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Coding of Qualitative Data
To prepare:
Select at least five of your classmates’ Introduction posts from the Class Café to code and analyze.
Using the strategies presented in the Smith and Firth article, code your five selected postings by removing identifying information, coding the information, and identifying specific themes. As you begin the process, keep in mind that there is no right or wrong way of coding; however, your categories and associated data should—without heavy explanation—make sense to someone unfamiliar with your research.
When you have completed coding the data, reflect on your experience of analyzing this type of data. Ask yourself: How can qualitative research methods promote evidence-based practice?
Post 1-2 pages cohesive response that addresses the following:
1. Identify the overall themes you selected from coding the posts. Justify why you chose these particular themes. Try to be as scholarly as possible and remember that researchers try to refrain from directly identifying the subjects of their qualitative studies.
2. Formulate a brief analysis and conclusion about your classmates’ Introduction posts based on the themes you identified.
3. Discuss what you gained from your experiences with coding and analyzing qualitative data and how qualitative research can promote evidence-based practice.
SELECTED 5 CLASSMATE INTRODUCTION FOR THIS ASSIGNMENT
1. Happy Thanksgiving! I live in Bakersfield, California and we are in the Pacific Standard Time (PST) zone. I am currently the Director of Critical Care and have been a critical care nurse my entire 25 year career. My current job responsibilities areas include ICU, SDU, Respiratory therapy, Palliative Care, Ethics and I’ve recently taken over the clinical practice and education responsibilities for the hospital. I have been married for 27 years to Rod, have a 23 yo son, Seth, and a 21 yo daughter, Brooke. We have 3 Shih Tzu’s (our youngest one is our “tripod dog” named Nemo, because she was born with a “little fin” for a left front leg). and a cat, who is queen of the castle. We also have a “grand dog” white Husky named Coco, a 1.5 year old “toddler dog” who is entering her “terrible twos”. She is WAY too smart and occasionally a little “naughty”. Last movie – The Foreigner. It is currently 77 Degrees and we are enjoying Thanksgiving by the pool
2. Good afternoon Dr. Minnick and classmates. I hope everyone is enjoying the Thanksgiving holiday! .I live in Saratoga, California and am on PST
I work at Stanford Children’s Hospital and will be going on my 29th year there! I have always been a pediatric critical care nurse, and now I am Director of Revive- The Initiative for Resuscitation Excellence at Stanford Children’s Health.
I am married to a wonderful man, my best friend for 25 years; I have 2 boys, 24 and 22 years old. One is in real estate the other will graduate from University of Arizona this month and he is in computer programming. I have a black Labrador, Theo, who just turned 3 yrs old and keeps me busy as he eats everything!
The last movie I saw was Glass House, and I am looking forward to seeing Wonder.
It is 73 degrees here in Sunny Northern California!
3. Hello to everyone!. I live in Vermont EST .Currently work at a local long term care rehabilitation center as the Director of Nursing. I have been married for 21 years, with 2 daughters, soon to be 18 and the youngest 15, our dog Jovie she is 2 and Kit Kat (whose a cat if you couldn’t tell by the name that we couldn’t agree on 😉 ) she just turned 1. Yes these are our toddlers. Arrival actually a very interesting movie.It is currently 32 degrees not really cold for being Vermont *also you can all call me Neva 🙂
4. I live in Wantagh, New York, Eastern Time Zone.I currently work in a Cardiology office and do Stress testing and work per deim for the tele service at NYU Winthrop. (Soon to change in January).I have a 17 year old son who is a senior in high school. and a Persian Cat named Quincy. Last Movie this past summer Cars 3. Going to see the Man who invented Christmas. Currently it is 49 degrees and breezy.
5. I live in Upper Darby, Pennsylvania and we are on Eastern Standard Tome (EST). I currently work part-time as an adjunct clinical instructor at one of the local university in PA, and I also work per diem at a rehabilitation hospital in PA.I am divorced with four grown children and seven grandchildren. As the health ministry leader of my church, I spent most of my spare time planning community health programs for the church. It is currently 53 degrees and sunny in Upper Darby Pa.
References
Smith, J., & Firth, J. (2011). Qualitative data analysis: The framework approach. Nurse Researcher, 18(2), 52–62.
Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.). St. Louis, MO: Saunders Elsevier.
Chapter 12, “Qualitative Research Methods” (pp. 251-274)
Taylor-Powell, E., & Renner, M. (2003). Analyzing qualitative data. University of Wisconsin-Extension, Cooperative Extension. Retrieved from http://learningstore.uwex.edu/assets/pdfs/g3658-12
PD
University of Wisconsin-Extension
Cooperative Extension
Madison, Wisconsin
Program Development & Evaluation
G3658-12
E&&
2003
Ellen Taylor-Powell
Marcus Renner
Qualitative data consist of words and observa
tions, not numbers. As with all data, analysis and
interpretation are required to bring order and
understanding. This requires creativity, discipline
and a systematic approach. There is no single or
best way
.
Your process will depend on:
■ the questions you want to answer,
■ the needs of those who will use the informa
tion, and
■ your resources.
This guide outlines a basic approach for analyz
ing and interpreting narrative data — often
referred to as content analysis — that you can
adapt to your own extension evaluations. For
descriptions of other types of qualitative data
analysis, see Ratcliff, 2002. Other techniques may
be necessary for analyzing qualitative data from
photographs and audio or video sources.
This booklet is a companion to Analyzing
Quantitative Data G3658-6 in this series.
Text or narrative data come in many forms and
from a variety of sources. You might have brief
responses to open-ended questions on a survey,
the transcript from an interview or focus group,
notes from a log or diary, field notes, or the text
of a published report. Your data may come from
many people, a few individuals, or a single case.
Any of the following may produce narrative data
that require analysis.
■ Open-ended questions and written com
ments on questionnaires may generate
single words, brief phrases, or full para
graphs of text.
■ Testimonials may give reactions to a
program in a few words or lengthy com
ments, either in person or in written corre
spondence.
■ Individual interviews can produce data in
the form of notes, a summary of the individ
ual’s interview, or word-for-word tran
scripts.
