Some of the phases of thematic analysis are
similar to the phases of other qualitative
research, so these stages are not necessarily
all unique to thematic analysis. The process
starts when the analyst begins to notice,
and look for, patterns of meaning and
issues of potential interest in the data /
this may be during data collection. The
endpoint is the reporting of the content
and meaning of patterns (themes) in the
data, where ‘themes are abstract (and often
fuzzy) constructs the investigators identify
[sic] before, during, and after analysis’
(Ryan and Bernard, 2000: 780). Analysis
involves a constant moving back and forward
between the entire data set, the coded
extracts of data that you are analysing, and
the analysis of the data that you are producing.
Writing is an integral part of analysis,
not something that takes place at the end, as
it does with statistical analyses. Therefore,
writing should begin in phase one, with the
jotting down of ideas and potential coding
schemes, and continue right through the
entire coding/analysis process.
There are different positions regarding
when you should engage with the literature
relevant to your analysis / with some
arguing that early reading can narrow your
analytic field of vision, leading you to focus
on some aspects of the data at the expense
of other potentially crucial aspects. Others
argue that engagement with the literature
can enhance your analysis by sensitizing
you to more subtle features of the data
(Tuckett, 2005). Therefore, there is no one
right way to proceed with reading for thematic
analysis, although a more inductive
approach would be enhanced by not engaging
with literature in the early stages of
analysis, whereas a theoretical approach
requires engagement with the literature
prior to analysis.
We provide an outline guide through the
six phases of analysis, and offer examples to
demonstrate the process.7 The different
phases are summarized in Table 1. It is
important to recognize that qualitative analysis
guidelines are exactly that / they are
not rules, and, following the basic precepts,
will need to be applied flexibly to fit the
research questions and data (Patton, 1990).
Moreover, analysis is not a linear process of
simply moving from one phase to the next.
Instead, it is more recursive process, where
movement is back and forth as needed,
throughout the phases. It is also a process
86 V Braun and V Clarke
that develops over time (Ely et al., 1997),
and should not be rushed.
Phase 1: familiarizing yourself with your
data
When you engage in analysis, you may have
collected the data yourself, or they may have
been given to you. If you collected them
through interactive means, you will come to
the analysis with some prior knowledge of
the data, and possibly some initial analytic
interests or thoughts. Regardless, it is vital
that you immerse yourself in the data to the
extent that you are familiar with the depth
and breadth of the content. Immersion
usually involves ‘repeated reading’ of the
data, and reading the data in an active way /
searching for meanings, patterns and so on.
It is ideal to read through the entire data set
at least once before you begin your coding,
as ideas and identification of possible patterns
will be shaped as you read through.
Whether or not you are aiming for an
overall or detailed analysis, are searching
for latent or semantic themes, or are data- or
theoretically-driven will inform how the
reading proceeds. Regardless, it is important
to be familiar with all aspects of your
data. At this phase, one of the reasons why
qualitative research tends to use far smaller
samples than, for example, questionnaire
research will become apparent / the reading
and re-reading of data is time-consuming.
It is, therefore, tempting to skip over
this phase, or be selective. We would
strongly advise against this, as this phase
provides the bedrock for the rest of the
analysis.
During this phase, it is a good idea to start
taking notes or marking ideas for coding
that you will then go back to in subsequent
phases. Once you have done this, you are
ready to begin, the more formal coding
process. In essence, coding continues to be
developed and defined throughout the entire
analysis.
Transcription of verbal data
If you are working with verbal data, such as
interviews, television programmes or political
speeches, the data will need to be
transcribed into written form in order to
conduct a thematic analysis. The process of
transcription, while it may seen time-consuming,
frustrating, and at times boring, can
be an excellent way to start familiarizing
yourself with the data (Riessman, 1993).
Further, some researchers even argue
it should be seen as ‘a key phase of
data analysis within interpretative qualitative
methodology’ (Bird, 2005: 227), and
recognized as an interpretative act, where
Table 1 Phases of thematic analysis
Phase Description of the process
- Familiarizing yourself
with your data:
Transcribing data (if necessary), reading and re-reading the data, noting down
initial ideas. - Generating initial codes: Coding interesting features of the data in a systematic fashion across the entire
data set, collating data relevant to each code. - Searching for themes: Collating codes into potential themes, gathering all data relevant to each
potential theme. - Reviewing themes: Checking if the themes work in relation to the coded extracts (Level 1) and the
entire data set (Level 2), generating a thematic ‘map’ of the analysis. - Defining and naming
themes:
Ongoing analysis to refine the specifics of each theme, and the overall story the
analysis tells, generating clear definitions and names for each theme. - Producing the report: The final opportunity for analysis. Selection of vivid, compelling extract
examples, final analysis of selected extracts, relating back of the analysis to the
research question and literature, producing a scholarly report of the analysis.
Using thematic analysis in psychology 87
meanings are created, rather than simply a
mechanical act of putting spoken sounds on
paper (Lapadat and Lindsay, 1999).
