Thematic analysis is a poorly demarcated, rarely acknowledged, yet widely
used qualitative analytic method within psychology. In this paper, we
argue that it offers an accessible and theoretically flexible approach to
analysing qualitative data. We outline what thematic analysis is, locating it
in relation to other qualitative analytic methods that search for themes or
patterns, and in relation to different epistemological and ontological
positions. We then provide clear guidelines to those wanting to start
thematic analysis, or conduct it in a more deliberate and rigorous way, and
consider potential pitfalls in conducting thematic analysis. Finally, we
outline the disadvantages and advantages of thematic analysis. We
conclude by advocating thematic analysis as a useful and flexible method
for qualitative research in and beyond psychology. Qualitative Research in
Psychology 2006; 3: 77/101
Key words: epistemology; flexibility; patterns; qualitative psychology;
thematic analysis
Thematic analysis is a poorly demarcated
and rarely acknowledged, yet widely used
qualitative analytic method (Boyatzis,
1998; Roulston, 2001) within and beyond
psychology. In this paper, we aim to fill
what we, as researchers and teachers in
qualitative psychology, have experienced
as a current gap / the absence of a paper
which adequately outlines the theory, application
and evaluation of thematic analysis,
and one which does so in a way
accessible to students and those not particularly
familiar with qualitative research.1
That is, we aim to write a paper that will
be useful as both a teaching and research
tool in qualitative psychology. Therefore,
in this paper we discuss theory and
method for thematic analysis, and clarify
Correspondence: Virginia Braun, Department of Psychology, University of Auckland, Private Bag 92019, Auckland,
New Zealand.
E-mail: v.braun@auckland.ac.nz
2006 Edward Arnold (Publishers) Ltd 10.1191/1478088706qp063oa
www.QualResearchPsych.com Qualitative Research in Psychology 2006; 3: 77/101
the similarities and differences between
different approaches that share features in
common with a thematic approach.
Qualitative approaches are incredibly
diverse, complex and nuanced (Holloway
and Todres, 2003), and thematic analysis
should be seen as a foundational method
for qualitative analysis. It is the first
qualitative method of analysis that researchers
should learn, as it provides core
skills that will be useful for conducting
many other forms of qualitative analysis.
Indeed, Holloway and Todres (2003: 347)
identify ‘thematizing meanings’ as one of a
few shared generic skills across qualitative
analysis.2 For this reason, Boyatzis (1998)
characterizes it, not as a specific method,
but as a tool to use across different methods.
Similarly, Ryan and Bernard (2000)
locate thematic coding as a process performed
within ‘major’ analytic traditions
(such as grounded theory), rather than a
specific approach in its own right. We
argue thematic analysis should be considered
a method in its own right.
One of the benefits of thematic analysis is
its flexibility. Qualitative analytic methods
can be roughly divided into two camps.
Within the first, there are those tied to, or
stemming from, a particular theoretical or
epistemological position. For some of these
/ such as conversation analysis (CA; eg,
Hutchby and Wooffitt, 1998) and interpretative
phenomenological analysis (IPA; eg,
Smith and Osborn, 2003) / there is (as yet)
relatively limited variability in how the
method is applied, within that framework.
In essence, one recipe guides analysis. For
others of these / such as grounded theory
(Glaser, 1992; Strauss and Corbin, 1998),
discourse analysis (DA; eg, Burman and
Parker, 1993; Potter and Wetherell, 1987;
Willig, 2003) or narrative analysis (Murray,
2003; Riessman, 1993) / there are different
manifestations of the method, from within
the broad theoretical framework. Second,
there are methods that are essentially independent
of theory and epistemology, and
can be applied across a range of theoretical
and epistemological approaches. Although
often (implicitly) framed as a realist/experiential
method (Aronson, 1994; Roulston,
2001), thematic analysis is actually firmly
in the second camp, and is compatible with
both essentialist and constructionist paradigms
within psychology (we discuss this
later). Through its theoretical freedom, thematic
analysis provides a flexible and useful
research tool, which can potentially
provide a rich and detailed, yet complex,
account of data.