■ Discussion group or focus group inter
views often involve full transcripts and
notes from a moderator or observer.
■ Logs, journals and diaries might provide
structured entries or free-flowing text that
you or others produce.
■ Observations might be recorded in your
field notes or descriptive accounts as a result
of watching and listening.
■ Documents, reports and news articles or
any published written material may serve as
evaluation data.
■ Stories may provide data from personal
accounts of experiences and results of pro
grams in people’s own words.
■ Case studies typically include several of
the above.
2 ■ ■ ■ P R O G R A M D E V E L O P M E N T A N D E V A L U A T I O N
Once you have these data, what do you do? The
steps below describe the basic elements of narra
tive data analysis and interpretation. This
process is fluid, so moving back and forth
between steps is likely.
Step 1 Get to know your data.
Good analysis depends on understanding the
data. For qualitative analysis, this means you
read and re-read the text. If you have tape
recordings, you listen to them several times.
Write down any impressions you have as you go
through the data. These impressions may be
useful later.
Also, just because you have data does not mean
those are quality data. Sometimes, information
provided does not add meaning or value. Or it
may have been collected in a biased way.
Before beginning any analysis, consider the
quality of the data and proceed accordingly.
Investing time and effort in analysis may give the
impression of greater value than is merited.
Explain the limitations and level of analysis you
deem appropriate given your data.
Step 2 Focus the analysis.
Review the purpose of the evaluation and what
you want to find out. Identify a few key ques
tions that you want your analysis to answer.
Write these down. These will help you decide
how to begin. These questions may change as
you work with the data, but will help you get
started.
How you focus your analysis depends on the
purpose of the evaluation and how you will use
the results. Here are two common approaches.
Focus by question or topic, time
period or event.
In this approach, you focus the analysis to look at
how all individuals or groups responded to each
question or topic, or for a given time period or
event. This is often done with open-ended ques
tions. You organize the data by question to look
across all respondents and their answers in order
to identify consistencies and differences. You put
all the data from each question together.
You can apply the same approach to particular
topics, or a time period or an event of interest.
Later, you may explore the connections and rela
tionships between questions (topics, time
periods, events).
Focus by case, individual or group.
You may want an overall picture of:
■ One case such as one family or one agency.
■ One individual such as a first-time or teen
participant in the program.
■ One group such as all first-time participants
in the program, or all teens ages 13 to 18.
Rather than grouping these respondents’
answers by question or topic, you organize the
data from or about the case, individual or group,
and analyze it as a whole.
Or you may want to combine these approaches
and analyze the data both by question and by
case, individual or group.
Step 3 Categorize
information.
Some people refer to categorizing information as
coding the data or indexing the data. However,
categorizing does not involve assigning numeri
cal codes as you do in quantitative analysis
where you label exclusive variables with preset
codes or values.
To bring meaning to the words before you:
■ Identify themes or patterns — ideas, con
cepts, behaviors, interactions, incidents,
terminology or phrases used.
■ Organize them into coherent categories
that summarize and bring meaning to the
text.
This can be fairly labor-intensive depending on
the amount of data you have. But this is the
crux of qualitative analysis. It involves reading
and re-reading the text and identifying coherent
categories.
You may want to assign abbreviated codes of a
few letters, words or symbols and place them
next to the themes and ideas you find. This will
help organize the data into categories. Provide a
descriptive label (name) for each category you
create. Be clear about what you include in the
category and what you exclude.
As you categorize the data, you might identify
other themes that serve as subcategories.
Continue to categorize until you have identified
and labeled all relevant themes.
The following examples show categories that
were identified to sort responses to the questions.
A N A L Y Z I N G Q U A L I T A T I V E D A T A ■ ■ ■ 3
Question Categories
Responses to the question were sorted into:
1. What makes a quality educational program? Staff (Stf), relevance (Rel), participation (Part),
timeliness (Time), content (Con)
2. What is the benefit of a youth mentoring program? Benefits to youth (Y), benefits to mentor (M),
benefits to family (Fam), benefits to
community (Comm)
3. What do you need to continue your learning
about evaluation?
Practice (P), additional training (Trg), time (T),
resources (R), feedback (Fdbk), mentor (M),
uncertain (U)
Possible code abbreviations are designated in parentheses
Here are two ways to categorize narrative data —
using preset or emergent categories.
.
Preset categories
You can start with a list of themes or categories in
advance, and then search the data for these
topics. For example, you might start with con
cepts that you really want to know about. Or you
might start with topics from the research litera
ture.
These themes provide direction for what you
look for in the data. You identify the themes
before you categorize the data, and search the
data for text that matches the themes.
Emergent categories
Rather than using preconceived themes or cate
gories, you read through the text and find the
themes or issues that recur in the data. These
become your categories. They may be ideas or
concepts that you had not thought about.
This approach allows the categories to emerge
from the data. Categories are defined after you
have worked with the data or as a result of
working with the data.
Sometimes, you may combine these two
approaches — starting with some preset cate
gories and adding others as they become
apparent.
Your initial list of categories may change as you
work with the data. This is an iterative process.
You may have to adjust the definition of your cat
egories, or identify new categories to accommo
date data that do not fit the existing labels.
Main categories may be broken into subcategories.
Then you will need to resort your data into these
smaller, more defined categories. This allows for
greater discrimination and differentiation.
For example, in the question about benefits of a
youth mentoring program, data within the cate
gory benefits to youth might be broken into a
number of subcategories.
Question Categories
What is the benefit Benefits to youth (Y)
of a youth mentoring School performance (Y-SP)
program? Friendship (Y-Friends)
SubSelf-concept (Y-SC)
Role modeling (Y-RM)
Benefits to mentor (M)
Benefits to family (Fam)
Benefits tocommunity (Comm)
categories
Continue to build categories until no new themes
or subcategories are identified. Add as many cat
egories as you need to reflect the nuances in the
data and to interpret data clearly.
While you want to try to create mutually exclu
sive and exhaustive categories, sometimes sec
tions of data fit into two or more categories. So
you may need to create a way to cross-index.
Reading and re-reading the text helps ensure that
the data are correctly categorized.