Various conventions exist for transforming
spoken texts into written texts (see Edwards
and Lampert, 1993; Lapadat and Lindsay,
1999). Some systems of transcription have
been developed for specific forms of analysis
/ such as the ‘Jefferson’ system for CA (see
Atkinson and Heritage, 1984; Hutchby and
Wooffitt, 1998). However, thematic analysis,
even constructionist thematic analysis, does
not require the same level of detail in the
transcript as conversation, discourse or even
narrative analysis. As there is no one way to
conduct thematic analysis, there is no one set
of guidelines to follow when producing a
transcript. However, at a minimum it requires
a rigorous and thorough ‘orthographic’
transcript / a ‘verbatim’ account of
all verbal (and sometimes nonverbal / eg,
coughs) utterances.8 What is important is
that the transcript retains the information
you need, from the verbal account, and in a
way which is ‘true’ to its original nature (eg,
punctuation added can alter the meaning of
data / for example ‘I hate it, you know. I do’
versus ‘I hate it. You know I do’, Poland,
2002: 632), and that the transcription convention
is practically suited to the purpose of
analysis (Edwards, 1993).
As we have noted, the time spent in
transcription is not wasted, as it informs
the early stages of analysis, and you will
develop a far more thorough understanding
of your data through having transcribed it.
Furthermore, the close attention needed to
transcribe data may facilitate the close reading
and interpretative skills needed to analyse
the data (Lapadat and Lindsay, 1999). If
your data have already been, or will be,
transcribed for you, it is important that you
spend more time familiarising yourself with
the data, and also check the transcripts back
against the original audio recordings for
‘accuracy’ (as should always be done).
Phase 2: generating initial codes
Phase 2 begins when you have read and
familiarized yourself with the data, and have
generated an initial list of ideas about what
is in the data and what is interesting about
them. This phase then involves the production
of initial codes from the data. Codes
identify a feature of the data (semantic
content or latent) that appears interesting
to the analyst, and refer to ‘the most basic
segment, or element, of the raw data or
information that can be assessed in a meaningful
way regarding the phenomenon’
(Boyatzis, 1998: 63). See Figure 1 for an
example of codes applied to a short segment
of data. The process of coding is part of
analysis (Miles and Huberman, 1994), as you
are organising your data into meaningful
groups (Tuckett, 2005). However, your
coded data differ from the units of analysis
(your themes), which are (often) broader.
Your themes, which you start to develop in
the next phase, are where the interpretative
analysis of the data occurs, and in relation to
which arguments about the phenomenon
being examined are made (Boyatzis, 1998).
Coding will, to some extent, depend on
whether the themes are more ‘data-driven’
or ‘theory-driven’ / in the former, the
Data extract Coded for
it’s too much like hard work I mean how much paper have you got to sign
to change a flippin’ name no I I mean no I no we we have thought about it
((inaudible)) half heartedly and thought no no I jus- I can’t be bothered,
it’s too much like hard work. (Kate F07a) - Talked about with partner
- Too much hassle to change name
Figure 1 Data extract, with codes applied (from Clarke et al ., 2006)
88 V Braun and V Clarke
themes will depend on the data, but in the
latter, you might approach the data with
specific questions in mind that you wish to
code around. It will also depend on whether
you are aiming to code the content of the
entire data set, or whether you are coding to
identify particular (and possibly limited)
features of the data set. Coding can be
performed either manually or through a
software programme (see, eg, Kelle, 2004;
Seale, 2000, for discussion of software
programmes).
Work systematically through the entire
data set, giving full and equal attention to
each data item, and identify interesting
aspects in the data items that may form
the basis of repeated patterns (themes)
across the data set. There are a number of
ways of actually coding extracts. If coding
manually, you can code your data by writing
notes on the texts you are analysing,
by using highlighters or coloured pens to
indicate potential patterns, or by using
‘post-it’ notes to identify segments of data.
You may initially identify the codes, and
then match them with data extracts that
demonstrate that code, but it is important in
this phase to ensure that all actual data
extracts are coded, and then collated together
within each code. This may involve
copying extracts of data from individual
transcripts or photocopying extracts of
printed data, and collating each code together
in separate computer files or using
file cards. If using computer software, you
code by tagging and naming selections of
text within each data item.
Key advice for this phase is: (a) code for as
many potential themes/patterns as possible
(time permitting) / you never know what
might be interesting later; (b) code extracts
of data inclusively / ie, keep a little of the
surrounding data if relevant, a common
criticism of coding is that the context is
lost (Bryman, 2001); and (c) remember that
you can code individual extracts of data in
as many different ‘themes’ as they fit into /
so an extract may be uncoded, coded once,
or coded many times, as relevant. Note that
no data set is without contradiction, and a
satisfactory thematic ‘map’ that you will
eventually produce / an overall conceptualization
of the data patterns, and relationships
between them9 / does not have to
smooth out or ignore the tensions and
inconsistencies within and across data
items. It is important to retain accounts
that depart from the dominant story in the
analysis, so do not ignore these in your
coding.
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