Given the advantages of the flexibility of
thematic analysis, it is important that we are
clear that we are not trying to limit this
flexibility. However, an absence of clear and
concise guidelines around thematic analysis
means that the ‘anything goes’ critique of
qualitative research (Antaki et al., 2002) may
well apply in some instances. With this
paper, we hope to strike a balance between
demarcating thematic analysis clearly / ie,
explaining what it is, and how to do it / and
ensuring flexibility in relation to how it is
used, so that it does not become limited and
constrained, and lose one of its key advantages.
Indeed, a clear demarcation of this
method will be useful to ensure that those
who use thematic analysis can make active
choices about the particular form of analysis
they are engaged in. Therefore, this paper
seeks to celebrate the flexibility of the
method and provide a vocabulary and
‘recipe’ for people to undertake thematic
analysis in a way that is theoretically and
methodologically sound.3 As we will show,
what is important is that as well as applying
a method to data, researchers make
their (epistemological and other) assump-
78 V Braun and V Clarke
tions explicit (Holloway and Todres, 2003).
Qualitative psychologists need to be clear
about what they are doing and why, and to
include the often-omitted ‘how’ they did
their analysis in their reports (Attride-
Stirling, 2001).
In this paper we outline: what thematic
analysis is; a 6-phase guide to performing
thematic analysis; potential pitfalls to
avoid when doing thematic analysis; what
makes good thematic analysis; and advantages
and disadvantages of thematic analysis.
Throughout, we provide examples
from the research literature, and our
own research. By providing examples, we
show the types of research questions and
topics that thematic analysis can be used to
study.
Before we begin, we need to define a few
of the terms used throughout the paper.
Data corpus refers to all data collected for
a particular research project, while data set
refers to all the data from the corpus that
are being used for a particular analysis.
There are two main ways of choosing the
data set (which approach you take depends
on whether you are coming to the data
with a specific question or not / see ‘A
number of decisions’ below). First, the data
set may consist of many, or all, individual
data items within your data corpus. So, for
example, in a project on female genital
cosmetic surgery, Virginia’s data corpus
consists of interviews with surgeons,
media items on the topic, and surgeon
websites. For any particular analysis, her
data set might just be the surgeon interviews,
just the websites (Braun, 2005b), or
it might combine surgeon data with some
media data (eg, Braun, 2005a). Second, the
data set might be identified by a particular
analytic interest in some topic in the data,
and the data set then becomes all instances
in the corpus where that topic is referred.
So in Virginia’s example, if she was interested
in how ‘sexual pleasure’ was talked
about, her data set would consist of all
instances across the entire data corpus that
had some relevance to sexual pleasure.
These two approaches might sometimes
be combined to produce the data set. Data
item is used to refer to each individual
piece of data collected, which together
make up the data set or corpus. A data
item in this instance would be an individual
surgeon interview, a television documentary,
or one particular website. Finally,
data extract refers to an individual coded
chunk of data, which has been identified
within, and extracted from, a data item.
There will be many of these, taken from
throughout the entire data set, and only a
selection of these extracts will feature in
the final analysis.
What is thematic analysis?
Thematic analysis is a method for identifying,
analysing and reporting patterns
(themes) within data. It minimally organizes
and describes your data set in (rich)
detail. However, frequently if goes further
than this, and interprets various aspects of
the research topic (Boyatzis, 1998). The
range of different possible thematic analyses
will further be highlighted in relation
to a number of decisions regarding it as a
method (see below).
Thematic analysis is widely used, but
there is no clear agreement about what
thematic analysis is and how you go about
doing it (see Attride-Stirling, 2001; Boyatzis,
1998; Tuckett, 2005, for other examples).
