Qualitative Data Analysis and Interpretation

SEMINAR ON:
QUALITATIVE DATA ANALYSIS
AND
INTERPRETATION
Presented by:
Ms. Prekshya Thapa
College of Nursing,
B. P. Koirala Institute of Health Sciences, Dharan,
Nepal

Introduction:
• The purpose of data analysis is to organize, provide
structure to, and elicit meaning from data.
• In qualitative studies, data collection and data analysis
often occur simultaneously rather than after data are
collected.
• The search for important themes and concept begins
from the moment data collection gets underway.
• Qualitative analysis is the labor-intensive activity that
requires creativity, conceptual sensitivity and sheer hard
work.
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QUALITATIVE DATA ANALYSIS AND
INTERPRETATION

Qualitative data analysis
challenges:
• Qualitative data analysis is particularly challenging
enterprise for three major reasons.
• First, there are no universal rules for analyzing
qualitative data , and the absence of standard
procedures makes it difficult to explain how to do such
analyses.
• The second challenge is the enormous amount of work
required. Qualitative analyst must organize and make a
sense of pages and pages of narrative materials.
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challenges:
• A final challenge comes in reducing data for reporting
purposes.
• Qualitative data must balance the need to be concise
with the need to maintain the richness and evidentiary
value of their data.
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Qualitative analysis process
• Qualitative researchers typically scrutinize their data
carefully and deliberately, often reading the data over
and over again in a search for meaning and deeper
understandings.
• Insights and theories cannot emerge until researchers
become completely familiar with data.
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• Morse and Field (1995) note that qualitative data
analysis is a “process of fitting data together, of making
invisible obvious, of linking and attributing consequences
to antecedents. It is a process of conjecture and
verification, of correction and modification , of suggestion
and defense.”
• Morse and Field (1995) have identified four process of
analysis:
– Comprehending
– Synthesizing
– Theorizing
– Recontextualizing
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process
1. Comprehending:
 Early in analytic process, qualitative researchers strive
to make sense of the data and to learn “ what is going
on”
 When comprehension is achieved , they are able to
prepare thorough , rich description of phenomenon
under study, and new data do not add much to that
description.
 Thus comprehension is completed when saturation is
achieved.
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process
2. Synthesizing
 It involves a “sifting” of the data and putting pieces
together.
 At this stage, researchers get a sense of what is typical
regard to the phenomenon , and what variation is like.
 At the end of the synthesis, researcher can make some
generalized statements about the phenomenon and
about study participants.
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process
3. Theorizing :
 Theorizing involves a systematic sorting of a data.
 During this process, researchers develop alternative
explanations of the phenomenon and then hold these
explanations up to determine their fit with the data.
 Theorizing continues to evolve until the best
explanation is obtained.
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process
4. Recontextualizing
 The process of recontextualizing involves further
development of the theory to explore its applicability to
other settings or groups.
 In qualitative inquires whose ultimate goal is theory
development, it is the theory that must be recontextualized
and generalized.
Although the intellectual processes in qualitative analysis
are not linear in the same sense that quantitative analysis
is, these four processes follow a rough progression over
the course of the study.
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process
• Comprehension occurs primarily while in the field
• Synthesis begins in the field but may continue well after
the field work is done.
• Theorizing and Recontextualizing are the processes that
are difficult to undertake before synthesis has been
completed.
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A. TRANSCRIBING
QUALITATIVE DATA
Qualitative Data
Management And
Organization
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Qualitative Data Management and
Organization
Transcribing qualitative data
– Audiotaped/videotaped interviews and field notes are
major data sources in qualitative studies.
– Verbatim transcription of the tapes is crucial step in
preparing for data analysis and researchers need to
ensure that the transcription are accurate and that
they validly reflect the interview experience.
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• Transcription errors are almost inevitable, which means
the researchers need to check the accuracy of
transcribed data. That there are three categories of error:
1. Deliberate alterations of data:
 Transcribers may intentionally “fix” data to make the
transcription look more like what they “should” look like.
 For e.g, transcriber may alter profanities, omit the sounds
such as phone ringing, or “tidy up” the text by deleting
“ums” and “uhs”.
 It is crucial to impress on transcribers the importance of
verbatim accounts.
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II. Accidental alterations of the data:
 The insertion or omission of commas or question
marks can alter the interpretation of the text.
 Another error is the misinterpretation of the text. For
example, the actual words might be, “ this was totally
moot, "but the transcription might read “this was totally
mute”.
 Researchers should take place to verify accuracy
before analysis gets underway.
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III. Unavoidable alterations:
 Data are unavoidably altered by the fact that
transcriptions capture only a portion of experience of
an interview experience.
 For example, transcriptions will inevitably miss
nonverbal clues such as body language, intonations
and so on.
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Qualitative data management and
organization
• Researcher should begin data analysis with the best
possible quality data, which requires careful training of
transcribers, ongoing feedback, and continuous efforts to
verify accuracy.
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B. DEVELOPING A CATEGORY
SCHEME
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organization
Developing a Category Scheme
– Qualitative data analysis begins with data
organization- by classifying and indexing the data.
– This phase of data analysis is essentially
reductionist- data must be converted to smaller, more
manageable units that can be retrieved and reviewed.
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– The most commonly used procedure is to develop a
category scheme and then to code data according to
the categories.
– Developing a high category scheme involves a careful
reading of the data, with an eye to identifying
underlying concepts and clusters of concepts.
– Researcher whose aims are primarily descriptive tend
to use categories that are fairly concrete.
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Coding scheme for
food insecurity and
hunger
I hate being on welfare, it is a pain in the
butt. I don’t need their cash, but the food
stamps, they help a lot because it is hard, it
is really hard. I got to live day by day for food
for my kids. I have to call down to the shelter
to get them to send you food, and you hate
doing that because it is embarrassing, but I
have to live day by day. I have to do things
so my kids can eat. I don’t worry about me,
just for my kids because I can go a day
without eating, but as long as my kids eat.
But I never have to worry about my kids
starving because I have family.
Excerpt from Polit et al. (2000) study for
food insecurity and hunger in low
income families
A. Use of food service/programs
1. Food Stamps
2. Food Pantries
3. Soup Kitchens
B. Food Inadequacy
Experiences
1. Problems feeding family,
having enough food
2. Having to eat undesirable
food.
C. Strategies to avoid hunger
1. Getting food from friends,
relatives
2. Borrowing money
D. Special issues
1. Mothers sacrificing for
children
2. Effects of welfare reform on
hunger
3. Stigma
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A1
B1
C1
D3
D1
A2