Example 1 shows labeling of one open-ended
question on an end-of-session questionnaire. In
this example, all responses were numbered and
given a label to capture the idea(s) in each
comment. Later, you can sort and organize these
data into their categories to identify patterns and
bring meaning to the responses.
4
■ ■ ■ P R O G R A M D E V E L O P M E N T A N D E V A L U A T I O N
Example 1. Labeling data from an end-of-session questionnaire (21 respondents)
Categories: Practice (P), additional training (Trg), time (T), resources (R), feedback (Fdbk), mentor (M),
uncertain (U)
Line 7 is left
uncoded
because
“Yes” is not
usable data.
A N A L Y Z I N G Q U A L I T A T I V E D A T A
Step 4 Identify patterns and
connections within and
between categories.
As you organize the data into categories — either
by question or by case — you will begin to see
patterns and connections both within and
between the categories. Assessing the relative
importance of different themes or highlighting
subtle variations may be important to your
analysis. Here are some ways to do this.
Within category description
You may be interested in summarizing the infor
mation pertaining to one theme, or capturing the
similarities or differences in people’s responses
within a category. To do this, you need to assem
ble all the data pertaining to the particular theme
(category).
What are the key ideas being expressed within
the category? What are the similarities and differ
ences in the way people responded, including
the subtle variations? It is helpful to write a
summary for each category that describes these
points.
Larger categories
You may wish to create larger super categories
that combine several categories. You can work up
from more specific categories to larger ideas and
concepts. Then you can see how the parts relate
to the whole.
Relative importance
To show which categories appear more impor
tant, you may wish to count the number of times
a particular theme comes up, or the number of
unique respondents who refer to certain themes.
These counts provide a very rough estimate of
relative importance. They are not suited to statis
tical analysis, but they can reveal general pat
terns in the data.
Relationships
You also may discover that two or more themes
occur together consistently in the data.
Whenever you find one, you find the other. For
example, youth with divorced parents consis
tently list friendship as the primary benefit of the
mentoring program.
You may decide that some of these connections
suggest a cause and effect relationship, or create
a sequence through time. For example, respon
dents may link improved school performance to
a good mentor relationship. From this, you might
argue that good mentoring causes improved
school performance.
Such connections are important to look for,
because they can help explain why something
occurs. But be careful about simple cause and
effect interpretations. Seldom is human behavior
or narrative data so simple.
Ask yourself: How do things relate? What data
support this interpretation? What other factors
may be contributing?
You may wish to develop a table or matrix to
illustrate relationships across two or more cate
gories.
Look for examples of responses or events that
run counter to the prevailing themes. What do
these countervailing responses suggest? Are they
important to the interpretation and understand
ing? Often, you learn a great deal from looking at
and trying to understand items that do not fit
into your categorization scheme.
Step 5 Interpretation –
Bringing it all together
Use your themes and connections to explain your
findings. It is often easy to get side tracked by the
details and the rich descriptions in the data. But
what does it all mean? What is really important?
This is what we call interpreting the data —
attaching meaning and significance to the analysis.
A good place to start is to develop a list of key
points or important findings you discovered as a
result of categorizing and sorting your data.
Stand back and think about what you have
learned. What are the major lessons? What new
things did you learn? What has application to
other settings, programs, studies? What will
those who use the results of the evaluation be
most interested in knowing?
Too often, we list the findings without synthesiz
ing them and tapping their meaning.
Develop an outline for presenting your results to
other people or for writing a final report. The
length and format of your report will depend on
your audience. It is often helpful to include
quotes or descriptive examples to illustrate your
points and bring the data to life. A visual display
might help communicate the findings.
Sometimes a diagram with boxes and arrows can
help show how all the pieces fit together.
Creating such a model may reveal gaps in your
investigation and connections that remain
unclear. These may be areas where you can
suggest further study.
■ ■ ■ 5
6 ■ ■ ■ P R O G R A M D E V E L O P M E N T A N D E V A L U A T I O N
“Nuts and bolts” of
narrative analysis
Moving from a mass of words to a final report
requires a method for organizing and keeping
track of the text. This is largely a process of
cutting and sorting.
Work by hand, either with a hard copy (print
copy) or directly on the computer. Exactly how
you manage the data depends on your personal
preference and the amount and type of qualita
tive data you have. Here are some data manage
ment tips:
■ Check your data. Often, there are data from
multiple respondents, multiple surveys or
documents. Make sure you have everything
together. Decide whether the data are of suf
ficient quality to analyze, and what level of
investment is warranted.
■ Add ID numbers. Add an identification (ID)
number to each questionnaire, respondent,
group or site.
■ Prepare data for analysis. You may need to
transcribe taped interviews. How complete
to make your transcription depends on your
purpose and resources. Sometimes, you may
make a summary of what people say, and
analyze that. Or certain parts of an interview
may be particularly useful and important
and just those sections are transcribed. Other
times, you will want to have every word of
the entire interview. However, transcription
is time-consuming. So be sure both data
quality and your use of the data are worth
the investment.
With small amounts of narrative data, you may
work directly from the original hard copy.
However, text is usually typed into a computer
program. In extension, we typically type into a
word processing program (Microsoft Word or
Word Perfect) or into Excel.
You may decide to use a relational data base
management program such as ACCESS, or a
special qualitative data analysis program.
Your decision depends on the size of your data
set, resources available, preferences, and level of
analysis needed or warranted.
Decide whether you will enter all responses ques
tion by question, or whether you want to keep all
text concerning one case, individual, group or site
together (see Step 2). Save the file.
If you type the data into a word processing
program, it is helpful to leave a wide margin on
the left so you have space to write labels for text
and any notes you want to keep. Number each
line to help with cutting and sorting later.
Computer software
Several software programs — for example,
Ethnograph and NUD*IST — specifically
analyze qualitative data. They systematize
and facilitate all the steps in qualitative
analysis. SAS software will manipulate
precategorized responses to summarize
open-ended survey questions (see Santos,
Mitchell and Pope, 1999). CDC EZ-Text is
a freeware program developed by the
Centers for Disease Control and
Prevention.
For smaller data sets and modest analysis
needs, many people work by hand, with a
word processing program or spreadsheet.
