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International Journal of Studies 49 (2012) 360–371

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UAGOL: A guide for qualitative data analysis

rnadette Dierckx de Casterlé a,*, Chris Gastmans b, Els Bryon c, Yvonne Denier b

entre of Health Services and Research, Faculty of Medicine, Catholic University of Leuven, Kapucijnenvoer 35 blok d – bus 7001, 3000 Leuven, Belgium

entre for Biomedical Ethics and , Faculty of Medicine, Catholic University of Leuven, Kapucijnenvoer 35 blok d – bus 7001, 3000 Leuven, Belgium

entre for Biomedical Ethics and & Centre for Health Services and Research, Faculty of Medicine, Catholic University of Leuven, Kapucijnenvoer 35

k d- bus 7001, 3000 Leuven, Belgium

What is already known about the topic?

Qualitative data analysis is a complex and challenging
part of the research process which has received only
limited attention in the research literature.

� During the analysis process of qualitative data, quite a lot
of researchers are struggling with problems that com-
promise the trustworthiness of the research findings.
� There is a lack of guidelines on how to analyze the mass

of qualitative interview data.

What this paper adds

� A theory- and practice-based guide that supports and
facilitates the process of analysis of qualitative interview
data.

R T I C L E I N F O

icle history:

ceived 27 June 2011

ceived in revised form 14 September 2011

cepted 16 September 2011

ywords:

alitative research

alysis

erview data

A B S T R A C T

Background: Data analysis is a complex and contested part of the qualitative research

process, which has received limited theoretical attention. Researchers are often in need of

useful instructions or guidelines on how to analyze the mass of qualitative data, but face

the lack of clear guidance for using particular analytic methods.

Objectives: The aim of this paper is to propose and discuss the Qualitative Analysis Guide

of Leuven (QUAGOL), a guide that was developed in order to be able to truly capture the

rich insights of qualitative interview data.

Method: The article describes six major problems researchers are often struggling with

during the process of qualitative data analysis. Consequently, the QUAGOL is proposed as a

guide to facilitate the process of analysis. Challenges emerged and lessons learned from

own extensive experiences with qualitative data analysis within the Grounded Theory

Approach, as well as from those of other researchers (as described in the literature), were

discussed and recommendations were presented. Strengths and pitfalls of the proposed

method were discussed in detail.

Results: The Qualitative Analysis Guide of Leuven (QUAGOL) offers a comprehensive method

to guide the process of qualitative data analysis. The process consists of two parts, each

consisting of five stages. The method is systematic but not rigid. It is characterized by iterative

processes of digging deeper, constantly moving between the various stages of the process. As

such, it aims to stimulate the researcher’s intuition and creativity as optimal as possible.

Conclusion: The QUAGOL guide is a theory and practice-based guide that supports and

facilitates the process of analysis of qualitative interview data. Although the method can

facilitate the process of analysis, it cannot guarantee automatic quality. The skills of the

researcher and the quality of the research team remain the most crucial components of a

successful process of analysis. Additionally, the importance of constantly moving between

the various stages throughout the research process cannot be overstated.

� 2011 Elsevier Ltd. All rights reserved.

Corresponding author.

E-mail address: [email protected]

. Dierckx de Casterlé).

Contents lists available at SciVerse ScienceDirect

International Journal of Studies

journal homepage: www.elsevier.com/ijns
20-7489/$ – see front matter � 2011 Elsevier Ltd. All rights reserved.
i:10.1016/j.ijnurstu.2011.09.012

http://dx.doi.org/10.1016/j.ijnurstu.2011.09.012

mailto:[email protected]

http://www.sciencedirect.com/science/journal/00207489

http://dx.doi.org/10.1016/j.ijnurstu.2011.09.012

�

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B. Dierckx de Casterlé et al. / International Journal of Studies 49 (2012) 360–371 361
An experience-based and detailed description of the
strengths and pitfalls of the Qualitative Analysis Guide of
Leuven (QUAGOL).

