to
your proposed capstone project.
The PICOT statement will provide a framework for your
capstone project (the project students must complete during
their final course in the RN-BSN program of study).
Conduct a literature search to locate research articles focused
on your selected practice problem of interest. This literature
search should include both quantitative and qualitative peer-
reviewed research articles to support your practice problem.
Select six peer-reviewed research articles which will be utilized
through the next 5 weeks as reference sources. Be sure that
some of the articles use qualitative research and that some use
quantitative research. Create a reference list in which the six
articles are listed. Beneath each reference include the article's
abstract. The completed assignment should have a title page and
a reference list with abstracts.
Suggestions for locating qualitative and quantitative research
articles from credible sources:

1. Use a library database such as CINAHL Complete for your
search.
2. Using the advanced search page check the box beside
"Research Article" in the "Limit Your Results" section.
3. When setting up the search you can type your topic in the top
box, then add quantitative or qualitative as a search term in one
of the lower boxes. Research articles often are described as
qualitative or quantitative.
To narrow/broaden your search, remove the words qualitative
and quantitative and include words that narrow or broaden your
main topic. For example: Diabetes and pediatric and dialysis.
To determine what research design was used, review the
abstract and the methods section of the article. The author will
provide a description of data collection using qualitative or
quantitative methods.
Prepare this assignment according to the guidelines found in the
APA Style Guide, located in the Student Success Center. An
abstract is not required.
This assignment uses a rubric. Please review the rubric prior to
beginning the assignment to become familiar with the
expectations for successful completion.
RUBRIC
PICOT Statement and Literature Search
1
Unsatisfactory
0.00%
2
Less than Satisfactory
75.00%
3
Satisfactory
83.00%

4
Good
94.00%
5
Excellent
100.00%
80.0 %Content
20.0 %Nursing Practice Problem and PICOT Statement
A nursing practice problem is not clearly described and/or a
PICOT statement is not included.
PICOT statement describes a nursing practice problem but lacks
reliable sources.
PICOT statement describes a nursing practice problem and
includes a few reliable sources.
PICOT statement articulates a nursing practice problem using
supporting information from reliable sources.
PICOT statement clearly articulates a nursing practice problem
using substantial supporting information from numerous reliable
sources.
60.0 %Qualitative and Quantitative Research Requirements: At
least six references with the abstracts of research studies focus
on a selected nursing practice problem.
Research articles that address the selected nursing practice
problem are not identified.
Fewer than six articles that address a selected nursing practice
problem are listed in the literature search, but two or more are
not research studies. Abstracts are incomplete.
Six articles that address a selected nursing practice problem are
listed in the literature search, but two are not research studies.
Abstracts are partially complete.
listed in the literature search, but one is not a research study.
Abstracts are mostly complete.

listed in the literature search and include abstracts.
10.0 %Organization and Effectiveness
10.0 %Mechanics of Writing (includes spelling, punctuation,
grammar, language use)
Surface errors are pervasive enough that they impede
communication of meaning. Inappropriate word choice or
sentence construction is used.
Frequent and repetitive mechanical errors distract the reader.
Inconsistencies in language choice (register), sentence
structure, or word choice are present.
Some mechanical errors or typos are present, but they are not
overly distracting to the reader. Correct sentence structure and
audience-appropriate language are used.
Prose is largely free of mechanical errors, although a few may
be present. A variety of sentence structures and effective
figures of speech are used.
Writer is clearly in command of standard, written, academic
English.
10.0 %Format
10.0 %Documentation of Sources (citations, footnotes,
references, bibliography, etc., as appropriate to assignment and
style)
Sources are not documented.
Documentation of sources is inconsistent or incorrect, as
appropriate to assignment and style, with numerous formatting
errors.
Sources are documented, as appropriate to assignment and style,
although some formatting errors may be present.
Sources are documented, as appropriate to assignment and style,
and format is mostly correct.
Sources are completely and correctly documented, as
appropriate to assignment and style, and format is free of error.

100 %Total Weightage
Paula Thompson
To assist you with preparing the Week 7 assignment, I am
providing a few additional resources. Attached is a slide deck I
created on coding textual data, and below are links to a video on
coding and an article on identifying themes in qualitative data.
Also, some learners find the Week 8 lecture helpful to this
assignment, so I have attached it here as well.
As you prepare this assignment, closely follow the directions in
the PSY 850 Assignments Document. Because it asks you to
inductively code the data, I will expect to see that each of you
have developed your own codes and themes. That means do not
use the codes and themes from the Clark and Springer (2007)
article. However, you should compare and contrast your
findings with theirs in the recommendations section.
Submit one paper in APA format with the required subsections
delineated in the Assignments Guide: Introduction, Sample,
Instruments, Data Analysis, Results, and Recommendations.
Include a references list. You must complete all three tables in
the Tables for Assignment 7 document and include those tables
as an Appendix in your document. Do not submit them as a
separate document nor embed them within the text of the paper.
Please use this space to ask additional questions about the
assignment.

Thanks and happy coding,
Paula
Video on Coding:
https://www.youtube.com/watch?v=DRL4PF2u9XA
GCU Recommended article on Techniques to Identify Themes in
Qualitative Data
http://www.analytictech.com/mb870/Readings/ryan-
bernard_techniques_to_identify_themes_in.htm
Attached Files
PSY-850-L8.pdf
Coding Textual Data.pptx
Techniques to Identify Themes in Qualitative Data
Gery W. Ryan
RAND
1700 Main Street
P.O. Box 2138
Santa Monica, CA 90407-2138
H. Russell Bernard
Department of Anthropology
1350 Turlington Hall
University of Florida
Gaineville, FL 32611
Key Words: Theme Identification, Exploratory Analysis, Open
Coding, Text Analysis, Qualitative Research Methods
Abstract
Theme identification is one of the most fundamental tasks in
qualitative research. It also one of the most mysterious. Explicit
descriptions of theme discovery are rarely described in articles
and reports and if so are often regulated to appendices or
footnotes. Techniques are shared among small groups of social
scientists and are often impeded by disciplinary or