It can be seen as a very poorly
‘branded’ method, in that it does not appear
to exist as a ‘named’ analysis in the same
way that other methods do (eg, narrative
Using thematic analysis in psychology 79
analysis, grounded theory). In this sense, it
is often not explicitly claimed as the
method of analysis, when, in actuality, we
argue that a lot of analysis is essentially
thematic / but is either claimed as something
else (such as DA, or even content
analysis (eg, Meehan et al., 2000)) or not
identified as any particular method at all /
for example, data were ‘subjected to qualitative
analysis for commonly recurring
themes’ (Braun and Wilkinson, 2003: 30).
If we do not know how people went about
analysing their data, or what assumptions
informed their analysis, it is difficult to
evaluate their research, and to compare
and/or synthesize it with other studies on
that topic, and it can impede other researchers
carrying out related projects in the
future (Attride-Stirling, 2001). For these
reasons alone, clarity on process and practice
of method is vital. We hope that this
paper will lead to more clarity around
thematic analysis.
Relatedly, insufficient detail is often given
to reporting the process and detail of
analysis (Attride-Stirling, 2001). It is not
uncommon to read of themes ‘emerging’
from the data (although this issue is not
limited to thematic analysis). For example,
Singer and Hunter’s (1999: 67) thematic
discourse analysis of women’s experiences
of early menopause identified that ‘several
themes emerged’ during the analysis. Rubin
and Rubin (1995: 226) claim that analysis is
exciting because ‘you discover themes and
concepts embedded throughout your interviews’.
An account of themes ‘emerging’ or
being ‘discovered’ is a passive account of
the process of analysis, and it denies the
active role the researcher always plays in
identifying patterns/themes, selecting
which are of interest, and reporting them
to the readers (Taylor and Ussher, 2001).4
The language of ‘themes emerging’:
can be misinterpreted to mean that themes ‘reside’
in the data, and if we just look hard enough
they will ‘emerge’ like Venus on the half shell.
If themes ‘reside’ anywhere, they reside in
our heads from our thinking about our data and
creating links as we understand them. (Ely et al .,
1997: 205/6)
At this point, it is important to acknowledge
our own theoretical positions and values in
relation to qualitative research. We do not
subscribe to a naı¨ve realist view of qualitative
research, where the researcher can
simply ‘give voice’ (see Fine, 2002) to their
participants. As Fine (2002): 218) argues,
even a ‘giving voice’ approach ‘involves
carving out unacknowledged pieces of
narrative evidence that we select, edit,
and deploy to border our arguments’. However,
nor do we think there is one ideal
theoretical framework for conducting qualitative
research, or indeed one ideal method.
What is important is that the theoretical
framework and methods match what the
researcher wants to know, and that they
acknowledge these decisions, and recognize
them as decisions.
Thematic analysis differs from other analytic
methods that seek to describe patterns
across qualitative data / such as ‘thematic’
DA, thematic decomposition analysis, IPA
and grounded theory.5 Both IPA and
grounded theory seek patterns in the data,
but are theoretically bounded. IPA is attached
to a phenomenological epistemology
(Smith et al., 1999; Smith and Osborn,
2003), which gives experience primacy
(Holloway and Todres, 2003), and is about
understanding people’s everyday experience
of reality, in great detail, in order to
gain an understanding of the phenomenon
in question (McLeod, 2001). To complicate
matters, grounded theory comes in different
versions (Charmaz, 2002). Regardless, the
goal of a grounded theory analysis is to
generate a plausible / and useful / theory
80 V Braun and V Clarke
of the phenomena that is grounded in the
data (McLeod, 2001). However, in our experience,
grounded theory seems increasingly
to be used in a way that is essentially
grounded theory ‘lite’ / as a set of procedures
for coding data very much akin to
thematic analysis. Such analyses do not
appear to fully subscribe to the theoretical
commitments of a ‘full-fat’ grounded theory,
which requires analysis to be directed towards
theory development (Holloway and
Todres, 2003). We argue, therefore, that a
‘named and claimed’ thematic analysis
means researchers need not subscribe to
the implicit theoretical commitments of
grounded theory if they do not wish to
produce a fully worked-up grounded-theory
analysis.