. Introduction

Imagine, a study about nurses’ involvement in euthana-
ia.1 The data are collected through in-depth interviews
ith nurses having experience in the care for patients

equesting euthanasia. The first respondent is a man,
orking in a neutral hospital, with a positive attitude
ward euthanasia. He has 10 years of experience in

ncology care and has been involved in 8 euthanasia cases.
he man speaks fluently and with conviction about the
ubject. ‘Respecting the patient’s euthanasia request’ seems

be the main focus of his care. The most important role of
e nurse, in his opinion, is to gain absolutely certainty that
e euthanasia request is really what the patient wants.

ubsequently, the nurse must be sure that all the necessary
teps of the procedure are taken. He tells you that the
ospital protocol serves as checklist, which is for him the
ost important instrument in the euthanasia care process.

The second respondent is a woman, working in a
eutral hospital. She also has a positive attitude toward
uthanasia. She has 5 years of experience on a geriatric
are ward and has been involved in 3 euthanasia cases.
ere, you are confronted with a quite different story. The
urse tells you how important it is for her to be able to
nderstand the patient’s request. Her most important
oncern is: what is the right attitude for me in guiding and
upporting the patient and the patient’s family through

is process? How should I be? Her primary focus in the
are for these patients is to show respect for the patient as
erson in the broad sense (a person with a specific
haracter, particular life history, own wishes, fears, coping
trengths and relationships). She describes in detail how
he enters into a close and personal relationship with
atients and their family in order to create a communica-
onal atmosphere, within which she helps them spend
eir final days together in a good way.
A next respondent, again a man, working in a catholic

ospital, with a negative attitude toward euthanasia. He
as 5 years of experience in a palliative support team and
as been involved in 12 euthanasia cases. This time, you
ear an emotional story, underlining the emotional
tensity of being involved in euthanasia. Caring for a

atient requesting euthanasia is intense, difficult and
rave, according to this nurse. ‘Truly helping the patient to
ie serenely’ is the central message in his story. ‘As a nurse I
ust do everything in my power to contribute to this’, he
lls you in the interview. His story makes clear that a

uthanasia care process is only successful when everyone
volved is able to make one’s peace with the situation.

The next participant is a woman, working in a neutral
ospital. She has a pro-attitude and has 3 years of
xperience on an oncology unit; she has been involved

in 2 euthanasia cases. You are confronted with a young
nurse telling, again, a totally different story about nurses’
involvement in euthanasia. Her story is one about the
organization of care. ‘Caring for a patient requesting
euthanasia requires, first of all, an efficient, practical
organisation of care’, she tells you. According to this nurse,
the responsibility of the nurse is to find out what to ‘do’ to
make this care process successfully.

And you can go on. You are confronted with pages and
pages of interview data. Every respondent has his or her
own unique story that can help you understand the nurses’
involvement in euthanasia care processes. How to analyze
and interpret all these different data? How to understand
their meaning and draw legitimate conclusions? How to
grasp the essence of these data while protecting the
integrity of each story when responding to the research
question? These questions point to the real challenge of
qualitative data analysis.

Data analysis is a complex and contested part of the
qualitative research process, which has received limited
theoretical attention (Savage, 2000). Researchers are often
in need of useful instructions or guidelines on how to
analyze the mass of qualitative data, but face the lack of clear
guidance for using particular analytic methods (Hunter
et al., 2002; McCance et al., 2001). Most available guidelines
or checklists related to qualitative studies are critical
appraisal tools or focus on reporting qualitative research
such as the CASP (Public Health Resource Unit, 2006),
COREQ (Tong et al., 2007), Malterud’s guidelines (2001), and
McMaster Critical Review Form (Letts et al., 2007). They do
not provide researchers with clear instructions on how to
analyze, interpret and summarize qualitative data.