• Studies that are designed to develop a theory are more
likely to involve abstract ,conceptual categories.
• In creating conceptual categories, researchers must
break the data into segments, closely examine them,
and compare them to other segments for similarities and
dissimilarities to determine what the meaning of those
phenomena are.
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• The researcher ask the questions such as the following
about discrete events, incidents, or statements:
– What is this?
– What is going on?
– What does it stand for?
– What else is like this?
– What is this distinct from?
• Important concepts that emerge from close examination
of the data are then given a label that forms a basis for a
category.
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C. CODING QUALITATIVE
DATA
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Qualitative Data Management And
Organization
Coding Qualitative Data
– Once the category scheme has been developed, the
data are read in their entirety and coded for
correspondence to the categories-a task that is seldom
easy.
– Researchers may have difficulty deciding the most
appropriate code, or may not fully comprehend the
underlying meaning of some aspect of the data.
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Coding of qualitative data
– Researchers often discover during coding that the initial
categories were incomplete.
– It is common for categories to emerge that were not
initially identified.
– A concept might not be identified as salient until it has
emerged a few times.
– Making changes midway is often vexing, but a
comprehensive category system is vital.
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Coding of qualitative data
– Another issue is that narrative materials usually are
not linear.
– For example, paragraphs from transcribed interviews
may contain elements relating to three or four different
categories, embedded in complex fashion.
– It is also recommended that a single person code the
entire data set to ensure the highest possible coding
consistency across interviews or observations.
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D. MANUAL METHODS OF
ORGANIZING QUALITATIVE
DATA
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process
Manual methods of organizing qualitative data:
– Traditional manual methods of organizing qualitative
data are becoming less common as a result of
widespread use of software that can perform indexing
functions.
– When a category is simple, researchers sometimes
use colored paper clips or Post-It notes to code
narrative content.
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Manual methods of organizing
qualitative data:
• For example, if we were analyzing interviews about
women's concerns about the menopause, we might use
blue paper clips for text relating to loss of fertility, red
clips for the text on menopausal side effects, yellow clips
for text relating to aging and so on.
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E. COMPUTER PROGRAMS FOR
MANAGING QUALITATIVE DATA
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organization
Computer Programs For Managing Qualitative
Data
– Computer assisted qualitative data analysis software
removes the work of cutting up pages of narrative
material.
– These programs allows the researchers to enter the
entire data file into the computer , code each portion
of the narrative and then retrieve and display the text
for specified code for analysis.
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Computer Programs For Managing
Qualitative Data
• The software can also be used to examine the
relationship between the codes.
• Software cannot, however, do the coding, and it cannot
tell researchers how to analyze the data.
• Researchers must continue to be analysts and critical
thinkers.
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Qualitative Data
• The main types of software package that are
available to handle and manage qualitative data
include:
– Text retrievers are the programs that help researchers
locate text and terms in data bases and documents.
– Code and retrieve packages permit the researchers to
code text.
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Qualitative Data
– Theory building software, permits the researchers to
examine the relationships between the concepts,
develop hierarchies of codes, diagram, and create
hyperlinks to create nonhierarchical networks.
– Software for concept mapping permits researchers to
construct more sophisticated diagrams than theory
building software.
– Concept maps include concepts and relationships
between them.
– cmc2004-060.pdf
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Qualitative Data
– Data conversion/collection software converts audio
into text.
– Voice recognition software can convert spoken voice
into text and is attractive because of the time and
expense needed to transcribe audiotape interviews.
• Computer programs offer many advantages for
managing qualitative data, but some prefer manual
methods because they allow researchers to get closer to
the data.
• Proponents insists that it frees up time and permits to
pay attention to important conceptual issues. 36
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ATLAS/ti (software for qualitative analysis) http://www.atlasti.de/
Computer Assisted Qualitative Analysis
(CAQDAS) Networking Project
http://caqdas.soc.surrey.ac.uk/
Ethnograph, The (software for qualitative
analysis)
http://www.qualisresearch.com/
Decision Explorer (software for mapping
concepts)
http://www.banxia.com/demain.ht
ml
Dragon Naturally Speaking (Voice recognition
software bypassing transcription)
http://www.nuance.com/
Freedom of Speech (Voice recognition software
for bypassing transcription)
http://www.freedomofspeech.com/f
os/
HyperResearch (software for qualitative analysis) http://www.researchware.com/
Institute for Human and Machine Cognition
(IHMC)
http://www.ihmc.us
International Institute for Qualitative
Methodology
http://www.uofaweb.ualberta.ca/iiq
m/
NVivo (software for qualitative analysis) http://www.datasense.org/
QUALPAGE (Resources for qualitative
researchers, )
http://www.qualitativeresearch.uga.
edu/QualPage/ 37

