A Brief Introduction to Thematic Analysis

Afregarde Research
Ground Floor, Lakeview Building
1277 Mike Crawford Ave.
Centurion, 0157
www.afregarderesearch.co.za
Prepared by Abisha Kampira &
Janine Meyer
consultants@afregarderesearch.co.za
Afregarde Strategies. 2019
Z204
A Brief Introduction to
Thematic Analysis
Foreword
This paper discusses thematic analysis, a popular yet often misunderstood
qualitative data analysis method. It is written with postgraduate and
institutional researchers who already have an appreciation of qualitative data
analysis in mind. The paper takes a practical rather than a theoretical
approach to thematic analysis focusing on methods and processes that are
applied by our researchers as they go about the analysis. The approaches
taken may, therefore, slightly differ from theory although they are highly
relatable.

Contents
Foreword .................................................................. 1
What is a theme? ..................................................... 3
Types of ideas .......................................................... 3
Characteristics of a theme ........................................ 4
Themes are subjective ...................................... 4
Themes are identifiable ..................................... 4
Themes are substantive .................................... 4
Thematic Analysis .................................................... 5
Content Analysis ...................................................... 5
Thematic Content Analysis ....................................... 5
Coding ..................................................................... 8
Semantic versus latent themes ....................... 13
Open coding.................................................... 14
Closed coding ................................................. 14
Hybrid coding .................................................. 15
In vivo coding .................................................. 15
Axial coding..................................................... 15
Summary ............................................................... 15
2

What is a theme?
The Oxford Learner’s Dictionary defines a theme as, the subject or main idea
in a talk, piece of writing or work of art . The Cambridge Dictionary defines
the same as the main subject of a talk, book, film, etc. Judging from the
definitions above, as well as from ordinary usage of the word in daily talk, the
word “theme” relates to the degree and/or intensity of occurrence of an
expressed idea on a specific subject. Thus, the more expressed an idea is,
the more likely that it is a theme of a subject. Using a theme, the frequency,
commonality or popularity of an expressed idea is more important than the
strength, correctness and meaningfulness of it. What is important is how
widely it is held, as evidenced by how frequently it is expressed. It is because
of this nature of a theme that it has found unprecedented importance in
constructivist-based research approaches that value subjectivity. As far as
constructivists are concerned, a theme can never be wrong. Content held
within it might be incorrect but this does not make it less of a theme. The
point is as long as ideas behind it are commonly held, then it is a theme.
René Descartes: philosopher,
scientist, mathematician (1596–
1650)
René Descartes was born in 1596
in La Haye en Touraine, France. He
studied Law, Mathematics,
philosophy, natural metaphysics and
others. Many sources in the modern
literature regard René Descartes as
the father of modern philosophy. He
wrote over twenty published works
in his lifetime including Meditations
on First Philosophy, Meditations
and Other Metaphysical Writings,
Meditations On First Philosophy:
With Selections From The
Objections And Replies, Discours de
la Méthode, The Rationalists:
Descartes among others.
Types of ideas
According to René Descartes, all ideas
can be classified under one or more of
these three groups: innate, adventitious,
and “factitious”. Descartes argues that a
human being is born with a concoction of
ideas. These he termed innate ideas –
ideas coming from inside. Most ideas,
however, come from outside one’s mind
and were therefore external. Descartes
called these adventitious ideas. Finally,
some ideas are generated by an
individual and these he called factitious
ideas. While the concept of adventitious
and factitious ideas is envisaged in
modern science and philosophy, the
innate ideas view -the view that a human
being is born with ideas is vehemently
debated. For example, another
philosophical figure, John Locke asserted
that human beings are born with a clear
mind. All our ideas are developed from
our interactions with our environment.
Ideas are therefore a result of sensed
experiences and Locke calls this line of
thinking empiricism.
Themes, like ideas, are developed
through the interaction of the external
3