In trying to meet this need and fill this lack, we should
not, however, forget to be careful. For on the one hand,
there is growing consensus that understanding or using a
prescribed method of analysis is not enough to generate
new insights. Qualitative data analysis is very complex,
and any description of the practical aspects of the analysis
process runs the risk of oversimplification. There is no one
right way to work with qualitative data. Essentially,
qualitative data analysis is a process best ‘learnt by doing’
(Froggatt, 2001).

On the other hand, we need to bear in mind that the
‘Aha-erlebenis’, the moment where one makes meaning
beyond the facts, does not just happen out of the blue
(Hunter et al., 2002). No themes, categories, concepts or
theories will ‘emerge’ without the researcher who must
‘make it so’ (Sandelowski, 1995, p. 371). This requires
expertise in reading, thinking, imagining, conceiving,
conceptualizing, connecting, condensing, categorizing
and thereby creating a new storyline (Jennings, 2007).
This implies the development of ‘intellectual craftmanship’
(Mills, 1995/1978, p. 195) without which no valuable
qualitative work can be produced (Sandelowski, 1995).
Extensive preparation is required to open the researcher’s
mind to multiple meanings and perspectives and to lay the
groundwork for one to be creative (Hunter et al., 2002). In
qualitative research it is essential that we ask which
techniques or methods can be used to guide and support
researchers in this challenging intellectual process (Jen-
nings, 2007; Hunter et al., 2002).

1 The following examples are inspired by our studies about nurses’

volvement in euthanasia in Flanders, Belgium (Denier et al., 2009,

010a,b; Dierckx de Casterlé et al., 2010).

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B. Dierckx de Casterlé et al. / International Journal of Studies 49 (2012) 360–371362
Problem statement

The process of qualitative data analysis is an extensive
d challenging activity, confronting the researcher with
any problems. Based on the literature and on our own
periences with qualitative data analysis, we can discern

major problems researchers are often struggling with.

. Over-reliance on qualitative software packages

Figuring out what to do with the data once they are
llected is one of ‘the most paralyzing moments’ in
alitative analyses (Jennings, 2007; Sandelowski, 1995).
e data generated with qualitative methods are often
luminous, and researchers are faced with the challenge

grasping a sense of the whole, extracting significant
cts, distinguishing relevant themes, discovering the
eaning beyond the facts and ultimately reconstructing
e story of the respondents on a general, overarching and
nceptual level.
The problem of figuring out how to start the process of

alysis frequently results in researchers relying too heavily
qualitative software packages (Jennings, 2007). Over-

helmed by all the narrative material that they must work
, researchers often focus too quickly and exclusively on
ding the data and entering the codes into qualitative
ftware packages (Jennings, 2007). Researchers often do
t take the necessary time to read and reread the material,

back and reflect on what one has read, trying to grasp the
neral themes and storylines and coming to the necessary
a-erlebnis’ (Hunter et al., 2002). Software cannot decide
w to segment data or what codes to attach to these
gments, nor what data means (Sandelowski, 1995). An
tensive preparation of the coding work is required to open
e researcher’s mind to multiple meanings and perspec-
es (Hunter et al., 2002).

. Word overload due to line-by-line approaches

Another problem that often occurs in qualitative
alysis, is word overload, which is produced by line-
-line approaches to coding. In such cases, the researcher
taches labels to lines of data without a sense of the whole

of analytic direction. Consequently, these lines either
ve no meaning by themselves or have more meanings
an can be grasped by one label (Sandelowski, 1995). This
nd of coding is meaningless. It is analytically and
ntextually empty and produces nothing but fatigue
d frustration. The generalizations developed in qualita-
e analyses are embedded in the contextual richness of

dividual experience. Qualitative data management
ategies that depend solely on coding and sorting of

xts into units of like meaning will give up much of the
ry’s contextual richness (Ayres et al., 2003).

. Coding using a preconceived framework

Further, many researchers struggle with the dilemma of
hether or not to perform pure inductive coding or to code
e data with the help of preconceived notions (Bailey and
ckson, 2003). Using a preconceived framework runs the

risk of prematurely excluding alternative ways of organiz-
ing the data that may be more illuminating. As such, one
runs the risk of premature analytic closure, resulting from
a persistent (but often unconscious and unrecognized)
commitment to some a priori view of the subject under
investigation (Sandelowski, 1995).