Analytic procedures
• Data management in qualitative research is reductionist
in nature: it involves converting masses of data into
smaller , manageable segments.
• Qualitative data analysis involves pervasive ideas and
searching for general concepts through an inductive
process.
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Analytic procedures
• The analysis of qualitative materials typically begins with
a search for broad categories or themes.
• According to desantis and ugarriza(2000) :" a theme is
an abstract entity that brings meaning and identity to a
current experience and its variant manifestations. As
such, a theme captures and unifies the nature or basis
of the experience into a meaningful whole"
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Analytic procedures
• Thematic analysis relies on similarity and dissimilarity
principle.
• The similarity principle involves looking for units of
information with the similar contents, symbols or
meanings.
• The contrast principle guides efforts to find out how
content or symbols differ from other content or symbols-
that is, to identify what is distinctive about the emerging
themes or categories.
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Analytic procedures
• During the analysis, qualitative researchers must
distinguish between the ideas that apply to all people
and aspects of experience that are unique to particular
participants
• The analysis of individual cases " enables the researcher
to understand those aspects of experience that occur not
as individual 'unit of meaning' but as part of the pattern
formed by the confluence of the meaning within the
individual accounts”.
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Analytic procedures
• Thematic analysis involves not only discovering
commonalities across participants but also seeking
natural variation.
• Researchers must attend not only to what themes arise,
but also how they are patterned.
– Does the theme apply only to certain types of people?
– In certain contexts?
– At certain periods?
– What are the conditions that precede the observed
phenomenon? And
– What are the apparent consequences of it?
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Analytic procedures
• In other words, the qualitative analyst must be sensitive to
relationships within the data.
• Researchers' search for themes and patterns sometimes
can be facilitated by charting devices that enable them to
summarize the evolution of the behaviors, events and
processes.
• For example, for qualitative studies that focus on dynamic
experiences-such as decision making-it is sometimes
useful to develop flow charts or timelines that highlight
time sequences , major decision points and events factors
affecting the decisions.
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Analytic procedures
• berends.pdf
• Two-dimensional matrices to array thematic material is
another frequently used method of displaying thematic
material.
• Traditionally , each row of a matrix is allocated to
individual participants and columns are used to enter
either raw data or themes.
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Analytic procedures
• Thematic Chart
• Case Chart
Case 1 Case 2 Case 3 etc…
Theme
Theme 1 Theme 2 Theme 3 etc…
Case
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Analytic procedures
• Some qualitative researchers -especially
phenomenologist -use metaphors as an analytic
strategy.
• A metaphor is a symbolic comparison, using figurative
language to evoke a visual analogy.
• Metaphors can be powerfully expressive tool for
qualitative analysts.
• Moser.pdf
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Analytic procedures
• Identifying key themes and categories is seldom a tidy,
linear process- iteration is always necessary.
• That is , researchers drive themes from the narrative
materials , go back to the materials with the themes in
mind to see if the materials really do fit and then refine
the themes as necessary.
• A further step involves validation. In this phase, the
concern is whether the themes accurately represent the
perspectives of participants.
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Analytic procedure
• In the final analysis stage, researchers strive to weave
thematic pieces together into an integrated whole .
• The various themes need to be interrelated to provide an
overall structure (such as theory or integrated
description) to the data.
• The integration task is a difficult one, because it
demands creativity and intellectual rigor if it is to be
successful
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Qualitative Data Analysis
• It isn’t always necessary to go through all the stages, just
as it isn’t always necessary to use multivariate modeling
in statistics!
• Let us take the example of the research question about
the perceived health needs of carers.
– What are the perceptions of carers living with people
with learning disability, as regards their own health
needs?
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Qualitative Data Analysis
• We may simply be interested in finding out the
community services that should be provided to meet
these perceived needs or might want to know what
sorts of services are valued or requested by the majority
of carers.
• Maybe several respondents mention that they struggle
with depression and loneliness.