and the internal. Externally, society creates ideas that eventually gain our
acceptance as common themes to a particular phenomenon. As intelligible
human beings, we can independently generate new themes to a
phenomenon. For instance, when society talks about war in Africa, common
adventitious themes, (themes developed by society and adopted by us as
individuals) include ethnic conflict, greed, dictatorship, religion among
others. Thus, if we are presented with a work, literal, artistic, visual, etc. that
requires our views on war in Africa, these recurrent themes are most likely
to come out. At the same time, we are also able to come up with completely
novel views and perceptions of war in Africa – factitious ideas.
Characteristics of a theme
A few common characteristics of a theme are discussed in this section.
Themes are subjective
A theme is subjective in nature: different individuals can extract different
themes from the same information. At the same time, different individuals can
extract a similar theme but express or classify it differently.
https://dapuan.wordpress.com
Themes are identifiable
A theme is identifiable. It is something that can be given a name and/or a
description or can be classified somehow. There is, therefore, nothing called
an unknown theme or an unidentified theme. To further argue, a theme is a
type of idea and much like an idea, it is not there until it has been thought,
described and then expressed.
Themes are substantive
A theme must be justified by reason. For any given theme, there should be
an explanation, discussion or description that back it. It must however be
noted that this justification does not necessarily have to be correct from a
third party’s perspective. It must, however, be reasonable enough to justify
the existence of a theme.
At this point, it must be mentioned that a theme is not the purpose of the
source of information we are interested in. A purpose refers to what the work
4

is intended to achieve which is to inform. Post information, it may be intended
to influence how we view a phenomenon. A theme is rather the ideas we get
from content and this is regardless of the purpose behind this content.
Thematic Analysis
Thematic analysis is a data analysis procedure that centres on identification,
description, explanation, substantiation and linkages of themes. It is premised
on the view that all information is conveyed with meaning and this meaning
can be deduced from identifying a central idea or a cluster of ideas that gives
it a comprehensive meaning.
Nowell et al. (2017) believe that thematic analysis’ flexibility and ease of use
is its core advantage. They assert that it is basic yet very helpful in querying
the meaning of qualitative data. Braun and Clarke (2006) see thematic
analysis as foundational in nature and can, therefore, be applied as a basis
for more rigorous qualitative data analysis processes. For instance, critical
discourse analysis, which is a more advanced qualitative data analysis
process can be applied using thematic analysis as its base. Thus, themes and
classes of data generated under thematic analysis can be interrogated using
CDA to get more insightful outcomes.
Content Analysis
Content analysis is “a systematic coding and categorizing approach used for
exploring large amounts of textual information unobtrusively to determine
trends and patterns of words used, their frequency, their relationships, and
the structures and discourses of communication” (Vaismoradi et al., 2013).
The difference between thematic analysis and thematic content analysis is
therefore the broadness of content analysis. In my view, content analysis is a
broad umbrella term for qualitative data analysis approaches that attempt to
use categorisation, classification and critiquing techniques to derive meaning
from data. In addition, content analysis enables the quantification of data and
can, therefore, extend to the quantitative data analysis realm.
Thematic Content Analysis
Because of its flexibility, thematic analysis can be conducted under the
guidance of different theoretical and philosophical underpinnings. The
University of Auckland article entitled, Thematic analysis: a reflexive
approach discusses six of these. Four of these are of interest to this paper:
• Inductive thematic analysis: the interpretation of data and
development of themes is guided by the content in a dataset.
Concepts and theories are developed with the analysed data as a
starting point.
• Deductive thematic analysis: themes are developed with reference to
existing concepts, theories and evidence. Concepts and theories are
tested for applicability, rather than developed from the data.
• Realist approach: theme development is guided by a pre-conceived,
actual or assumed reality.
5

• Constructivist approach: themes are developed based on the
subjective views reflected in the data.
The above highlights that thematic analysis can be conducted with several
approaches and as a method can produce varying results based on the
orientations of the researcher. When reading the results of data analysed
using thematic analysis, one should, therefore, be on guard for the
philosophical biases that might be held by a researcher.
Clarke and Braun (2006), who have contributed immensely to our
understanding of thematic analysis, state that thematic analysis is generally
a five-step process. These processes are outlined below:
Thematic analysis processes
1. Familiarisation with the data
2. Coding
3. Generating initial themes
4. Reviewing themes
5. Writing up
Source: Clarke & Braun, 2006
I am not sure whether it is relevant to discuss familiarisation with data as a
thematic analysis process. Every data analysis technique starts with
familiarising with a provided dataset. It is, however, worth mentioning that
there might be the need to familiarise with a dataset as well as its context.
For example, when conducting thematic analysis with a deductive and/or
realistic aim in mind, it is of paramount importance for the researcher to be
fully conversant with the concepts, theories, models and practice from which
any deductions to the data are to be made. The identification and extraction
of themes under deductive research contexts will, therefore, be guided by
pre-existing and pre-identified perspectives.
6