2.4. Difficulty of retaining the integrity of each respondent’s

story

The feeling of losing the uniqueness of each of the
individual interviews is another problem in the analysis of
qualitative data (Bailey and Jackson, 2003). This is
characteristic for the analytical process, which does not
always respect the interviewees’ particular portrayal of
their stories. The analytical method segments the data,
thus limiting the researchers’ understanding of the
interviewee’s perspective. As such, it prevents them from
understanding and describing a participant’s experience in
its richness (Bailey and Jackson, 2003; Riessman, 1990).
The content of each interview is unique, differing from the
other interviews qua experiences, tone, emotional invol-
vement, physical involvement, etc. How to retain the
integrity of each respondent’s responses constitutes one of
the most important challenges that qualitative researchers
are faced with (Bailey and Jackson, 2003).

2.5. Full potential of data is not exploited

Next, the analysis does not always go beyond a mere
descriptive account. It does not always offer a thorough
interpretation or theoretical development, although the
use of a Grounded Theory Approach is reported. It happens
that explanation is oversimplified and the complexities of
the research phenomena are ignored, so that the ambi-
guities and diversities of the participants’ experiences are
not reflected in the final description (Froggatt, 2001). In
such cases, we meet research reports that present only lists
of themes and subthemes, but stop short of interpretation.
Here, the full potential of the data is not exploited. The
analysis does not offer a thorough interpretation of the
interviewee’s world, which clearly undermines the cred-
ibility of the results.

This type of merely descriptive presentation happens,
for instance, when the analysis is separated out as a
discrete activity without analogously undertaking an
iterative dialogue with the interview data. It also occurs
when deductive rather than inductive analysis is under-
taken or when too much emphasis is being placed upon
allowing the data to speak for themselves. In such cases,
we see papers that successively present large fragments
from interviews with little explanation or interpretation,
with no attempt to identify commonalities within the data,
and without clarification of the purpose of the quotes
(Froggatt, 2001).

2.6. Data analysis as individual process

Finally, conceiving the qualitative data analysis as an
individual process rather than a team process is also a
common problem among qualitative researchers, leading

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B. Dierckx de Casterlé et al. / International Journal of Studies 49 (2012) 360–371 363
personal frustration and little depth in the analysis.
unter et al. (2002) underscores the importance of viewing
ata from several perspectives facilitating multidimen-
ional thinking and offering different ways of making
eaning of the interview data. A team approach will

nhance the possibility to gain creative and thoughtful
sight in the research phenomenon. Jennings (2007) also

oints to the importance of mentors, rather than manuals,
guide the researcher in grasping the essence of the

esearch findings.
As qualitative researchers we experienced similar

ifficulties in analyzing qualitative interview data within
Grounded Theory Approach (Glaser and Strauss, 1999;
orbin and Strauss, 2008). The process of analysis, as well
s the guidance of young researchers in this process
onstitutes a real challenge. As such, we were triggered to
nd a method that could support researchers in the
nalytical process without imposing a rigid, detailed step-
y-step plan. We searched for a supporting guide that
akes researchers able to understand the meaning of the

ata in a consistent and scientific way, sufficiently based
n the use of intuition, imagination and creativity.

. Aim

The purpose of this article is to propose and discuss the
ualitative Analysis Guide of Leuven (QUAGOL), a guide
at we developed in order to be able to truly capture the

ich insights of qualitative interview data. The QUAGOL is
ased on our own experiences with qualitative research as
ell as on that of other researchers (as described in the

terature) and is inspired by the constant comparative
ethod of the Grounded Theory Approach (Corbin and

trauss, 2008). QUAGOL is proposed as a guide to facilitate
e process of qualitative data analysis.