epistemological boundaries. During the proposal-writing phase
of a project, investigators struggle to clearly explain and justify
plans for discovering themes. These issues are particularly
cogent when funding reviewers are unfamiliar with qualitative
traditions. In this article we have outlined a dozen techniques
that social scientists have used to discover themes in texts. The
techniques are drawn from across epistemological and
disciplinary boundaries. They range from quick word counts to
laborious, in-depth, line-by-line scrutiny. Some methods work
well for short answers to open-ended questions while others are
more appropriate for rich, complex narratives. Novices and non-
native speakers may find some techniques easier than others. No
single technique is does it all. To us, these techniques are
simply tools to help us do better research.
Authors’ Statement
Gery W. Ryan is an Associate Behavioral Scientist at RAND in
Santa Monica, California. H. Russell Bernard is professor of
anthropology at the University of Florida. The research on
which this article is based is part of a National Science
Foundation Grant, on "Methods for Conducting Systematic Text
Analysis" (SRB-9811166). We wish to thank Stephen Borgatti
for his helpful suggestions and two anonymous reviewers for
their invaluable comments on earlier drafts of this paper.
Introduction
At the heart of qualitative data analysis is the task of
discovering themes. By themes, we mean abstract, often fuzzy,
constructs which investigators identify before, during, and after
data collection. Where do these themes come from?
They come from reviewing the literature, of course. Richer
literatures produce more themes. They come from the
characteristics of the phenomena being studied. And they come
from already-agreed-upon professional definitions, from local
common-sense constructs, and from researchers’ values,
theoretical orientation, and personal experience with the subject
matter (Bulmer 1979; Strauss 1987; Maxwell 1996).

Mostly, though, researchers who consider themselves part of the
qualitative tradition in social science induce themes from texts.
This is what grounded theorists call open coding, and what
classic content analysts call qualitative analysis (Berleson
1952) or latent coding (Shapiro and Markoff 1997). There are
many variations on these methods. Unfortunately, however, they
are (a) scattered across journals and books that are read by
disparate groups of specialists; and (b) often entangled in the
epistemological wars that have divided the social sciences. Our
goal in this paper is to cross these boundaries and lay out a
variety of theme-dredging methods so that all researchers who
deal with texts can use them to solve common research
problems.
We outline here a dozen helpful techniques for discovering
themes in texts. These techniques are based on: (1) an analysis
of words (word repetitions, key-indigenous terms, and key-
words-in contexts); (2) a careful reading of larger blocks of
texts (compare and contrast, social science queries, and
searching for missing information); (3) an intentional analysis
of linguistic features (metaphors, transitions, connectors); and
(4) the physical manipulation of texts (unmarked texts, pawing,
and cut and sort procedures).
The list is by no means exhaustive. Social scientists are an
enterprising lot. Over the last century they have invented
solutions to all kinds of problems for managing and analyzing
texts, and they will continue to do so. These bursts of
methodological creativity, however, are commonly described
perfunctorily, or are relegated to footnotes, and get little notice
by colleagues across disciplines. The dozen methods we
describe here come from across the social sciences and have
been used by positivists and interpretivists alike.
1. Word repetitions
We begin with word-based techniques. Word repetitions, key-
indigenous terms, and key-words-in-contexts (KWIC) all draw
on a simple observation—if you want to understand what people
are talking about, look at the words they use.

Words that occur a lot are often seen as being salient in the
minds of respondents. D'Andrade notes that "perhaps the
simplest and most direct indication of schematic organization in
naturalistic discourse is the repetition of associative linkages"
(1991:294). He observes that "indeed, anyone who has listened
to long stretches of talk, whether generated by a friend, spouse,
workmate, informant, or patient, knows how frequently people
circle through the same network of ideas" (1991:287).
Word repetitions can be analyzed formally and informally. In
the informal mode, investigators simply read the text and note
words or synonyms that people use a lot. For example, while
conducting multiple in-depth interviews with Tony, a retired
blue collar worker in Connecticut, Claudia Strauss (1992) found
that Tony repeatedly referred to ideas associated with greed,
money, businessmen, siblings, and "being different." These
repetitions indicated to Strauss that these ideas were important,
recurring themes in Tony’s life. Strauss displayed the
relationships among these ideas by writing the concepts on a
page of paper and connecting them with lines and explanations.
Computer programs such as ATLAS.ti and Nud*ist let you do
this kind of connect-the-dots exercise by computer.1
A more formal analysis of word frequencies can be done by
generating a list of all the unique words in a text and counting
the number of times each occurs. Computers can easily generate
word-frequency lists from texts and are a quick and easy way to
look for themes. Ryan and Weisner (1996) asked fathers and
mothers of adolescents: "Describe your children. In your own
words, just tell us about them." Ryan and Weisner produced a
list of all the unique words in the set of responses and the
number of times each word was used by mothers and by fathers.
Mothers were more likely than fathers to use words like friends,
creative, time, and honest; fathers were more likely than
mothers to use words like school, good, lack, student, enjoys,
independent, and extremely. Ryan and Weisner used this
information as clues for themes that they would use later in
actually coding the texts.

2. Indigenous categories
Another way to find themes is to look for local terms that may
sound unfamiliar or are used in unfamiliar ways. Patton
(1990:306, 393-400) refers to these as "indigenous categories"
and contrasts them with "analyst-constructed typologies."
Grounded theorist refer to the process of identifying local terms
as in vivo coding (Strauss 1987:28-32, Strauss and Corbin
1990:61-74).
Understanding indigenous categories and how they are
organized has long been a goal of cognitive anthropologists.
The basic idea in this area of research is that experience and
expertise are often marked by specialized vocabulary. For
example, Spradley (1972) recorded conversations among tramps
at informal gatherings, meals, card games, and bull sessions. As
the men talked to each other about their experiences, there were
many references to making a flop.
Spradley combed through his recorded material and notes
looking for verbatim statements made by informants about his
topic. On analyzing the statements, he found that most of the
statements could fit into subcategories such as kinds of flops,
ways to make flops, ways to make your own flop, kinds of
people who bother you when you flop, ways to make a bed, and
kinds of beds. Spradley then returned to his informants and
sought additional information from them on each of the
subcategories. For other classic examples of coding for
indigenous categories see Becker’s (1993) description of
medical students use of the word crock, and Agar’s (1973)
description of drug addicts’ understandings of what it means to
shoot up.
3. Key-words-in-context (KWIC)
Key-words-in-context (KWIC) are closely associated with
indigenous categories. KWIC is based on a simple observation:
if you want to understand a concept, then look at how it is used.
In this technique, researchers identify key words and then
systematically search the corpus of text to find all instances of
the word or phrase. Each time they find a word, they make a