Thematic Analysis Example: Speeches by Mandela and
Obama on poverty in Africa…
Like slavery and apartheid, poverty is not natural. It is man-made and it
can be overcome and eradicated by the actions of human beings. And
overcoming poverty is not a gesture of charity. It is an act of justice. It is
the protection of a fundamental human right, the right to dignity and a
decent life. While poverty persists, there is no true freedom.
The steps that are needed from the developed nations are clear. The first
is ensuring trade justice. I have said before that trade justice is a truly
meaningful way for the developed countries to show commitment to
bringing about an end to global poverty. The second is an end to the
debt crisis for the poorest countries. The third is to deliver much more
aid and make sure it is of the highest quality.
Nelson Mandela: Address by Nelson Mandela for the "Make Poverty History"
Campaign, London - United Kingdom - 3 February 2005
Now, even with Africa’s impressive progress, we must acknowledge that
many of these gains rest on a fragile foundation. Alongside new wealth,
hundreds of millions of Africans still endure extreme poverty. Alongside
high-tech hubs of innovation, many Africans are crowded into
shantytowns without power or running water -- a level of poverty that’s
an assault on human dignity.
I suggest to you that the most urgent task facing Africa today and for
decades ahead is to create opportunity for this next generation. And this
will be an enormous undertaking. Africa will need to generate millions
more jobs than it’s doing right now. And time is of the essence. The
choices made today will shape the trajectory of Africa, and therefore, the
world for decades to come. And as your partner and your friend, allow
me to suggest several ways that we can meet this challenge together.
Many of your nations have made important reforms to attract investment
-- it’s been a spark for growth. But in many places across Africa, it’s still
too hard to start a venture, still too hard to build a
business. Governments that take additional reforms to make doing
business easier will have an eager partner in the United States.
Barack Obama: “Remarks by President Obama to the People of Africa” - July 28, 2015
7

Coding
The whole thematic analysis process is underpinned by coding. This section
discusses the coding process and also illustrates the same by way of an
example based on Former-Presidents Mandela’s and Obama’s speeches on
poverty in Africa.
Coding is the identification, marking and labelling of pieces of text or content
that hold any meaning or is of interest in answering research questions. It
occurs in hierarchical stages starting with the identification of words and
sentences into individual codes and then grouping individual codes into code
groups. As can be seen below, our coding process starts with identifying
pieces of texts (words, partial and full sentences) that we believe aid us to
understand poverty. Each of these was marked and given a short name or
description.
The next step is to group all the first-level codes that are similar under one
group. In the below example, Poverty, human dignity and human rights is the
code group and the extracted quotes below it are the individual codes.
Poverty, human dignity and human rights
• Like slavery and apartheid, poverty is not natural-NM
• While poverty persists, there is no true freedom-NM
• a level of poverty that’s an assault on human dignity-BO (human
dignity violation)
• we must acknowledge that many of these gains rest on a fragile
foundation-BO (Weak economies, slow growth)
• Alongside high-tech hubs of innovation, many Africans are
crowded into shantytowns without power or running water-BO
(inequality)
Another code group that was identified from the dataset is Solutions – what
is required from developed economies.
Solutions – what is required from developed economies
• The first is ensuring trade justice-NM (address trade injustices)
• The second is an end to the debt crisis for the poorest countries-
NM (debt relief)
• The third is to deliver much more aid and make sure it is of the
highest quality-NM (increased aid)
8

The last code group that was identified from the dataset is Solutions –
what is required from developed economies.
Solutions – what is required from developed economies
• the most urgent task facing Africa today and for decades ahead
is to create opportunity for this next generation-BO (create
investment opportunities)
• need to generate millions more jobs than it’s doing right now-BO
(job creation)
• Governments that take additional reforms to make doing
business easier will have an eager partner in the United States –
BO (economic alliances with the US)
“Extreme poverty anywhere is a
threat to human security
everywhere.” — Kofi Annan, Seventh
Secretary-General of the United Nations
9