. The Qualitative Analysis Guide of Leuven (QUAGOL)

The proposed method is comprehensive and systematic
ut not rigid; it offers space that stimulates the research-
r’s intuition and creativity as maximal and optimal as
ossible. The method gets the researcher out of his isolated
osition as the analysis process is predominantly con-
idered as a team activity rather than a purely individual
rocess.

The process of analysis consists of two parts: (1) a
orough preparation of the coding process and (2) the

ctual coding process using a qualitative software pro-
ram. Both parts consist of 5 stages which, for the purpose
f this article, are summarized artificially as discrete and
near stages. However, in reality, our method is char-
cterized by iterative processes of digging deeper, con-
tantly moving between the different stages (Froggatt,
001). The process of analysis immediately starts after the
rst interview has been conducted and continues till the
oint of data saturation has been reached.

The first part consists of a thorough preparation of the
oding process, implying only paper and pencil work. In

is part, the researcher and his team explicitly and
eliberately postpone the process of actual coding. As
andelowski (1995, p. 371) reports, ‘first look at your data

in order to see what you should look for in your data’. This
preparatory work is crucially important to develop a useful
and empirically based framework for the actual coding
process.

While the first part happens by paper and pencil work,
the stages of the second part require the use of qualitative
software, as we start with the actual coding process. Based
on the conceptual insights resulting from the previous
stages, a list of contextually and analytically meaningful
concepts is drawn up. It serves as a coding list for the actual
coding process allowing a systematic analysis of the
concepts based on empirical data. This part ends with an
empirically based description of the results. Fig. 1 offers a
schematic overview of the 10 stages in the process of data
analysis.

As the collection and analysis of data occurs simulta-
neously, both parts cannot be strictly separated. Newly
collected data, even at the end of the study, require that the
researchers go through the previous stages again, inevi-
tably resulting in partial overlap and interaction between
both parts of the process of analysis.

4.1. Preparation of the coding process

4.1.1. Stage 1: thorough (re)reading of the interviews

Every interview is meticulously transcribed verbatim
immediately by the interviewing researcher, including the
non-verbal signals. Additionally, a short report about the
interviewee’s and contextual characteristics of the inter-
view is made, helping the researcher to comprehend the
interview within its particular context. The transcript is
thoroughly read different times in order to familiarize with
the data and getting a sense of the interview as a whole.
What is this interview about? What does this participant
tell me that is relevant for the research question? As the
analysis is considered as a team process, the transcript is
also read by the other members of the research team. Each
interview is read as many times as necessary to apprehend
its essential features, without feeling pressured to move
forward analytically. During this reading process, the
researcher will underline key phrases, simply because they
make some, though yet embryonic, impression on him/her.
The meaning of some words or passages, as interpreted
tentatively by the researcher, thoughts or reflections
evoked by some passages are noted in the margins next
to the text. It is clear that a rudimentary kind of analysis
begins in this stage. Fig. 2 offers an example of the results
of the (re)reading process.

4.1.2. Stage 2: narrative interview report

Stage 1 results in a holistic understanding of the
respondent’s experience. In the second stage, the
researcher tries to phrase (articulate) this understanding.

The interview is read again and put aside. Then, the
researcher tries to articulate the essence of the inter-
viewee’s story in answer to the research question. The
writing of the narrative report is guided by the question:
‘What are the essential characteristics of the interviewee’s
story that may contribute to a better insight in the research
topic?’ The answer is described in a narrative way, using
the specific story of the interviewee. The narrative report

1. Thorough (re)reading of the interviews A holis�c understanding of the respondent’s experience

2. Narra�ve interview report A brief abstract of the key storylines of the interview

3. From narra�ve interview report to conceptual interview scheme Concrete experiences replaced by concepts

4. Fi�ng-test of the conceptual interview scheme Tes�ng the appropriateness of schema�c card in dialogue

5. Constant comparison process Forward-backwards movement between within -case and across -case analysis

ACTUAL CODING PROCESS (using qualita�ve so�ware)

PREPARATION OF CODING PROCES (paper and pencil work)

6. Draw up a list of concepts A common list of concepts as preliminary codes

7. Coding process – back to the ‘ground’ Linking all relevant fragments to the appropriate codes

8. Analysis of concepts Descrip�on of concepts, their meaning, dimensions & characteris�cs

9. Extrac�on of the essen�al structure Conceptual framework or story -line

10. Descrip�on of the results Description of the essential findings

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Fig. 1. Stages of the Qualitative Analysis Guide of Leuven (QUAGOL).