The term ‘thematic DA’ is used to refer to
a wide range of pattern-type analysis of
data, ranging from thematic analysis within
a social constructionist epistemology (ie,
where patterns are identified as socially
produced, but no discursive analyse is
conducted), to forms of analysis very
much akin to the interpretative repertoire
form of DA (Clarke, 2005). Thematic decomposition
analysis (eg, Stenner, 1993; Ussher
and Mooney-Somers, 2000) is a specifically
named form of ‘thematic’ DA, which identifies
patterns (themes, stories) within data,
and theorizes language as constitutive of
meaning and meaning as social.
These different methods share a search
for certain themes or patterns across an
(entire) data set, rather than within a data
item, such as an individual interview or
interviews from one person, as in the case of
biographical or case-study forms of analysis,
such as narrative analysis (eg, Murray,
2003; Riessman, 1993). In this sense, they
more or less overlap with thematic analysis.
As thematic analysis does not require the
detailed theoretical and technological
knowledge of approaches, such as grounded
theory and DA, it can offer a more accessible
form of analysis, particularly for those early
in a qualitative research career.
In contrast to IPA or grounded theory (and
other methods like narrative analysis DA or
CA), thematic analysis is not wedded to any
pre-existing theoretical framework, and
therefore it can be used within different
theoretical frameworks (although not all),
and can be used to do different things
within them. Thematic analysis can be an
essentialist or realist method, which reports
experiences, meanings and the reality of
participants, or it can be a constructionist
method, which examines the ways in which
events, realities, meanings, experiences and
so on are the effects of a range of discourses
operating within society. It can also be a
‘contextualist’ method, sitting between the
two poles of essentialism and constructionism,
and characterized by theories, such as
critical realism (eg, Willig, 1999), which
acknowledge the ways individuals make
meaning of their experience, and, in turn,
the ways the broader social context impinges
on those meanings, while retaining
focus on the material and other limits of
‘reality’. Therefore, thematic analysis can be
a method that works both to reflect reality
and to unpick or unravel the surface of
‘reality’. However, it is important that the
theoretical position of a thematic analysis is
made clear, as this is all too often left
unspoken (and is then typically a realist
account). Any theoretical framework carries
with it a number of assumptions about the
nature of the data, what they represent in
terms of the ‘the world’, ‘reality’, and so
forth. A good thematic analysis will make
this transparent.
A number of decisions
Thematic analysis involves a number of
choices which are often not made explicit
Using thematic analysis in psychology 81
(or are certainly typically not discussed in
the method section of papers), but which
need explicitly to be considered and discussed.
In practice, these questions should
be considered before analysis (and sometimes
even collection) of the data begins,
and there needs to be an ongoing reflexive
dialogue on the part of the researcher or
researchers with regards to these issues,
throughout the analytic process. The
method section of Taylor and Ussher’s
(2001) thematic DA of S&M provides a
good example of research which presents
this process explicitly; the method section
of Braun and Wilkinson (2003) does not.
What counts as a theme?
A theme captures something important
about the data in relation to the research
question, and represents some level of
patterned response or meaning within the
data set. An important question to address
in terms of coding is: what counts as a
pattern/theme, or what ‘size’ does a theme
need to be? This is a question of prevalence,
in terms both of space within each data item
and of prevalence across the entire data set.