copy of it and its immediate context. Themes get identified by
physically sorting the examples into piles of similar meaning.
The concept of deconstruction is an abstract and often
incomprehensible term used by social scientists, literary critics
and writers in the popular press. Jacques Derrida, who coined
the term, refused to define it. To Derrida, the meaning of any
text is inherently unstable and variable. Wiener (1997) was
curious as to how the concept of deconstruction was used in the
popular press. He used a text-based data set (such as
Lexis/Nexis), to find instances of the word in popular
publications. He found the term used in by everything from
Entertainment Weekly to the American Banker. Wiener
concludes that:
Most often writers use "deconstruction" as a fancy word for
"analysis" or "explanation," or else as an upscale synonym for
"destruction." But in some genres, like rock music writing, the
term isn't negative at all; it has become a genuinely floating
signifier, a verbal gesture that implies a kind of empty
intellectual sophistication.
Word-based techniques are typically a fast and efficient ways to
start looking for themes. We find that they are particularly
useful at early stages of theme identification. These techniques
are also easy for novice researchers to apply. Nothing, however,
beats a careful scrutiny of the texts for finding themes that may
be more subtle or that don’t get signified directly in the lexicon
of the text. Scrutiny-based techniques are more time-intensive
and require a lot of attention to details and nuances.
4. Compare and contrast
The compare and contrast approach is based on the idea that
themes represent the ways in which texts are either similar or
different from each other. Glazer and Strauss (1967:101_116)
refer to this as the "constant comparison method." [For other
good descriptions of the technique see Glazer (1978:56_72) and
Strauss and Corbin (1990:84_95).] Typically, grounded theorists
begin by conducting a careful line-by-line analysis. They read
each line or sentence and ask themselves, "What is this about?"

and "How does it differ from the preceding or following
statements?" This kind of detailed work keeps the researcher
focused on the data themselves rather than on theoretical flights
of fancy (Charmaz 1990).
This approach is like interviewing the text and is remarkably
similar to the ethnographic interviewing style that Spradley
talks about using with his informants (1979:160_172).
Researchers compare pairs of texts by asking "How is this text
different from the preceding text?" and "What kinds of things
are mentioned in both?" They ask hypothetical questions like
"What if the informant who produced this text had been a
woman instead of a man?" and "How similar is this text to my
own experiences?" Bogdan and Biklen (1982:153) recommend
reading through passages of text and asking "What does this
remind me of?" Like a good journalist, investigators compare
answers to questions across people, space, and time.
5. Social science queries
Besides identifying indigenous themes—themes that
characterize the experience of informants—researchers are
interested in understanding how textual data illuminate
questions of importance to social science. Spradley (1979:199–
201) suggested searching interviews for evidence of social
conflict, cultural contradictions, informal methods of social
control, things that people do in managing impersonal social
relationships, methods by which people acquire and maintain
achieved and ascribed status, and information about how people
solve problems. Bogdan & Bilken (1982:156-162) suggested
examining the setting and context, the perspectives of the
informants, and informants’ ways of thinking about people,
objects, processes, activities, events, and relationships.
"Moving across substantive areas," says Charmaz, "fosters
developing conceptual power, depth, and comprehensiveness"
(1990:1163).
Strauss and Corbin (1990:158_175) urge investigators to be
more sensitive to conditions, actions/interactions, and
consequences of a phenomenon and to order these conditions

and consequences into theories. To facilitate this, they offer a
useful tool called the conditional matrix. The conditional matrix
is a set of concentric circles, each level corresponding to a
different unit of influence. At the center are actions and
interactions. The inner rings represent individual and small
group influences on these actions, and the outer rings represent
international and national effects.
Querying the text as a social scientist is a powerful technique
because investigators concentrate their efforts on searching for
specific kinds of topics – any of which are likely to generate
major social and cultural themes. By examining the data from a
more theoretical perspective, however, researchers must be
careful that they do not overfit the data – that is, find only that
for which they are looking. There is a trade-off between
bringing a lot of prior theorizing to the theme-identification
effort and going at it fresh. Prior theorizing, as Charmaz says
(1990), can inhibit the forming of fresh ideas and the making of
surprising connections. Assiduous theory-avoidance brings the
risk of not making the connection between data and important
research questions. Novice researchers may be more
comfortable with the tabula rasa approach. More seasoned
researchers, who are more familiar with theory issues, may find
the social science query approach more compatible with their
interests.
6. Searching for missing information
The final scrutiny-based approach we describe works in reverse
from typical theme identification techniques. Instead of
identifying themes that emerge from the text, investigators
search for themes that are missing in the text.
Much can be learned from a text by what is not mentioned. As
early as 1959, propaganda analysts found that material not
covered in political speeches were sometimes more predictive
that material that was covered (George 1959). Sometimes
silences indicate areas that people are unwilling or afraid to
discuss. For instance, women with strong religious convictions
may fail to mention abortion during discussions of birth control.