Note: Mention of products is not intended
to endorse them, nor to exclude others that
may be similar. These are mentioned as a
convenience to readers.
■ Make copies. Make a copy of all your data
(hard copy and electronic files). This gives
you one copy to work from and another for
safekeeping.
■ Identify the source of all data. As you
work with the data, you will need to keep
track of the source of the information or the
context of the quotes and remarks. Such
information may be critical to the analysis.
Make sure you have a way to identify the
source of all the data, such as by individual,
site and date.
hink about what information to keep with the
ata. For example, you might use identifiers to
esignate the respondent, group, site, county,
ate or other source information. Or you may
ish to sort by variables such as age, gender or
osition. Will you want to compare and contrast
y demographic variable, site and date?
hese identifiers stay with the information as you
ut and sort the data, either by hand or in the
omputer. If you are working with hard copies,
ou might use different colors of paper to color-
ode responses from different people or groups
for example, see Krueger, 1998).
■ Mark key themes. Read through the text.
Look for key ideas. Use abbreviations or
symbols (codes) to tag key themes — ideas,
concepts, beliefs, incidents, terminology
used, or behaviors. Or, you might give each
theme a different color. Keep notes of emerg
T
d
d
d
w
p
b
T
c
c
y
c
(
A N A L Y Z I N G Q U A L I T A T I V E D A T A ■ ■ ■ 7
Example 2. Identify themes and label data.
Be
responsive
to local
needs and
questions
Availability
Responsive: willing and
able to answer
questions, timeliness,
personal touch
Local connection
Follow-up
Geographic coverage
Service area, serve same
people, need to extend
out
Staff
Serve community,
professional, responsive
Focus
set priorities; stretched
too thin
Reaching
out vs.
focus
Staff =
program
Create a
wide margin
where you
can label
key
ideas.
Highlight
quotes for
future use.
Keep notes
of emerging
ideas.
ing ideas or patterns and how you are inter
preting the data. You can write or type these
in the margins, or in a specified column. Or
keep a separate notebook that records your
thoughts and observations about the data
(see Example 2).
■ Define categories. Organize or combine
related themes into categories. Name (label)
these categories by using your own descrip
tive phrases, or choose words and key
phrases from the text. Be clear about what
the category stands for. Would someone
unfamiliar with the data understand the
label you have chosen? Write a short
description or definition for each category,
and give examples or quotes from the text
that illustrate meaning. Check with others to
see if your labels make sense. You may also
describe what the category does not include
to clarify what is included.
■ Cut and sort. Once you define categories
and label data, grouping the data into cate
gories involves some form of cutting and
sorting. This is a process of selecting sec
tions of data and putting them together in
their category.
Hard copy — A simple method is to cut text
out of the printed page and sort into differ
ent piles. Each pile represents a category
and has a name. As you work with the data,
8 ■ ■ ■ P R O G R A M D E V E L O P M E N T A N D E V A L U A T I O N
you may make new piles, combine piles, or
divide piles into subcategories. Remember
to keep the identifier (source of data) with
the data so you know where the text came
from. Also, remember that you are working
with a copy, not the original material.
Electronic copy — It is relatively simple
and fast to move text around in a word pro
cessing program using the Windows plat
form. You can cut and paste text into differ
ent Windows, each representing a single cat
egory. If you type the category label directly
into the computer file, you can use the
search function to gather chunks of text
together to copy and paste. Or you can sepa
rate the text into paragraphs, code the
beginning of each paragraph, and then sort
the paragraphs. You may prefer to use Excel.
If the data are in Microsoft Word, you can
easily transfer them to Excel. Set up an Excel
file that includes columns for the ID
number, identifiers, categories (themes),
codes, and text (see Example 3).
When cutting and sorting, keep track of the
source of the data. Be sure to keep identifiers
attached to all sections of data.
Keep enough text together so you can make
sense of the words in their context. As you cut
and move data, text can easily become frag
mented and lose its contextual meaning. Be sure
to include enough surrounding text so the
meaning is not open to misinterpretation.
Example 3. Screen shot of Excel spreadsheet
If data do not seem to fit, place those in a sepa
rate file for possible use later.
■ Make connections. Once you sort the data,
think about how the categories fit together
and relate. What seems more important, less
important? Are there exceptions or critical
cases that do not seem to fit? Consider alter
native explanations. Explore paradoxes, con
flicting themes, and evidence that seems to
challenge or contradict your interpretations.
To trace connections, you can spread note cards
across a table, use sticky notes on walls, or draw
diagrams on newsprint showing the categories
and relationships. Another approach is to create
a two-dimensional or three-dimensional matrix.
List the categories along each axis, and fill the
cells with corresponding evidence or data. For
further explanation, see Patton, 1990.
You can use simple hand tabulations or a com
puter program:
■ to search and count the frequency a topic
occurs or how often one theme occurs with
another, or
■ to keep track of how many respondents
touch on different themes.
Such counts may be illuminating and indicate
relative importance. But treat them with caution
— particularly when responses are not solicited
the same way from all respondents, or not all
respondents provide a response.
A N A L Y Z I N G Q U A L I T A T I V E D A T A
Enhancing the
process
As with any analysis process, bias can influence
your results. Consider the following ways to
increase the credibility of your findings.
Use several sources of data.
Using data from different sources can help you
check your findings. For example, you might
combine one-on-one interviews with information
from focus groups and an analysis of written
material on the topic. If the data from these dif
ferent sources point to the same conclusions, you
will have more confidence in your results.
Track your choices.
If others understand how you came to your con
clusions, your results will be more credible. Keep
a journal or notebook of your decisions during
the analysis process to help others follow your
reasoning. Document your reasons for the focus
you take, the category labels you create, revisions
to categories you make, and any observations
you note concerning the data as you work with
the text.
People tend to see and read only what supports
their interest or point of view. Everyone sees data
through his or her own lens and filters. It is
important to recognize and pay attention to this.
The analysis process should be documented so
that another person can see the decisions that
you made, how you did the analysis, and how
you arrived at the interpretations.
Involve others.