Ideally, there will be a number of instances
of the theme across the data set, but more
instances do not necessarily mean the
theme itself is more crucial. As this is
qualitative analysis, there is no hard-andfast
answer to the question of what proportion
of your data set needs to display
evidence of the theme for it to be considered
a theme. It is not the case that if it was
present in 50% of one’s data items, it would
be a theme, but if it was present only in
47%, then it would not be a theme. Nor is it
the case that a theme is only something that
many data items give considerable attention
to, rather than a sentence or two. A theme
might be given considerable space in some
data items, and little or none in others, or it
might appear in relatively little of the data
set. So, researcher judgement is necessary to
determine what a theme is. Our initial
guidance around this is that you need to
retain some flexibility, and rigid rules really
do not work. (The question of prevalence is
revisited in relation to themes and subthemes,
as the refinement of analysis (see
later) will often result in overall themes,
and sub-themes within those.)
Furthermore, the ‘keyness’ of a theme is
not necessarily dependent on quantifiable
measures / but rather on whether it captures
something important in relation to
the overall research question. For example,
in Victoria’s research on representations
of lesbians and gay parents on 26
talk shows (Clarke and Kitzinger, 2004),
she identified six ‘key’ themes. These six
themes were not necessarily the most prevalent
themes across the data set / they
appeared in between two and 22 of the 26
talk shows / but together they captured an
important element of the way in which
lesbians and gay men ‘normalize’ their
families in talk show debates. In this instance,
her thematic analysis was driven by
this particular analytic question. How she
‘measured’ prevalence is relevant, as prevalence
can be determined in a number of
different ways. Prevalence was counted at
the level of the data item (ie, did a theme
appear anywhere in each individual talk
show?). Alternatively, it could have been
counted in terms of the number of different
speakers who articulated the theme, across
the entire data set, or each individual
occurrence of the theme across the entire
data set (which raises complex questions
about where an ‘instance’ begins and ends
within an extended sequence of talk / see
Riessman, 1993). Because prevalence was
not crucial to the analysis presented, Victoria
chose the most straightforward form,
82 V Braun and V Clarke
but it is important to note there is no right or
wrong method for determining prevalence.
Part of the flexibility of thematic analysis
is that it allows you to determine themes
(and prevalence) in a number of ways. What
is important is that you are consistent in
how you do this within any particular
analysis.
There are various ‘conventions’ for representing
prevalence in thematic (and other
qualitative) analysis that does not provide a
quantified measure (unlike much content
analysis, Wilkinson, 2000) / for instance:
‘the majority of participants’ (Meehan et al.,
2000: 372), ‘many participants’ (Taylor and
Ussher, 2001: 298), or ‘a number of
participants’ (Braun et al., 2003: 249).
Such descriptors work rhetorically to
suggest a theme really existed in the data,
and to convince us they are reporting
truthfully about the data. But do they tell
us much? This is perhaps one area where
more debate is needed about how and why
we might represent the prevalence of
themes in the data, and, indeed, whether,
if, and why prevalence is particularly important.
A rich description of the data set, or a
detailed account of one particular aspect
It is important to determine the type of
analysis you want to do, and the claims
you want to make, in relation to your data
set. For instance, you might wish to provide
a rich thematic description of your entire
data set, so that the reader gets a sense of the
predominant or important themes. In this
case, the themes you identify, code, and
analyse would need to be an accurate reflection
of the content of the entire data set. In
such an analysis, some depth and complexity
is necessarily lost (particularly if you are
writing a short dissertation or article with
strict word limits), but a rich overall description
is maintained. This might be a
particularly useful method when you are
investigating an under-researched area, or
you are working with participants whose
views on the topic are not known.
An alternative use of thematic analysis is
to provide a more detailed and nuanced
account of one particular theme, or group of
themes, within the data. This might relate to
a specific question or area of interest within
the data (a semantic approach / see below),
or to a particular ‘latent’ theme (see below)
across the whole or majority of the data set.
An example of this would be Victoria’s talk
show paper, discussed previously (Clarke
and Kitzinger, 2004), which examined normalization
in lesbians’ and gay men’s accounts
of parenting.
The post Using thematic analysis in psychology appeared first on My Assignment Online.