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• There are three broad levels of analysis that
could be pursued here:
– One strategy would be to simply count the number of
times a particular word or concept occurs (eg
loneliness) in a narrative.
– The qualitative data can then be categorized
quantitatively, and subjected to statistical analysis.
– This kind of analysis is sometimes called qualitative
content analysis.
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• For a thematic analysis we would want to go deeper than
this.
• All units of data(e.g. sentences or paragraphs) referring
to loneliness could be given a particular code, extracted
and examined in more detail.
– Do participants talk of being lonely even when others
are present?
– Are there particular times of day or week when they
experience loneliness?
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– In what terms do they express loneliness?
– Do men and women talk of loneliness in different
ways?
– Are those who speak of loneliness also those who
experience depression?
– Themes could eventually be developed such as
‘lonely but never alone’ or ‘these four walls’.
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• For a theoretical analysis such as grounded theory we
would want to go further still.
• Perhaps we have developed theories if we have been
analyzing data about depression being associated with
perceived loss of a ‘normal’ child/spouse.
• The disability may be attributed to an accident, or to some
failure of medical care, without which the person cared for
would still be ‘normal’.
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• We may be able to test this emerging theory against
existing theories of loss in the literature, or against
further analysis of the data.
• We may even search for ‘deviant cases’, that is data
which seems to contradict theory, and seek to modify
theory to take account of this new finding.
• This process is sometimes known as ‘analytic induction’,
and is used to build and test emerging theory
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• So some decisions have to be made by the researcher
as to the questions she or he is asking of the data, and
the depth of analysis that is required.
• It may even come down to the amount of time available,
or ease of access to adequate resources.
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Analytic Procedures
• Qualitative content analysis:
– Qualitative content analysis is the analysis of the
content of narrative data to identify prominent themes
and pattern among the themes.
– It involves breaking down the data into smaller units,
coding and naming the unit according to the content
they represent, and grouping coded material based
on shared concepts.
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Ethnographic Analysis
Ethnographers are continually looking for the
patterns in the behavior and thoughts of
participants, comparing one pattern against
another, analyzing many patterns
simultaneously
As they analyze patterns of everyday life,
ethnographers acquire deeper understandings
of the culture being studied.
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Ethnographic analysis
• Maps, flowcharts and organizational charts are
useful tools that help to crystallize and illustrate
the data.
• Matrices(two dimension displays ) can also help
to highlight a comparison graphically, to cross-
reference categories and to discover emerging
patterns.
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Locating an
informant
Interviewing
an informant
Making an
ethnographic
record
Asking
descriptive
questions
Analyzing
ethnographic
interviews
Making a
domain
analysis
Asking
structural
questions
Making
taxonomic
analysis
Asking
contrast
questions
Making
componential
analysis
Discovering
cultural
themes
Writing the
ethnography
SPRADLEY’S METHOD
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Ethnographic Analysis
• Thus in Spradley's methods there is four levels
of data analysis, the first of which is domain
analysis.
• Domains, which are the units of cultural
knowledge, are the broad categories that
encompass smaller ones.
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• During this first level of data analysis,
ethnographers identify the relational patterns
among the terms in the domains that are used
by members of the culture.
• The ethnographers focuses on the cultural
meaning of terms and symbols(objects and
events) used in culture and their
interrelationships.
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• In taxonomic analysis, ethnographic decides
how many domains the analysis will encompass.
• After making this decision, a taxonomy-a system
of classifying and organizing terms-is developed
to illustrate the internal organization of a domain
and the relationship among the subcategories of
the domain.
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• In componential analysis, relationships among
the terms in the domains are examined. The
ethnographer analyze the data for similarities
and differences among the cultural terms in a
domain.
• Finally, in theme analysis, cultural themes are
uncovered.
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• Domains are connected in cultural themes,
which help to provide holistic view of culture
being studied. The discovery of cultural meaning
is the outcome.
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The culture of general palliative nursing