In power-laden interviewers, silence may be tied to implicit or
explicit domination (Gal 1991). In a study of birth planning in
China, Greenhalgh (1994) surveyed 1,011ever-married women,
gathered social and economic histories from 150 families. She
conducted in-depth interviews with present and formal officials
(known as cadres), and collected documentary evidence from
local newspapers, journals and other sources. Greenhalgh notes
that "Because I was largely constrained from asking direct
questions about resistance, the informal record of field notes,
interview transcripts, and questionnaire data contains few overt
challenges to state policy (1994:9)." Greenhalgh concludes,
however, that
I believe that in their conversations with us, both peasants and
cadres made strategic use of silence to protest aspects of the
policy they did not like. Cadres, for example were loathe to
comment on birth-planning campaigns; peasant women were
reluctant to talk about sterilization. These silences form one
part of the unofficial record of birth planning in the villages.
More explicit protests were registered in informal
conversations. From these interactions emerged a sense of
profound distress of villagers forced to choose between a
resistance that was politically risky and a compliance that
violated the norms of Chinese culture and of practical reason
(1994:9).
Other times, absences may indicate primal assumptions made by
respondents. Spradley (1987:314) noted that when people tell
stories, they assume that their listeners share many assumptions
about how the world works and so they leave out information
that "everyone knows." He called this process abbreviating.
Price (1987) takes this observation and builds on it. Thus, she
looks for what is not said in order to identify underlying
cultural assumptions. Price finds the missing pieces by trying to
translate what people say in the stories into something that the
general public would understand.
Of all the scrutiny-based techniques, searching for missing
information is the most difficult. There are many reasons people

do not mention topics. In addition to avoiding sensitive issues
or assuming investigator already knows about the topic, people
may not trust the interviewer, may not wish to speak when
others are present, or may not understand the investigator’s
questions. Distinguishing between when informants are
unwilling to discuss topics and when they assume the
investigator already knows about the topic requires a lot of
familiarity with the subject matter.
In addition to word- and scrutiny-based techniques, researchers
have used linguistic features such as metaphors, topical
transitions, and keyword connectors to help identify themes.
7. Metaphors and analogies
Schema analysts suggest searching through text for metaphors,
similes, and analogies (D’Andrade 1995, Quinn and Strauss
1997). The emphasis on metaphor owes much to the pioneering
work by Lakoff and Johnson (1980) and the observation that
people often represent their thoughts, behaviors, and
experiences with analogies.
Naomi Quinn (1997) has analyzed hundreds of hours of
interviews to discover concepts underlying American marriage
and to show how these concepts are tied together. She began by
looking at patterns of speech and at the repetition of key words
and phrases, paying particular attention to informants' use of
metaphors and the commonalities in their reasoning about
marriage. Nan, one of her informants, says that "marriage is a
manufactured product." This popular metaphor indicates that
Nan sees marriages as something that has properties, like
strength and staying power, and as something that requires work
to produce. Some marriages are "put together well," while
others "fall apart" like so many cars or toys or washing
machines (Quinn 1987:174).
The object is to look for metaphors in rhetoric and deduce the
schemas, or underlying principles, that might produce patterns
in those metaphors. Quinn found that people talk about their
surprise at the breakup of a marriage by saying that they
thought the couple’s marriage was "like the Rock of Gibraltar"

or that they thought the marriage had been "nailed in cement."
People use these metaphors because they assume that their
listeners know that cement and the Rock of Gibraltar are things
that last forever.
But Quinn reasons that if schemas or scripts are what make it
possible for people to fill in around the bare bones of a
metaphor, then the metaphors must be surface phenomena and
cannot themselves be the basis for shared understanding. Quinn
found that the hundreds of metaphors in her corpus of texts fit
into just eight linked classes that she calls: lastingness,
sharedness, compatibility, mutual benefit, difficulty, effort,
success (or failure), and risk of failure. For example, Quinn’s
informants often compared marriages (their own and those of
others) to manufactured and durable products ("it was put
together pretty good") and to journeys ("we made it up as we
went along; it was a sort of do-it-yourself project"). Quinn sees
these metaphors, as well as references to marriage as "a lifetime
proposition," as exemplars of the overall expectation of
lastingness in marriage.
Other examples of the search for cultural schemas in texts
include Holland’s (1985) study of the reasoning that Americans
apply to interpersonal problems, Kempton’s (1987) study of
ordinary Americans’ theories of home heat control, and Claudia
Strauss’s (1997) study of what chemical plant workers and their
neighbors think about the free enterprise system.
8. Transitions
Another linguistic approach is to look for naturally occurring
shifts in thematic content. Linguistic forms of transition vary
between oral and written texts. In written texts, new paragraphs
are often used by authors to indicate either subtle or abrupt
shifts in topics. In oral speech, pauses, change in tone, or
particular phrases may indicate thematic transitions. Linguists
who have worked with precisely recorded texts in Native
American languages have noticed the recurrence of elements
like "Now," "Then," "Now then," and "Now again." These often
signal the separation of verses and "once such patterning has

been discovered in cases with such markers, it can be discerned
in cases without them" (Hymes 1977:439).
For example, Sherzer (1994) presents a detailed analysis of a
two-hour performance by Chief Olopinikwa of a traditional San
Blas Kuna chant. The chant was recorded in 1970. Like many
linguistic anthropologists, Sherzer had taught an assistant,
Alberto Campos, to use a phonetic transcription system. After
the chant, Sherzer asked Campos, to transcribe and translate the
tape. Campos put Kuna and Spanish on left- and right-facing
pages (1994:907). By studying Campos’s translation against the
original Kuna, Sherzer was able to pick out certain recurrent
features. Campos left out the chanted utterances of the
responding chief (usually something like "so it is"), which
turned out to be markers for verse endings in the chant. Campos
also left out so-called framing words and phrases (like "Thus"
at the beginning of a verse and "it is said, so I pronounce" at the
end of a verse). These contribute to the line and verse structure
of the chant. Finally, "instead of transposing metaphors and
other figurative and allusive language into Spanish" Campos
"explains them in his translation" (Sherzer 1994:908).
Researchers
In two-party and multiparty speech, transitions occur naturally.
Conversation or discourse analysts closely examine linguistic
features such as turn-taking and speaker interruptions to
identify transitions in speech sequences. For a good overview,
see Silverman (1993:114-143).
9. Connectors
A third linguistic approach is to look carefully at words and
phrases that indicate relationships among things. For example,
causal relationships are often indicated by such words and
phrases as, because, since, and as a result. Words such as if or
then, rather than, and instead of often signify conditional
relationships. The phrase is a is often associated with taxonomic
categories. Time-oriented relationships are expressed with
words such as before, after, then, and next. Typically negative
characteristics occur less often than positive characteristics.