Getting feedback and input from others can help
with both analysis and interpretation. You can
involve others in the entire analysis process, or in
any one of the steps. For example, several people
or one other person might review the data inde
pendently to identify themes and categories.
Then you can compare categories and resolve
any discrepancies in meaning.
You can also work with others in picking out
important lessons once cutting and sorting is
done. Or you can involve others in the entire
analysis process, reviewing and discussing the
data and their meaning, arriving at major conclu
sions, and presenting the results.
Involving others may take more time, but often
results in a better analysis and greater ownership
of the results.
Finally, with any qualitative analysis, keep in
mind the following cautions.
Avoid generalizing.
The goal of qualitative work is not to generalize
across a population. Rather, a qualitative
approach seeks to provide understanding from
the respondent’s perspective. It tries to answer
the questions: “What is unique about this indi
vidual, group, situation or issue? Why?”
Even when you include an open-ended question
on a survey, you are seeking insight, differences,
the individual’s own perspective and meaning.
The focus is on the individual’s own or unique
response.
Narrative data provide for clarification, under
standing and explanation — not for generalizing.
Choose quotes carefully.
While using quotes can lend valuable support to
data interpretation, often quotes are used that
only directly support the argument or illustrate
success. This can lead to using people’s words
out of context or editing quotes to exemplify a
point.
When putting together your final report, think
about the purpose for including quotes. Do you
want to show the differences in people’s com
ments, give examples of a typical response rela
tive to a certain topic, highlight success? In any
event, specify why you chose the selected quotes.
Include enough of the text to allow the reader to
decide what the respondent is trying to convey.
Confidentiality and anonymity are also concerns
when using quotes. Even if you do not give the
person’s identity, others may be able to tell who
made the remark. Consider what might be the
consequences of including certain quotes. Are
they important to the analysis and interpreta
tion? Do they provide a balanced viewpoint?
Get people’s permission to use their words.
Check with others about the usefulness and
value of the quotes you select to include.
Address limitations and
alternatives.
Every study has limitations. Presenting the prob
lems or limitations you had while collecting and
analyzing the data helps others better under
stand how you arrived at your conclusions.
Similarly, it is important to address possible
alternative explanations. What else might explain
the results? Show how the evidence supports
your interpretation.
■ ■ ■ 9
10 ■ ■ ■ P R O G R A M D E V E L O P M E N T A N D E V A L U A T I O N
Concluding comments
Working with qualitative data is a rich and enlightening
experience. The more you practice, the easier and more
rewarding it will become. As both a science and an art,
it involves critical, analytical thinking and creative,
innovative perspectives (Patton, 1990).
Be thoughtful, and enjoy.
CDC EZ-Text. Centers for Disease Control and
Prevention, National Center for HIV, STD, and TB
Prevention Divisions of HIV/AIDS Prevention,
Behavioral Intervention Research Branch. Retrieved 4-9
03: http://www.cdc.gov/hiv/software/ez-text.htm
Krueger, Richard A. 1998. Analyzing and Reporting Focus
Group Results. Focus Group Kit 6. Thousand Oaks,
Calif.: Sage Publications.
Krueger, Richard A. 1988. Focus Groups: A Practical Guide
for Applied Research. Newbury Park, Calif.: Sage
Publications.
Miles, Matthew B., & A. Michael Huberman. 1994.
Qualitative Data Analysis: An Expanded Sourcebook.
Second Edition. Thousand Oaks, Calif.: Sage
Publications.
Patton, Michael Q. 1990. Qualitative Evaluation and
Research Methods. 2nd Edition. Newbury Park, Calif.:
Sage Publications.
Pope, Catherine, Sue Ziebland & Nicholas Mays. 1999.
Qualitative Research in Health Care. Second Edition.
London: BMJ Publishing Group. Chapter 8. Analysing
Qualitative Data. Retrieved 4-9-03:
http://www.bmjpg.com/qrhc/chapter8.html
Ratcliff, Donald. 2002. Qualitative Research. Part Five:
Data Analysis. Retrieved 4-9-03: http://www.don.rat
cliff.net/qual/expq5.html
Santos, J. Reynaldo A., Diann Mitchell & Paul Pope.
1999. Are Open-Ended Questions Tying You in Knots?
Journal of Extension. 37:4.
Retrieved 4-9-03:
http://www.joe.org/joe/1999august/iw2.html
This publication is one in a series of program evaluation
guides designed to help extension educators better plan
and implement credible and useful evaluations. These
also may be useful to agencies or funders seeking realis
tic evaluation strategies.
These practical how-to evaluation publications are
available on the UW-Extension Program Development
and Evaluation web site:
www.uwex.edu/ces/pdande
This web site also houses Quick Tips, easy-to-use briefs
for improving your evaluation practice. You can also
find evaluation studies, instruments, workshop presen
tations, an evaluation curriculum and links to more
resources. Maintained as part of the University of
Wisconsin System, the web site is continually updated
and improved.
www.uwex.edu/ces/pdande
http://www.joe.org/joe/1999august/iw2.html
http://www.don.rat
http://www.bmjpg.com/qrhc/chapter8.html
http://www.cdc.gov/hiv/software/ez-text.htm
■ ■ ■ P R O G R A M D E V E L O P M E N T A N D E V A L U A T I O N
Note: Analyzing Qualitative Data is a companion to Analyzing Quantitative Data G3658-6 in this series.
© 2003 by the Board of Regents of the University of Wisconsin System. Send inquiries about copyright
permissions to Cooperative Extension Publishing Operations, 103 Extension Bldg., 432 N. Lake St.,
Madison, WI 53706.
Authors: Ellen Taylor-Powell, evaluation specialist, and Marcus Renner, research assistant, Program
Development and Evaluation, University of Wisconsin-Extension.
Acknowledgements: This booklet is based on material initially written in 1999 and reviewed by Dick
Krueger (University of Minnesota), Rey Santos (Texas A&M University) and Heather Boyd (University
of Wisconsin-Extension). Thanks go to them for their early input that hopefully is reflected in this final
product that has been ably edited by Rhonda Lee.