care in medical departments:
an ethnographic study.
• Bergenholtz H1, Jarlbaek L, Hølge-
Hazelton B.
• BACKGROUND:
– In many countries, approximately half of the
population dies in hospital, making general palliative
nursing care (GPNC) a core nursing task. GPNC in
the hospital setting is described as challenging,
however little is known about its actual practice.
AIM:
– To explore the GPNC culture in medical departments.
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• METHODS:
– An ethnographic study, using Spradley's 12-
step method, with observational field studies and
interviews with nurses from three medical departments in a
Danish regional hospital.
• FINDINGS:
– Three cultural themes emerged from the analysis, focusing
on the setting, the practice and the nurses' reflections on
GPNC:
– (1) GPNC provided in a treatment setting,
– (2) transition to loving care and the licence to perform
palliative care (PC) and
– (3) potential for team improvement.QUALITATIVE DATA ANALYSIS AND
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An ethnography: Understanding
emergency
nursing practice belief systems
• An ethnography_ Understanding
emergency nursing practice_LoBiondo.pdf
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Phenomenological analysis
• Three frequently used methods for
phenomenology are the methods of
Colaizzi(1978), Giorgi(1985), and vankaam
(1966), all of whom are from the duquesne
school of phenomenology.
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• Phenomenological analysis using all three
methods involves a search for common patterns
, but there are some important differences
among these approaches
• The basic outcome of all three methods is the
description of the meaning of an experience,
often through the identification of essential
themes.
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• Colaizzi method is only one that calls for a
validation of results by returning to study
participants.
• Giorgi analysis relies solely on researchers. His
view is that it is inappropriate either to return to
participants to validate findings or to use
external judges to review analysis.
• Van Kaam's method requires that intersubjective
agreement be reached with other expert judges.71
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Step 1
Read
written
protocol
Step 2
Extract
Significant
statements
Step 3
Formulate
meanings for
each significant
statement
Step 4
Extract
Significant
statements
Step 5
Integrate
results into
exhaustive
description
of the
phenomeno
n
Step 6
Formulate
exhaustive
description
into statement
of
identification
of its
fundamental
structure
Step 7
Return to participants
for validation of
Step 8
(if
necessary
)relevant
new data
are
worked
into final
product of
research
Repeat Steps 1-3 for each
protocol
Refer back to
original protocols
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Example of Phenomenological
Study using Colaizzi’s Method
• Supporting hemodialysis patients_ A
phenomenological study.epub
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Step 1
Statements
of patients
read
Step 2
Key phrases
were
extracted
Step 3
Each significant
statements were
written in
scientific
language
Step 4
Concepts were
organized into
thematic
categories
Step 5
Findings
were
integrated
into
comprehensi
ve description
of the desired
phenomenon
Step 6
Description of
the
investigated
phenomenon
was
presented in
the form of an
explicit and
clear
statement
Step 7
Findings were returned to
the participants and were
evaluated
Supporting Hemodialysis
Patients:
A phenomenological Study
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Grounded theory analysis
• Grounded theory methods emerged in the 1960s
in connection with Glaser and Strauss's (1967)
research program on dying in hospitals.
• The two co-originators eventually split and
developed divergent school of thought , which
have been called the "Glaserian" and
"Straussian" version of grounded theory.
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Glaser and Strauss's grounded
theory method
• Grounded theory in both systems of analysis
uses the constant comparative method of
analysis.
• This method involves a comparison of elements
present in one data source(e.g. In one interview)
with those in another to determine if they are
similar.
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theory method
• The process continues until the content of each
source has been compared to the content in all
sources. In this fashion, commonalities are
identified.
• The concept of fit is an important element in the
Glaserian grounded theory analysis.
• By fit, Glaser meant that the developing
categories of the substantive theory must fit the
data. 77
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theory method
• Fit enables the researcher to determine if data can
be placed in the same category or if they can be
related to one another.
• Coding in the Glaserian approach is used to
conceptualize data into patterns.
• The substance of the topic under study is
conceptualized through substantive codes, while the
theoretical codes provide insights into how codes
relate to each other.
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Coding
Substantiv
e
Open
Level I
Level II
Level III
Selective
Theoretica
l
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Core
Categorie
s