Simply searching for the words not, no, none, or the prefix non
may be a quick way to identify themes. Investigator can
discover themes by searching on such groups of word and
looking to see what kinds of things the words connect.
What other kinds of relationships might be of interest to social
scientists? Casagrande and Hale (1967) suggest looking for:
attributes (e.g., X is Y), contingencies (e.g., if X, then Y),
functions (e.g., X is a means of affecting Y), spatial
orientations (e.g., X is close to Y), operational definitions (e.g.,
X is a tool for doing Y), examples (e.g., X is an instance of Y),
comparisons (e.g., X resembles Y), class inclusions (X is a
member of class Y), synonyms (e.g., X is equivalent to Y),
antonyms (e.g., X is the negation of Y), provenience (e.g., X is
the source of Y), and circularity (e.g., X is defined as X). [For
lists of kinds of relationships that may be useful for identifying
themes see Burton and Kirk (1980:271), Werner and Schoepfle
(1987) and Lindsay and Norman (1972).]
Investigators often use the linguistic features described above
unconsciously. Metaphors, transitions, and connectors are all
part of a native speaker’s ability to grasp meaning in a text. By
making these features more explicit, we sharpen our ability to
find themes.
Finally, we turn to more tactile approaches for theme discovery.
Each of the next three techniques requires some physical
manipulation of the text itself.
10. Unmarked texts
One way to identify new themes is to examine any text that is
not already associated with a theme (Ryan 1999). This
technique requires multiple readings of a text. On the first
reading, salient themes are clearly visible and can be quickly
and readily marked with different colored pencils or
highlighters. In the next stage, the search is for themes that
remain unmarked. This tactic–marking obvious themes early and
quickly—forces the search for new, and less obtrusive themes.
11. Pawing
We highly recommend pawing through texts and marking them

up with different colored highlighter pens. Sandelowski
(1995a:373) observes that analysis of texts begins with
proofreading the material and simply underlining key phrases
"because they make some as yet inchoate sense." Bernard
(2000) refers to this as the ocular scan method, otherwise
known as eyeballing. In this method, you get a feel for the text
by handling your data multiple times. [Bogdan and Biklen
(1982:165) suggest reading over the text at least twice.]
Researchers have been known to spread their texts out on the
floor, tack bunches of them to a bulletin board, and sort them
into different file folders. By living with the data, investigators
can eventually perform the interocular percussion test—which is
where you wait for patterns to hit you between the eyes.
This may not seem like a very scientific way to do things, but it
is one of the best ways we know of to begin hunting for patterns
in qualitative data. Once you have a feel for the themes and the
relations among, then we see no reason to struggle bravely on
without a computer. Of course, a computer is required from the
onset if the project involves hundreds of interviews, or if it’s
part of a multi-site, multi-investigator effort. Even then, there is
no substitute for following hunches and intuitions in looking for
themes to code in texts (Dey 1993).
12. Cutting and sorting
Cutting and sorting is a more formal way of pawing and a
technique we both use quite a bit. It is particularly useful for
identifying subthemes. The approach is based on a powerful
trick most of us learned in kindergarten and requires paper and
scissors. We first read through the text and identify quotes that
seem somehow important. We cut out each quote (making sure
to maintain some of the context in which it occurred) and paste
the material on small index cards. On the back of each card, we
then write down the quote’s reference—who said it and where it
appeared in the text. Then we lay out the quotes randomly on a
big table and sort them into piles of similar quotes. Then we
name each pile. These are the themes. This can be done with tag
and search software, but we find that nothing beats the ability to

manually sort and group the cards.
There are many variations on this pile-sorting technique. The
principle investigator on a large project might ask several team
members to sort the quotes into named piles independently. This
is likely to generate a longer list of possible themes than would
be produced by a group discussion. In really large projects,
pairs of coders could sort the quotes together and decide on the
names for the piles. The pile-sorting exercise should be video-
or audiotaped and investigators should pay close attention to
discussions—between themselves and coders or between
coders—about which quotes belong together and why. These
conversations are about as close as we will ever get to
witnessing the emergence of themes.
Barkin et al. (1999) interviewed clinicians, community leaders,
and parents about what physicians could and did do to prevent
violence among youth. These were long, complex interviews, so
Barkin et al. broke the coding process into two steps. They
started with three major themes that they developed from
theory. The principle investigator went through the transcripts
and cut out all the quotes that pertained to each of the major
themes. Then four other coders independently sorted the quotes
from each major theme into piles. Then, the pile sort data were
analyzed with multidimensional scaling and cluster analysis to
identify subthemes shared across coders. [See Patterson et al.
(1993) for another example.]
Jehn and Doucet (1997) had short answers to open-ended
questions. They found that several coders could easily sort these
paragraph-length descriptions of inter and intra-ethnic conflict.
Then, like Barkin et al., Jehn and Doucet then used
multidimensional scaling and cluster analysis to identify
subthemes of conflict.
Another advantage to the cutting and sorting technique is that
the data can be used to systematically describe how such themes
are distributed across informants. After the piles have been
formed and themes have been named, simply turn over each
quote and identify who mentioned each theme. (If the people

sorting the quotes are unaware of who the quotes came from,
this is an unbiased way of coding.)
Discussion
The variety of methods available for coding texts raises some
obvious questions:
(1) Which technique generates more themes?
Frankly, we don’t know. There are just too many factors that
influence the number of themes that are generated, including the
technique itself, who and how many people are looking for
themes, and the kind and amount of texts being analyzed. If the
goal is to generate as many themes as possible—which is often
the case in initial exploratory phases of research—then more is
better. This means using multiple techniques, investigators, and
texts.
Nowhere is a multiple technique approach better exemplified
than in the work of Jehn and Doucet (1996, 1997). Jehn and
Doucet asked 76 U.S. managers who had worked in
Sino_American joint ventures to describe recent interpersonal
conflicts with business partners. Each person described a
situation with a same_culture manager and a different_cultural
manger. First they generated separate lists of words from the
intercultural and intracultural conflict narratives. They asked 3
expatriate managers to act as judges and to identify all the
words that were related to conflict. They settled on a list of 542
conflict words from the intercultural list and 242 words from
the intracultural list.
Jehn and Doucet then asked the three judges to sort the words
into piles or categories. The experts identified 15 subcategories
for the intercultural data—things like conflict, expectations,
rules, power, and volatile—and 15 categories for the
intracultural data—things like conflict, needs, standards, power,
contentious, and lose. Taking into consideration the total
number of words in each corpus, conflict words were used more
in intracultural interviews and resolution terms were more
likely to be used in intercultural interviews.
Jehn and Doucet (1996, 1997) also used traditional content