Produced by Cooperative Extension Publishing Operations
University of Wisconsin-Extension, U.S. Department of Agriculture and Wisconsin counties cooperat
ing. UW-Extension provides equal opportunities in employment and programming, including Title IX
and ADA. If you need this material in another format, contact the Office of Equal Opportunity and
Diversity Programs or call Cooperative Extension Publishing Operations at (608) 262-2655.
Copies of this publication and others in this series are available from your Wisconsin county
UW-Extension office or from Cooperative Extension Publications:
(877) 947-7827; Fax (414) 389-9130
http://www1.uwex.edu/ces/pubs
Analyzing Qualitative Data (G3658-12) I-04-2003
http://www1.uwex.edu/ces/pubs
- Analyzing Qualitative Data
- “Nuts and bolts” of narrative analysis
- Enhancing the process
Introduction
Narrative data
The analysis process
Pitfalls to avoid
References
Resources
52 NURSERESEARCHER 2011, 18, 2
issues in research
Qualitative data analysis: the
framework approach
Introduction
The framework approach was developed in the 1980s by social policy research-
ers at the National Centre for Social Research as a method to manage and
analyse qualitative data in applied policy research. In this context, the research
brief is commissioned; aims and objectives are highly focused and the research-
ers work with structured topic guides to elicit and manage data. This approach
contrasts with entirely inductive approaches, such as grounded theory, where the
research is an iterative process and develops in response to the data obtained
and ongoing analysis. More recently, the framework approach has been gaining
in popularity as a means of analysing qualitative data derived from healthcare
research because it can be used to manage qualitative data and undertake
Abstrac
t
Qualitative methods are invaluable for exploring the complexities of health
care and patient experiences in particular. Diverse qualitative methods are
available that incorporate different ontological and epistemological
perspectives. One method of data management that is gaining in popularity
among healthcare researchers is the framework approach. We will outline
this approach, discuss its relative merits and provide a working example of
its application to data management and analysi
s
.
Author
s
Joanna Smith MSc, BSc(Hons) RSCN, RGN is lecturer in children
and young people’s nursing, School of Nursing and Midwifery,
University of Salford, UK
Jill Firth RGN, PhD is a senior research fellow at the School of
Healthcare, University of Leeds, UK
Keywords
Qualitative research, framework approach, patient experiences
NURSERESEARCHER 2011, 18, 2 53
analysis systematically. This enables the researcher to explore data in depth
while simultaneously maintaining an effective and transparent audit trail, which
enhances the rigour of the analytical processes and the credibility of the findings
(Ritchie and Lewis 2003). This article will provide an overview of the framework
approach as a means of managing and analysing qualitative data. To illustrate its
application, we will draw on a study undertaken by one of the authors (JS) as
part of her programme of doctoral research investigating parents’ management
of their children’s hydrocephalus and shun
t.
Context
Delivering health care that is responsive to individual needs is an integral part
of the modernisation agenda of the UK’s NHS. Policy directives for people with
long-term conditions emphasise actively involving patients in the management of
their conditions, valuing their expertise and working collaboratively with patients
(Department of Health (DH) 2001, 2005, 2007). When the patient is a child, this
includes understanding the views and experiences of their parents. The potential
benefits of this involvement include: empowering patients to take control of their
health needs, better mutual understanding for patients and healthcare profession-
als, and patients influencing the healthcare agenda (Simpson 2006). Qualitative
approaches are appropriate for exploring the complexities of health and wellbeing
and can help in creating an in-depth understanding of the patient experience.
Debates about the epistemological and ontological perspectives underpinning
qualitative methods can overshadow the need to ensure that qualitative studies
are methodologically robust. Published qualitative research often lacks transpar-
ency in relation to the analytical processes employed, which hinders the ability of
the reader to critically appraise the studies’ findings (Maggs-Rapport 2001). This is
not to say that the research is not of good quality, but there are sometimes weak-
nesses in reporting that make evaluation problematic. For the novice researcher,
a framework to guide the stages of the data analysis has the potential to assist in
developing the skills required to undertake robust qualitative data analysis.
Approaches to qualitative data analysis
Methods for undertaking qualitative data analysis can be divided into three
categories:
54 NURSERESEARCHER 2011, 18, 2
issues in research
n Sociolinguistic methods, such as discourse and conversation analysis, that
explore the use and meaning of language.
n Methods, typified by grounded theory, that focus on developing theory.
n Methods, such as content and thematic analysis, that describe and interpret
participants’ views.
Despite the diversity of qualitative methods, data are often obtained through
participant interviews. The subsequent analysis is based on a common set of
principles: transcribing the interviews; immersing oneself in the data to gain
detailed insights into the phenomena under investigation; developing a data-
coding system; and linking codes or units of data to form overarching categories
or themes that can lead to the development of theory (Morse and Richards
2002). Analytical frameworks such as the framework approach (Ritchie and
Lewis 2003) and thematic networks (Attride-Stirling 2001) are gaining in popu-
larity because they systematically and explicitly apply the principles of undertak-
ing qualitative analysis to a series of interconnected stages that guide the process.
Generating themes from data is a common feature of qualitative methods and
a widely used analytical method. Thematic analysis is an interpretive process in
which data are systematically searched for patterns to provide an illuminating
description of the phenomenon (Tesch 1990). This results in the development
of meaningful themes without explicitly generating theory. Thematic analysis
can provide rich insights into complex phenomena, be applied across a range
of theoretical and epistemological approaches, and expand on or test existing
theory (Braun and Clarke 2006). However, thematic analysis has been criticised
for lacking depth (Attride-Stirling 2001). The thematic analysis approach can
result in sections of data being fragmented from the original, which can result in
data being misinterpreted. As a consequence findings are subjective and lacking
transparency in how themes are developed.
An overview of the framework approach
The framework approach has many similarities to thematic analysis, particularly
in the initial stages when recurring and significant themes are identified. However,
analytical frameworks, such as thematic networks and the framework approach,
emphasise transparency in data analysis and the links between the stages of the
analysis (Pope et al 2000, Ritchie and Lewis 2003, Braun and Clark 2006). Central
NURSERESEARCHER 2011, 18, 2 55
to the analytical processes in the framework approach is a series of interconnected
stages that enables the researcher to move back and forth across the data until a
coherent account emerges (Ritchie and Lewis 2003). This results in the constant
refinement of themes that may aid the development of a conceptual framework.