theory method
• Substantive codes are either open or selective.
• Open coding used in the first stage of constant
comparative analysis captures what is going on
in the data.
• Open codes may the actual words used by the
participants.
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theory method
• Through the open coding , data are broken
down into incidents and their similarities and
differences are examined.
• During the open coding, researcher might ask"
what category or the property of the category
does this incident indicate?“
• There are three level of open coding that vary in
the degree of abstraction.
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theory method
• Level 1 codes(in vivo codes) are derived
directly from the language of the substantive
area and have vivid imagery.
• Researchers constantly compare new level I
codes to previously identified ones, and then
condense them into broader level II codes.
e.g. : These 5 level I codes were collapsed into
level II code as “Reaping the Blessing”
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theory method
• Level III codes (theoretical constructs) are the
most abstract. Collapsing level II codes aids in
identifying constructs.
• Open coding ends when the core category is
discovered and then selective coding begins.
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theory method
• The core category is a pattern of behavior that is
relevant and/or problematic for participants.
• In the selective coding, researcher code only
those data that are related to the core variable.
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INTERPRETATION

theory method
• Glaser (1978) provided nine criteria to help
researchers decide on a core category:
– It must be central, meaning that it is related to many
categories.
– It must reoccur frequently in the data.
– It takes more time to saturate than other categories.
– It relates meaningfully and easily to other categories.
86
INTERPRETATION