analysis on their data. The had two coders read the 152 conflict
scenarios (76 intracultural and 76 intercultural) and evaluated
(on a 5_point scale) each on 27 different themes they had
identified from the literature. This produced two 76x27
scenario_by_theme profile matrices—one for the intracultural
conflicts and one for the intercultural conflicts. The first three
factors from the intercultural matrix reflect: (1) interpersonal
animosity and hostility; (2) aggravation; and (3) the volatile
nature of the conflict. The first two factors from the
intracultural matrix reflect: (1) hatred and animosity with a
volatile nature and (2) conflicts conducted calmly with little
verbal intensity.
Finally, Jehn and Doucet identified the 30 intracultural and the
30 intercultural scenarios that they felt were the most clear and
pithy. They recruited fifty more expatriate managers to assess
the similarities (on a 5_point scale) of 60–120 randomly
selected pairs of scenarios. When combined across informants,
the managers judgments produced two aggregate,
scenario_by_scenario, similarity matrices—one for the
intracultural conflicts and one for the intercultural conflicts.
Multidimensional scaling of the intercultural similarity data
identified four dimensions: (1) open versus resistant to change,
(2) situational causes versus individual traits, (3) high_ versus
low_resolution potential based on trust, and (4) high_ versus
low_resolution potential based on patience. Scaling of the
intracultural similarity data identified four different
dimensions: (1) high versus low cooperation, (2) high versus
low confrontation, (3) problem_solving versus accepting, and
(4) resolved versus ongoing.
The work of Jehn and Doucet is impressive because the analysis
of the data from these tasks produced different sets of themes.
All three emically induced theme sets have some intuitive
appeal and all three yield analytic results that are useful. They
could have also used the techniques of grounded theory or
schema analysis to discover even more themes.
(2) When are the various techniques most appropriate?

The choice of techniques depends minimally on the kind and
amount of text, the experience of the researcher, and the goals
of the project. Word-based techniques (e.g., word repetitions,
indigenous categories, and KWIC) are probably the least labor
intensive. Computer software such as Anthropac and Code-a-
text have little trouble in generating frequency counts of key
words.2 A careful look at the frequency list and maybe some
quick pile sorts are often enough to identify quite a few themes.
Word-based techniques are also the most versatile. They can
easily be used with complex texts such as the complete works of
Shakespear or the Bible, as well as, with simple short answers
to open-ended questions. They can also be used relatively easily
by novice and expert investigators alike. Given their very
nature, however, they are best used in combination with other
approaches.
Scrutiny-based techniques (e.g., compare and contrast, querying
the text, and examining absences) are most appropriate for rich
textual accounts and tend to be overkill for analyzing short
answer responses. Investigators who are just beginning to
explore a new topical area might want to start with compare-
and-contrast techniques before moving on to the more difficult
tasks of querying the text or searching for missing information.
We do not advise using the latter two techniques unless the
investigator is fluent in the language in which the data are
collected. If the primary goal of the this portion of the
investigation is to discover as many themes as possible, then
nothing beats using these techniques on a line-by-line basis.
Like scrutiny-based techniques, linguist-based approaches are
better used on narrative style accounts rather than short answer
responses. Looking for transitions is the easiest technique to
use, especially if the texts are actually written by respondents
themselves (rather than transcribed from tape recordings of
verbal interviews). Searching for metaphors is also relatively
easy once novices have been trained on what kind of things to
look for in the texts. Looking for connecting words and phrases
is best used as a secondary wave of finding themes, once the

investigator has a more definite idea of what kinds of themes he
or she finds most interesting.
In the early stages of exploration, nothing beats a thorough
reading and pawing through of the data. This approach is the
easiest for novice researchers to master and is particularly good
for identifying major themes. As the exploration progresses,
investigators often find themselves looking for subthemes
within these major themes. The cutting and sorting techniques
are most helpful here. Investigators can identify all text
passages that are related to a major theme, cut them out, and
sort them into subthematic categories. Likewise, if they are
marking texts for each newly discovered theme, then they can
apply the unmarked text technique as they go. We have seen
these three techniques applied successfully to both rich
narrative data as well as simple responses to open-ended
questions.
An even more powerful strategy would be to combine multiple
techniques in a sequential manner. For example, investigators
might begin by pawing through the data to see what kinds of
themes just stick out. As part of this process, they might want to
make comparisons between paragraphs and across informants. A
quick analysis of word repetitions would also be appropriate for
identifying themes at such an early stage of the analysis. If key
words or indigenous phrases are present, researchers might
followed-up by conducting more focused KWIC analyses. If the
project is examining issues of equality, investigators might also
look for texts that are indicative of power differentials and
access to resources. Texts representing major themes can be
marked either on paper or by computer. Investigators can then
search areas that are not already marked for additional themes
or cut and sort marked texts into subthemes.
Researchers also might consider beginning by looking for
identifying all metaphors and similes, marking them, cutting
them out and sorting them into thematic categories. There is no
single way to discover themes. In theme discovery, we assume
that more is always better.