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B. Dierckx de Casterlé et al. / International Journal of Studies 49 (2012) 360–371 365
an include brief paraphrasing that stays close to the data,
ore abstract renderings of the data, or comments on the

arrative structure or interactional features of the inter-
iew event (Sandelowski, 1995). This stage results in a
rief abstract of the key storylines including a summary

pression of the characteristics of the interview.
It is suggested to start the second stage after some

terviews have been conducted and to select (in consulta-
on with the other team members) the interview that
ppears to provide the most ‘rich’ information, i.e. the most
aluable information to contribute to the research aim.

Focusing on the real essence of the story, it is suggested
limit the narrative report to one page. Analogously, all
embers of the team read the interviews and make

arrative interview reports, which are discussed during
e meetings of the research team.

.1.3. Stage 3: from narrative report to conceptual interview

cheme

While the narrative interview report provides a general,
arrative view of the essence of the interview, the
onceptual interview scheme provides concepts that
ppear relevant to get insight into the research topic. As
uch, the researcher makes a first move from the concrete
vel of experience to the conceptual level of the story.
oncrete experiences are being replaced by concepts
rising from these experiences. What has been told during
e interview and (narratively) described in the narrative
terview report is being brought to a more abstract and

onceptual level. The researcher distances from the
articularity of the interview and the narrative report,
y filtering the most important data and clustering them in

concepts. Which concepts grasp the essence of the
interview in response to the research question? All-
embracing concepts must be avoided in this stage as
one looks for manageable concepts that will guide the
coding process. The key concepts – those considered as
most characteristic for the interview – are highlighted;
they can help find the essential structure of the research
answer (see stage 9). The concepts are represented in a
scheme and, where necessary, clarified with respect to
their content (see Fig. 3).

The translation of the narrative report into a conceptual
interview scheme is a crucial preparatory stage for the
actual analysis of the data with the qualitative software as
this scheme will facilitate the transition from raw data to
manageable concepts. The concepts will be further
developed and refined as the researcher gets more insight
into the research phenomenon. We experienced these
schemes as an important analytic instrument to retain the
integrity of each respondent’s story. It also helps in keeping
track of the data as a whole, since every interview will have
its own conceptual interview scheme. After having
analyzed 20 interviews, one can easily go back and grasp
the essence of the first interview by looking into the
conceptual interview scheme of this interview. Further-
more, these schemes are also an important instrument of
communication within the research team, for they provide
the researchers with a strategy to support the trustworthi-
ness of the process of analysis.

We have observed that more experienced researchers
sometimes skip the second stage and immediately start
with the formulation of the conceptual interview scheme
after having read the interview.

Fig. 2. Example of the results of the (re)reading process.

4.1

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B. Dierckx de Casterlé et al. / International Journal of Studies 49 (2012) 360–371366
.4. Stage 4: Fitting-test of the conceptual interview

hemes

In stage 4, the appropriateness of the conceptual
terview schemes is being verified by iterative dialogue
ith the interview data. The researcher reread the interview
ith the conceptual interview scheme in mind. Two
estions need to be answered: (1) Does the content of
e conceptual interview scheme actually reflect the most
portant concepts in answer to the research question?
e there any other important concepts the researcher

overlooks? (2) Can the concepts of the conceptual interview
scheme be linked to the interview data? Through scrapping,
completion or reformulation, the conceptual interview
schemes are adapted, completed or refined.

Characteristic for this stage is that it represents the …