theory method
– It has clear and grabbing implications for formal
theory.
– It is completely variable.
– It is a dimension of the problem.
– It can be kind of theoretical code.
87
INTERPRETATION

theory method
• Glasers' grounded theory method is concerned
with the generation of categories and hypothesis
rather than testing them.
• The product of typical grounded theory analysis
is a theoretical model that endeavors to
generate " a theory of continually resolving the
main concern, which explain most of the
behavior in an area of interest."
88
INTERPRETATION

theory method
• Once the basic problem or central concern
emerges, the grounded theorists goes on to
discover the process these participants
experience in coping with or resolving this
problem.
89
INTERPRETATION

Strauss and Corbin's approach
• The Strauss and Corbin approach to grounded
theory analysis, differs from the Glaser and
Strauss method with regard to method,
processes, and outcomes.
• Glaser stressed that to generate a grounded
theory, the basic problem must emerge from the
data-it must be discovered.
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INTERPRETATION

• The theory is , from the very start, grounded in
the data, rather than starting with a
preconceived problem.
• Strauss and Corbin stated that research itself is
only one of the possible sources of a research
problem.
• Research problem can come from literature or a
researcher's personal and professional
experience 91
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• The Corbin and Strauss method involves two
types of coding: open and axial coding.
• In open coding, data are broken down into parts
and concepts are identified and their properties
and dimension are delineated.
• In axial coding, the analyst relates concepts to
each other.
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INTERPRETATION

Strauss and Corbin's
approach
• The first step in integrating the findings is to
decide on the central category(sometimes called
the core category), which is the main theme of
the research.
• Techniques to facilitate the central category are
writing the storyline, using diagrams, and
reviewing and organizing memos.
93
INTERPRETATION

• Grounded Theory_Evolving Self-Care in
Individuals with Schizophrenia and
Diabetes Mellitus.pdf
INTERPRETATION
94

Focus Group Data Analysis:
• Focus group interviews yield rich and complex
data that pose special analytic challenges.
• Focus group interviews are especially difficult to
transcribe , partly because of technical
problems.
95
INTERPRETATION

Focus Group Data Analysis:
• For example, it is difficult to place microphones
so that the voices of all group members are
picked up with equal clarity , particularly
because the participant tend to speak at different
volumes.
• An additional issue is the inevitability that
several participants will speak at once, making it
impossible for the transcriptionist to discern
everything being said.
96
INTERPRETATION

Focus group data analysis
A controversial issue in the analysis of the focus
group data is whether the unit of analysis is
individual or group.
Some writer maintain that group is the proper unit
of analysis.
Analysis of group-level data involves scrutiny of
themes, interactions and sequences within and
between groups.
97
INTERPRETATION

• Others, however argued that analysis should
occur at both the group and individual level.
• Those who insist on only group level analysis
argue that what individuals say in focus group
cannot be treated as personal disclosures
because they are inevitably treated influenced
by the dynamics of the group.
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INTERPRETATION

• For those who wish to analyze data from
individual participants, it is essential to maintain
information about what each person did-a task
that is not possible if researcher rely solely on
audiotapes
• Videotapes, as supplements to audiotapes, are
sometimes used to identify who said what in
focus group sessions.
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INTERPRETATION

• Transcription quality is especially important in
focus group interviews.
• Emotional content as well as words must be
faithfully recorded because participants are
responding not only to questions being posed,
but also to the experience of being in the group.
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INTERPRETATION

• Field notes, debriefing notes and verbatim
transcripts ideally must be integrated to yield a
comprehensive transcript for analysis.
• Because of group dynamics, focus group
analysts must be sensitive to both the thematic
content of these interviews, and also to how,
when, and why themes are developed.
101
INTERPRETATION