(3) When do you know when you’ve found all the themes?
There is no magic formula to answer this question. The problem
is similar to asking members of a population to list all the
illnesses they know. One can never be sure of the full range of
illnesses without interviewing the entire population. This is true
because there is always the possibility that the last person
interviewed will mention a new disease. We can simplify the
process considerably, however, if we are willing to miss rarely-
mentioned illness. One strategy would be to interview people
until some number of respondents in a row (say five or more)
fail to mention any new illnesses.
In text analysis, grounded theorists refer to the point at which
no new themes are being identified as theoretical saturation
(Strauss and Corbin 1990:188). When and how theoretical
saturation is reached, however, depends the number of texts and
their complexity, as well as on investigator experience and
fatigue, and the number of investigators examining the texts.
Again, more is better. Investigators who have more experience
finding themes are likely to reach saturation latter than novices.
Wilson and Hutchinson warn against premature closure where
the researcher "fails to move beyond the face value of the
content in the narrative (1990:123)."
Summary
Theme identification is one of the most fundamental tasks in
qualitative research. It also one of the most mysterious. Explicit
descriptions of theme discovery are rarely described in articles
and reports and if so are often regulated to appendices or
footnotes. Techniques are shared among small groups of social
scientists and are often impeded by disciplinary or
epistemological boundaries. The lack of clear methodological
descriptions is most evident during the grant-writing phase of
research. Investigators (ourselves included) struggle to clearly
explain and justify plans for discovering themes in the
qualitative data. These issues are particularly cogent when
funding reviewers are unfamiliar with qualitative traditions.
In this article we have outlined a dozen techniques that social

scientists have used to discover themes in texts. The techniques
are drawn from across epistemological and disciplinary
boundaries. They range from quick word counts to laborious, in-
depth, line-by-line scrutiny. Some work well for short answers
to open-ended questions while others are more appropriate for
rich, complex narratives. Novices and non-native speakers may
find some techniques easier than others. No single technique is
does it all. To us, these techniques are simply tools to help us
do better research.
Notes
1 ATLAS.ti (Scientific Software Development) and Nud•ist
(Qualitative
Solution
s & Research) are qualitative analysis packages distributed in
the United States by SCOLARI, Sage Publications, Inc., 2455
Teller Road, Thousand Oaks, CA 91320. Tel: (805) 499 1325.
Fax: (805) 499 0871. E_mail: [email protected] Web:
www.scolari.com.
2 Anthropac (Analytic Technologies) and Coda-A-Text
(Cartwright) are software packages that have the capacity to
convert free flowing texts into word-by-document matrices.
Code-A-Text is distributed in the United States by SCOLARI,
Sage Publications. Anthropac is created and distributed by
Analytic Technologies, Inc., Analytic Technologies, Inc., 11
Ohlin Lane, Harvard, MA 01451. Tel: (978) 456_7372. Fax:

(978) 456_7373. E_mail: [email protected] Web:
www.analytictech.com.
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Agar, Michael and Jerry Hobbs
1985 How to grow schemata out of interviews. In Directions in
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Barkin, Shari, Gery Ryan, Lillian Gelberg
1999 What clinicians can do to further youth violence primary
prevention: A qualitative study. Injury Prevention, 5:53-58.
Becker, Howard
1993 How I learned what a crock was. Journal of Contemporary
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1998 Tricks of the trade: How to think about your research
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Berelson, Bernard
1952 Content analysis in communication research. Glencoe, IL:
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Bernard, H. Russell
2000 Social Research Methods: Qualitative and Quantitative
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1992 Qualitative Research for Education: An Introduction to
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Borgatti, Stephen
1999 Elicitation Methods for Cultural Domain Analysis. In J.
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Bulmer, Martin
1979 Concepts in the analysis of qualitative data. Sociological
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Charmaz, Kathy
1990 "Discovering" Chronic Illness: Using Grounded Theory.
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Charmaz, Kathy
2000 Grounded theory: Objectivist and constructivist methods.
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Denzin and Yvonna Lincoln, eds. Thousand Oaks, CA: Sage
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1995 The development of cognitive anthropology. Cambridge:
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Dey, Ian
1993 Qualitative Data Analysis: A User_Friendly Guide for
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1991 Between speech and silence: The problematics of research

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+
Qualitative Analysis: Coding and
Thematic Analysis
College of Doctoral
Studies
+Topics Covered in this Lecture
n “Begin with the End in Mind”
n Variety of Approaches for Analyzing Qualitative
Data
n Structure of Data Collection Will Inform the
Analysis
n What is Thematic Analysis?

n Create the Code Book
n Code the Data
n Connect the Codes to Identify Themes
n Visualize the Phenomena
+ Begin with the End in Mind
n Begin with the research questions
n The researcher identifies the data needed and how it will
be analyzed to answer each research question, before
data collection begins.
n The researcher carefully thinks through the data needed
and how it will be collected to directly, clearly, and
thoroughly answer each research question.
n Then, the researcher selects the best analysis approach
for the data in order to answer each research question.

+Variety of Approaches for Analyzing
Qualitative Data
Qualitative analysis: search for patterns in the data and
explain those patterns
n KWIC: Key Words In Context
n Narrative Analysis: phenomenology; grounded theory
n Visualizing patterns: Graphs, matrices, flow charts,
models
n Conceptual Models: Process; Decision; Transition;
Taxonomies; Mental Models
n Thematic Analysis: Many different approaches to coding
and thematic analysis exist
This presentation will focus on one systematic approach to

coding and thematic analysis.
+ Structure of Data
Collection Will Inform
the Analysis
Data collected
through a single
approach such as
interviews around
the phenomena:
How the decision
gets made as to
where a child will
go to school
(parents)
RQ 1
RQ2

RQ3
Structured
Questionnaire:
Criteria used to select
a school; criteria used
to reject a school
RQ2
Interview
Q1-3 - What parents
consider as
alternative choices
Q4-5 - How parents make
the decision
Study 1 Study 2

+What is Thematic Analysis?
n A search for themes that emerge and help describe the
phenomenon by answering the research questions
n Requires careful reading, coding data, grouping/
categorizing codes, and reflecting on the categorized/
groups of codes to name and describe the theme
n Involves recognizing concepts and patterns in the data
n Provides names and definitions for the codes and themes
n Can be deductive and inductive
n Inductive: concepts emerge from data
n Deductive: models or literature provide framework for
identifying and
coding the concepts before the coding process
+Step 1: Create the Code Book
n Codebook: A codebook is a template the researcher create for

a study
n Determine the structure for the codes
n Unstructured: Emerge from the data to address all research
questions
n Semi-Structured: Emerge from the data based on research
questions
n Semi-Structured: Use concepts from literature to define
codes or coding areas/concepts
n Structured: Use models to define codes or coding areas/
concepts
n Read and understand all of the data (interviews,
questionnaires, artifacts)
n Creating a coding table is both creative and analytical.
+Step 1: Sample Codebook Structure