All the above-highlighted codes can be group by how they relate to a
common idea, in this case, the idea that they are solutions to poverty. We can
classify the group code as a theme and the unit codes as subthemes.
Group codes
Unit codes
Address trade
imbalances
Job creation
Solutions to
poverty
Address
inequality
Increase aid
Economic
reforms
Relieve poor
states of debt
In the above example, we were able to code our data into one theme –
Solutions to poverty. But is it not possible that our codes can be grouped any
further? It is notable that the solutions can possibly be categorised into two
groups, what developed states need to do and what developing nations
themselves can do as solutions to poverty. Thus, the individual codes we
obtained can be grouped further to distinctly classify the solution by
responsibility between developed and developing nations.
10

The image below shows the resulting coding structures of this grouping:
Theme
codes
Group
codes
(Subthemes)
Unit
codes
(can be
quotes)
What
developed
states must do
address trade
imbalances
Debt relief
Solutions to
poverty
The role of
developing
states
Increase aid
Economic
reform
Job creation
Address
inequality
The schema above shows the themes generated from the extracts from
former presidents Nelson Mandela and Barack Obama on what needs to be
done to end poverty in Africa. Both presidents have solutions but they differ
in orientation. With the above data, we can treat solutions to poverty as a
theme and the solution focus areas (what developed nations must do and the
role of developing states) as sub-themes.
From the above, we can see the hierarchal nature of thematic analysis.
Higher-level codes or group codes are classified as themes while the lower
level codes form sub-themes. This hierarchical classification is useful in
creating meaningful relationships from masses of data. You can also see that
that question or issue of how poverty can be ended is answered by two
themes, each with related sub-themes. As stated earlier, we cannot always
tell with certainty that the above solutions are indeed valid. We are simply
analysing the views of our two speakers using data directly included in the
provided data and not their other views outside the extracts.
11

From the above, we can therefore, make the following
conclusions about sub-themes:
Sub-themes contribute
to our understanding
of a theme
Sub-themes do not
necessarily have to be
positive to a theme
All subthemes to a
single theme must
have a specific and
distinct link.
• Sub-themes contribute to our understanding of a
theme. They are pieces of information that develop a
theme into a holistic, more comprehensive idea. They
are, therefore, related to or associated with the theme.
• Sub-themes do not necessarily have to be positive to
a theme. A sub-theme may even contest the theme.
For example, in our theme, solutions to poverty, we
can have some respondents contesting that increased
aid is not a solution to poverty because it results in
donor dependency and limited internal development
and so forth… Increased aid is therefore still a subtheme
of our main theme.
• Sub-themes should not overlap across themes. As can
be seen in our hierarchical presentation of subthemes,
every theme can be viewed like a parent with
child themes (subthemes) that are related but
different. A sub-theme, therefore, appears once under
one theme and cannot be a child of more than one
theme. It may, however, be related to as many themes
and sub-themes as possible albeit not as a child. There
is also debate on whether a single code can contribute
to more than one theme. As long as it is possible that
a single piece of data can mean more than one thing
to the same audience, it is possible for a single code
to contribute to more than one subtheme or theme.
• All subthemes to a single theme must have a specific
and distinct link.
o
o
This link can be a related idea, an argument,
theory, etc. (e.g. all sub-themes to the second
theme are related by their focus on what
developing nations can do to reduce poverty).
They can be related by source (e.g. the
subtheme – the role of developing states is
associated with President Obama’s speech).
Regardless of how they are related, there must be some sort
of a logical association that justify their grouping under one
theme.
• A subtheme cannot exist without a theme (although a
theme can exist without subthemes). Where a
subtheme appears to hang in the air, it is possible that
it is either a theme on its own or it does not fit as a
theme in the analysis.
12