• Some of the issues that could be central to focus
group analysis are the following:
– Does an issue raised in a focus group
constitute a theme or merely a strongly held
viewpoint of one or two members?
– Do the same issues or themes arise in more
than one group?
102
INTERPRETATION

– If there are group differences, why might this
be the case-were participants different in
characteristics and experiences or did group
processes affect the discussions?
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INTERPRETATION

– Are some issues sufficiently salient that not
only are they discussed in response to
specific questions posed by the moderator ,
but also spontaneously emerge at multiple
points in the session?
– Do group members find certain issues both
interesting and important?
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INTERPRETATION

Interpretation of Qualitative
findings
• Interpretation and analysis of qualitative data
occur simultaneously, in an iterative process.
• That is, researcher interpret the data as they
read and re-read them , categorize and code
them, inductively develop a thematic analysis,
and integrate the themes into unified whole.
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INTERPRETATION

findings
• Incubation is the process of living the data in
which the researchers must try to understand
their meanings , find their essential patterns ,
and draw legitimate , insightful conclusions.
• Another key ingredient in interpretation and
meaning making is researchers' self awareness
and ability to reflect on their own world view and
perspectives- that is reflexivity.
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INTERPRETATION

findings
• Creativity also plays important roles in
uncovering meaning of the data.
• Efforts to validate analysis are necessarily
efforts to validate the interpretations as well.
• Prudent qualitative researchers hold their
interpretations up for closer scrutiny as well as
review by peers and outside reviewers.
107
INTERPRETATION

Critiquing qualitative Analysis
• Evaluating a qualitative analysis in a report is
not easy to do, even for experienced
researchers.
• The main problem is that readers do not
– have access to the information they would need to
determine whether the researchers exercised good
judgment and critical insight in coding the narrative
materials
– developing the thematic analysis and integrating
materials into meaning whole. 108
INTERPRETATION

• Researchers are seldom able to include the
handful of data in a journal article.
• Moreover, the process they used to abstract
meaning from the data is difficult to describe and
illustrate.
• In a critique of qualitative analysis, a primary
task usually is assessing whether researchers
took sufficient steps to validate inferences and
conclusions. 109
INTERPRETATION

• A major focus of critique, then, is whether the
researchers adequately documented the analytic
process.
• The report should provide information about the
approach used to analyze the data.
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INTERPRETATION

• One aspect of a qualitative analysis that can be
critiqued, however is, whether the researchers
documented that they have used one approach
consistently and have been faithful to the
integrity of its procedures.
• Thus, for example, if researchers say they are
using the Glaser and Strauss approach to
grounded theory analysis, they should not also
include elements from Strauss and Corbin
method. 111
INTERPRETATION

• Which of the following best describes the
term themes? (Select all that apply.)
a) A theme is a label.
b) Themes must be determined before data
analysis.
c) Themes describe large quantities of data in
a condensed format.
d) Themes predict relationships among
variables.
Ans: a, c
INTERPRETATION
112

• The nurse researcher identifies that
saturation has occurred in a research study.
On the basis of this, what does the
researcher conclude?
a. That additional subjects should be interviewed
b. That a new category of subjects should be
interviewed
c. That no additional subjects need to be
identified
d. That additional data can emerge from current
interviews
Ans: c QUALITATIVE DATA ANALYSIS AND
INTERPRETATION
113

References
• (2012)Qualitative data analysis. In: Polit, D.F., Beck, C.T.
(eds.). Nursing Research: Generating and Assessing
Evidence for Nursing Practice. 9th Edition. New Delhi:
Wolters Kluwer India Pvt. Ltd.
• United Kingdom. National Health Services. (2007)
Qualitative Research Analysis. London: The NIHR RDS
for the East Midlands / Yorkshire & the Humber.
• www.pubmed.com
• www.googlescholar.com
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INTERPRETATION

Qualitative Data Analysis and Interpretation

Qualitative Data Analysis and Interpretation

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