Code
#
Code Name: This
study used theory to
structure codes
Code
Definition
Number of
Occurrence
s of code
Direct
quotes that
illustrate
the Code
(source)
Food for Thought:
Notes and
Comments
S1.0 Define options

S2.0
A
Criteria to accept
S2.0
B
Criteria to reject Step not in model
S3.0
A
Collect data-
personal sources
S3.0
B
Collect data- hard
sources
S4.0 Analyze options
against criteria

S X Validate information Step not in model
S5.0 Make decision
+Step 2: Code the Data
n A code is a “label” to tag a variable, concept, and/
or a value found in the data
n Data are coded according to the selected
approach
n Hand coding: Highlight wording/data and put a
code number in the margin
n Add new codes to the codebook if new/
unexpected data is found
n Put examples of quotations (to illustrate the code)
in the code book

+Step 3: Connect the Codes to Identify
Themes
Identifying relationships to begin to create
meaning
n Group and/or connect the codes/concepts
n Similarities and/or differences
n Aspects of the phenomena
n Steps in the process
n Components of a model
n Identify the themes
n Name the theme for each group of codes
n Describe or define the theme that emerged from
the grouped codes
+Visualize the Phenomena
Create visuals or models to illustrate the
phenomenon or aspects of it

n Create descriptive matrices, graphs, networks,
processes, or conceptual models
n Illustrate aspects of the phenomena and their
relationships
n Can be done at the content/coding level and/or
the theme level
n Helps to answer the research questions
Visualization is analysis
+References
Bazeley, Pat (2009). Analyzing qualitative data: More than
‘identifying themes’. Malaysian Journal of Qualitative
Research,
2, 6-22.
Bernard, H. R. & Ryan, G. W. (2010). Analyzing qualitative
data:

Systematic Approaches. Los Angeles, CA: SAGE Publications,
Int.
Fereday, Jennifer (2006). Demonstrating rigor using thematic
analysis: A hybrid approach of inductive and deductive
coding and theme development. International Journal of
Qualitative Methods, 5(1), 80-91.
PSY 850
Paula Thompson, Ed.D.
Coding Textual Data

Visuals
Interpre-tation
Codes (Code Books)
Themes
Examples

Creswell’s 6 Steps of Qualitative Analysis
Organize and prepare the textual data for analysis.
Read through to get a general sense of the information and its
meaning.

meaning.
Begin coding by identifying “chunks” of text and labeling it
with a brief code. Repeat. Build Codebook.

meaning.
Identify categories and themes across the codes.
meaning.
Determine how to best represent the codes and themes in the

report (examples, visuals).
meaning.
Determine how to best represent the codes and themes in the
report (examples, visuals).
Interpretation: make meaning of the text.

What is a Code?
“A code in qualitative inquiry is most often a word or short
phrase that symbolically assigns a summative, salient, essence-
capturing and/or evocative attribute for a portion of the
language-based or visual data.”
Saldana (2013), p. 3

What is a Code?
Think of codes as a labeling and filing system
Things that Can Be CodedWhat Can Be
CodedExampleCodeEvents“It happened at the annual Board
meeting”Activities“I wrote ideas on the flip chart”Feelings or
states of mind“We were feeling hopeless about the
budget”Relationships“She was the Chair of the Board”Norms
and values“It was a real test of our ethical fortitude”Conditions
or constraints“We were told to reduce the
workforce”Theories/models“The whole culture of our

organization changed that day”Behaviors, acts“I cried about it”
When to Code Text?
When the text relates to one or more of your research questions
When a certain word, phrase, or idea is repeated in several
places or by several participants
When the text reminds you of a theory or concept from the
literature
When you simply have a gut feeling that the text is meaningful
When in doubt, code it

Subjectivity in Qualitative Research
Because you are the researcher, you subjectively select of text
to code
Part of your role is to highlight phenomena you consider
important
You also attempt to be consistent and unbiased
Consistent: like text from different interviews should be coded
the same way.
Unbiased: data that does not support your opinion/preference
should be coded anyway
Stay true to the participants’ words, stories, meanings
Chain of evidence: your findings link back to the original text

Inductively Creating Codes
Read the data
Highlight the chunk of data that you want to code
Think of a word or short phrase that captures the essence of the
meaningful text. That becomes the code.
Codes represent the text, and also summarize, distill, and
condense it
Keep code names simple, but distinct (ex. hopeful versus
optimistic)

Deductively Creating Codes
Use a theory or model to create codes before you start coding.
Codebook structure comes from pre-existing theory or model
Read with the intention of identifying presence of those codes
in the data
Optional: inductively create new codes alongside the deductive
coding
Can have the advantage of a more efficient coding process
Cycles of Coding / Recoding
Lumping and splitting – does this need to be its own code or can
it be combined with a similar one?
“high school dropout” versus “didn’t finish high school”
Trimming – getting rid of codes that only show up once or twice
and don’t seem to add value to emerging themes

Saturation – when continued reading of the text does not
generate new insights or codes
Creating hierarchies – to cluster codes into categories
Coding Hierarchies
From this:
Successful Career
Career Trajectory
Married
Children
Stay at home mom
Returned to work
Family before career

Equality
Mom belongs at home
Satisfied
Combine career and family
To This...
Coding Hierarchies
Career
Successful career
Career trajectory
Family
Married
Equality

Children
Stay at home mom
Mom belongs at home
Combine work and family
Returned to work
Satisfied
References
Bernard, H. R., & Ryan, G. W. (2009). Analyzing qualitative
data: Systematic approaches. SAGE publications.
Creswell, J.W. (2003). Research design: Qualitative,
quantitative, and mixed methods approaches. Sage

Publications.
Richards, L & Morse, J. (2012) 3rd ed. README FIRST for a
User’s Guide to Qualitative Methods. Sage Publications.
Saldana, J. (2013). The coding manual for qualitative
researchers. Sage Publications.

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