Application of the framework approach
We independently chose the framework approach to underpin data analysis for
a range of reasons. First, the framework approach is particularly suited to analys-
ing cross-sectional descriptive data, enabling different aspects of the phenomena
under investigation to be captured (Ritchie and Lewis 2003). Second, research-
ers’ interpretations of participants’ experiences are transparent (Ritchie and Lewis
2003). Third, moving from data management to developing the analysis suffi-
ciently to answer research questions can be a daunting and bewildering task for
novice researchers. The interconnected stages in the framework approach explic-
itly describe the processes that guide the systematic analysis of data from initial
management through to the development of descriptive to explanatory accounts.
In the example we use to illustrate the stages of analysis, JS conducted inter-
views to elicit parents’ perceptions of living with a child with hydrocephalus.
Shunts are the main treatment for hydrocephalus but they are problematic in that
they are prone to malfunctions, which for some children can be life-threatening.
Detecting shunt failure is not straightforward because the signs and symptoms are
variable, subtle and often idiosyncratic to the individual child. Common symp-
toms, such as headache, vomiting and drowsiness, are presenting symptoms of
many childhood illnesses, particularly viral infections. An interview topic guide
enabled the interviewer to explore parents’ perceptions of living with their child
and examine their decision making in relation to identifying shunt malfunction
and seeking healthcare advice. The interviews were conducted face-to-face, either
with individual parents or jointly. Interviews were transcribed verbatim. The next
stage of the research applied the framework approach described by Ritchie and
Lewis (2003). Briefly these stages are:
n Data management – becoming familiar with the data (reading and re-read-
ing); identifying initial themes/categories; developing a coding matrix; assigning
data to the themes and categories in the coding matrix.
n Descriptive accounts – summarising and synthesising the range and
56 NURSERESEARCHER 2011, 18, 2
issues in research
diversity of coded data by refining initial themes and categories; identify
association between the themes until the ‘whole picture’ emerges; devel-
oping more abstract concepts.
n Explanatory accounts – developing associations/patterns within concepts
and themes; reflecting on the original data and analytical stages to ensure
participant accounts are accurately presented and to reduce the possibil-
ity of misinterpretation; interpreting/finding meaning and explaining the
concepts and themes; seeking wider application of concepts and themes.
Data management using a case and theme-based approach
Codes and categories were developed by considering each line, phrase or para-
graph of the transcript in an attempt to summarise what parents were describing.
The process initially involved using printed versions of the transcripts with key
phrases highlighted and comments written in the margins to record preliminary
thoughts. Key phrases were summarised using participants’ own words (‘in-vivo’
codes). In-vivo codes are advocated in the framework approach as a means of
staying ‘true’ to the data (Ritchie and Lewis 2003). Initial thoughts began to
develop into more formal ideas from which a coding matrix was generated. The
coding index enabled changes to be tracked and progress to be recorded. Table
1 gives an example of the coding matrix, highlighting the processes involved in
identifying codes and categories.
Identifying and testing a thematic framework
The coding matrix was developed from four family interview transcripts, which
appeared to represent a range of experiences. These parents had different
experiences in relation to the frequency of shunt complications, including one
family whose older child had not had any problems with the shunt. As part of
the measures taken to ensure rigour, two experienced researchers reviewed the
coding matrix and transcripts from which the matrix was derived. Changes were
tracked by maintaining a research journal and adding notes to the margins of
the matrix. Each in-vivo code initially formed a potential category but as coding
progressed and the number of categories developed they were grouped together
into broader categories. Similar categories were eventually brought together to
form initial themes. These categories and themes formed a ‘coding index’ that
NURSERESEARCHER 2011, 18, 2 57
was used as a means of organising the whole dataset. However, the coding index
was constantly refined throughout the data analysis as new insights emerged.
Table 2 includes an example of the coding index.
Unlike policy-driven research, the interview topic guide in healthcare research
may be less tightly focused and a qualitative software package such as NVivo can
aid data retrieval when searching for patterns in the data. Initial data manage-
ment used written notes and memos, but these were subsequently transferred
to an NVivo database. As data management progressed, NVivo was used more
intuitively, with the tagging of data into relevant categories shifting from a paper-
based exercise to directly coding data in NVivo. Data management using the
coding retrieval and search facilities in NVivo was the first stage of more in-depth
analysis because it enabled researchers to start thinking about the links between
the initial categories and themes, while retaining links to the original data.
Table 1. Example of the coding matrix used to identify codes
and categories
Interview transcript:
Family 11, child five
years, many hospital
admissions, three
shunt revisions
Description
(in-vivo codes)
Preliminary
thoughts
(what is this
about?)
Initial
categories*
‘You know if your child is
being sick whether they
are poorly or not.’ Dad
‘Out of hours that they
tend to keep her in…
I think it is the out of
hours that we find
difficult, if we are
unsure…
If we go out of hours
service we know that
we will be admitted. So
we tend to wait a bit
longer.’ Mum
‘know… your
child’.
‘Unsure’ whether
to access out of
hours services
‘know we will be
admitted’ ‘wait
a bit’.
Knowing
something is
wrong.
Uncertainty
access out of
hours services.
Experience/
views of out of
hours services.
Trying to
decide what
to do.
Recognising
when the child
is ill.
Uncertainty:
when to access
services.
Views about
services.
Making
decisions.
*Some of the initial categories became themes (for example, recognising when the
child is ill) or core concepts (for example, uncertainty)
58 NURSERESEARCHER 2011, 18, 2
issues in research
Table 2. An example of the coding index
Initial themes Initial categories
Uncertainty n Immediate impact of the condition.
n Long-term effects of the condition.
n Child becoming independent.
n Child’s development.
n Embarking on family activities.
Responding to
the child’s needs
n Recognising when the child is ill.
n Experiences of shunt complications.
n Beliefs about the signs of shunt malfunction.
n Recognising when the child’s illness is due to shunt
malfunction.
n Feelings relating to the possibility of child’s shunt
malfunctioning.
n Seeking help for child.
n Taking precautions to protect child because of having
a shunt.
n Making allowances for child because of hydrocephalus.
n Explaining hydrocephalus to child.
n Supporting child to develop.
Making
decisions
n Making choices about treatment options.
n Beliefs about involvement in healthcare decisions.
n Deciding if illness is due to shunt problem or not.
n Deciding when to access healthcare services.
n Lifestyle choices.
n Family activities.
n Factors that influence decision making.
n Feelings about making decisions.
Coding matrices can be created using Word or Excel spreadsheets but the process
can be unwieldy and problematic when large volumes of data are involved. Since
this research was undertaken, the National Centre for Social Research (www.
natcen.ac.uk) has developed a computer-aided, qualitative data package to assist
in the application of the framework approach. The package can be used to sum-
marise data in a series of matrices from which it is possible to conduct case-based
and thematic analysis. This may overcome some of the inherent difficulties faced
NURSERESEARCHER 2011, 18, 2 59
when trying to manage large volumes of data using spreadsheets. However, the
software and training provided by the centre have cost implications that need to
be factored into applications for research funding.
Development of descriptive and explanatory accounts
Descriptive accounts involve summarising and synthesising the range and diver-
sity of coded data by refining initial themes and categories. Crucial elements in
qualitative analysis are the critical thinking that occurs in relation to: how par-
ticipants’ descriptions are coded; links between codes and categories; and links
between categories and themes (Ritchie and Lewis 2003). Remaining true to
participants’ descriptions is a fundamental principle in the framework approach
and central when developing more abstract concepts.
For the novice, the movement from in-vivo codes and initial categories and
themes to more abstract concepts can seem contradictory. Two linked proc-
esses were undertaken to reconcile these tensions. First, data were synthesised
by refining the initial themes and categories until the ‘whole picture’ emerged.
To ensure the themes were grounded in participants’ descriptions, the author
constantly referred to the original transcripts and checked meaning across inter-
views using Nvivo’s search functions. Second, abstract concepts were developed
by identifying key dimensions of the synthesised data, and making associations
between themes and concepts. Table 3 (page 61) gives an example of moving
from the initial themes and categories in the coding index, and the links between
the refined categories and final themes from which the core concepts emerged.
To ensure the experiences and beliefs of parents were accurately reflected and
to minimise misinterpretation, explanatory accounts began with reflection on the
original data and on the analytical stages. Through using the framework approach,
three core concepts were developed that appeared to reflect parents’ accounts of
living with a child with hydrocephalus: ‘uncertainty’, ‘becoming an expert’ and ‘liv-
ing a normal life’. In the remainder of this article, the development of ‘uncertainty’
will be used to illustrate the application of the framework approach.
The final stages involved making sense of the concepts and themes in terms
of participants’ lives and experiences. This was achieved by exploring the rela-
tionship between the core concepts, the established literature and theoretical
perspectives relating to living with a child with a long-term condition.
60 NURSERESEARCHER 2011, 18, 2
issues in research
Once the natures of the phenomena have been described and concepts identi-
fied, typologies may emerge that explain how concepts operate (Ritchie and
Lewis 2003). The way in which parents respond to illness in their children was
considered by linking their accounts to ‘uncertainty’. Penrod (2007) identified
four possible typologies that reflect individuals’ perceptions of their levels of con-
fidence and control when faced with uncertainty: ‘overwhelming uncertainty’;
‘role uncertainty’; ‘pervasive uncertainty’; and ‘minimal uncertainty’. Parents’
lack of control over shunt malfunction positioned them in ‘overwhelming uncer-
tainty’. This may explain why parents’ accounts were dominated by the possibil-
ity that their children’s shunts could malfunction at any time.
Our experiences of undertaking qualitative data analysis share similarities with
the experiences of other novice qualitative researchers (Li and Seale 2007). The
first challenge related to the process of attaching labels to preliminary codes,
which were initially abstract in nature and did not fully represent the extracts
from which they were derived. Although our enthusiasm remained undiminished,
we grossly underestimated the time required to undertake the early stages of the
analysis. Yet these stages are essential if the findings are to be credible. In our
separate studies, we valued working with experienced researchers who were will-
ing to challenge assumptions and decisions at each stage of the analysis, adding
to the rigour of the research. Sufficient time needs to be allocated to evaluating
initial thoughts and reflecting on the relationships between ideas and participants’
accounts. Asking the question, ‘What are participants really trying to describe?’
when considering sections of the data and using participants’ own words when-
ever possible assisted in ensuring that labelling reflected participants’ accounts.
For part-time students, other work commitments can make it difficult to
re-engage with the data after a period away, although it can prevent over-immer-
sion. Returning to data analysis after time away and re-reading all the transcripts
to consider the phenomena as a whole resulted in data analysis becoming much
more meaningful. Forward and backward movement between the data, par-
ticipants’ accounts and links with initial categories resulted in the emergence of
the final categories and the development of the final conceptual framework that
describes parents’ accounts. This iterative process resonates with the central tenet
of the framework approach that the interconnected stages are not linear, but a
scaffold that guides the analysis (Ritchie and Lewis 2003).
NURSERESEARCHER 2011, 18, 2 61
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62 NURSERESEARCHER 2011, 18, 2
issues in research
Conclusion
We have found the framework approach a valuable tool for data analysis in
qualitative healthcare research. For researchers engaging with qualitative research
for the first time, this approach provides an effective route map for the journey
and enables a case and theme-based approach to data analysis. We have given
examples that illustrate instances where the context of patients’ experiences has
been retained, while exploring associations and explanations in the data and
drawing on existing theories and established literature. The approach enables
researchers to track decisions, which ensures links between the original data and
findings are maintained and transparent. This adds to the rigour of the research
process and enhances the validity of the findings.
This article has been subject to double-blind review and checked
using antiplagiarism software
Acknowledgement
We wish to thank Dr David Alldred, lecturer, Academic Unit of Medicines
Management at the University of Leeds and Dr Jon Silcock, senior lecturer in
pharmacy practice at University of Bradford.
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