Semantic themes
“explicit and surface
meanings of the data and the
analyst is not looking for
anything beyond what a
participant has said or what
has been written”
The above examples show the common three-level categorisation from code
(short name or quotation) to subtheme and finally to the theme. It is also
possible that data in a dataset can have more than three levels of
categorisation. For instance, under the first level code job creation, several
different views on how these jobs can be created may be given – for example,
job creation through the promotion of entrepreneurship, job creation through
increased government spending and so on. All these can be classified, still
under the same sub-theme. This makes thematic analysis a procedure that
flexibly allows and supports various levels of detail.
Sub-themes can exist under various types of theme taxonomies. The next
subsection looks at these different taxonomies starting with the differences
between semantic and latent themes.
Semantic versus latent themes
Semantic themes are themes that are derived from codes with explicit
meanings. Clarke and Braun (2016:13) define them as
“explicit and surface meanings of the data (in cases
where) the analyst is not looking for anything beyond
what a participant has said or what has been written”. In
our example, both our themes and most subthemes are
sematic in nature because we derived them from direct
speeches of the speakers without us having to make any
assumptions of what they wanted to say. Thus, there is a
code that directly points to a part of the speech on debt relief,
job creation and so on.
With latent themes, on the other hand, there may not be such
explicit speech parts. We extract these from critical assumptions that we can
make from the speech. For example, President Obama does
Latent themes
not directly say that developing states should address
“the underlying ideas,
inequality as part of the solution to the problem of poverty.
assumptions, and
We, however, deduced this from his mention of inequality as
conceptualisations and
part of the poverty problem – “Alongside high-tech hubs of
ideologies that are theorised
innovation” although he did not necessarily state that
as shaping or informing the
addressing it is a solution to poverty. Our assumption is
semantic content of the
based on the view that, outside Obama’s speech, inequality
data.”
is seen as both a cause and a result of poverty, depending
on the context. This theme is therefore called a latent theme
based on, “the underlying ideas, assumptions, and
conceptualisations and ideologies that are theorised as shaping or
informing the semantic content of the data” (Clarke & Braun, 2006:13).
We are able to assume that Obama sees inequality as a cause of poverty
because he mentions it alongside the solutions that developing nations can
take to address poverty.
In the sections above, we have indicated that themes and subthemes are
developed through coding. But what exactly is coding? Coding generally
involves:
13

1. Identifying text or content important for answering research questions
2. Selecting, labelling or marking of content
3. Grouping content according to main ideas being conveyed,
similarities, differences, sources, etc.
In our above examples, coding was done through marking the text that we
thought would be helpful in bringing out the solutions of poverty according
to the two greats. Coding can be done on images and videos as well.
Open coding
With open coding, we do not know what exactly we are looking for
though we have a guideline that it is related to our research
question. We start with first level codes until we build them to last
level codes which are our themes. In open coding, we can use short
names, alphanumeric markers or other innovative ways to label and
classify data. With smaller datasets like the one we used above,
short names can work very well but as datasets become larger and
more complex, that is when the alphanumeric codes may be
needed, For example, for all data relating to debt relief, we could
use a code DR11 (debt relief statement 1 by Respondent 1) and so
on. Open coding is also used as a preliminary coding method to
identify patterns in a data mass.
Closed coding
With closed coding, we start with themes or sub-themes and go
about identifying pieces of text that can make these meaningful and
understandable. This form of coding is useful when:
Open coding: we start
with a dataset and go on
to deduce pieces of data
that might be of interest
to our research (mostly
inductive)
Closed coding: we start
with perceptions and
views that we wish to
justify using an available
dataset (somewhat
deductive)
• we are working with highly objective qualitative data,
e.g. in document analysis
• the themes behind data have already been established
by other forms of data analysis
• we want to investigate if our data agrees-conforms
with pre-determined or existing views
Assume we are now analysing data from the World Poverty Report with a
view of assessing whether the perceptions of the two leaders conform to
global findings. We will work with a predetermined theme, Solutions to
poverty in Africa or the two sub-themes. Working backwards, we look for any
data that supports the predetermined views. This is why this method of
coding works well with document analysis, particularly in a policy
environment. Policy documents and other public reports are generally
objective reports in contrast to speech, interview and focus group transcripts.
It is therefore possible to query these reports for objective truths, or what we
believe to be objective truths.
14

Hybrid coding
We use this term to describe qualitative data coding that incorporates both
open and closed coding. Assume in our example we have established our
themes/sub-themes and then go about looking for data that justifies/disputes
them in a selected source. At the same time, we are open to new themes and
sub-themes that can answer our research questions. This method is rarely
discussed although in my experience I have encountered its use in document
analysis also referred to as document research.
In vivo coding
Lately, most researchers apply in vivo statements as a way of marking and
labelling data. Instead of selecting data and giving it alphabetic or
alphanumeric codes, we just use the whole word, sentence or part of a
sentence as a code. This is what we did above. This kind of marking is now
even called in vivo coding though primarily in vivo is not a coding method but
just a way of marking data. Its major advantages are that the selected codes
can be directly used in reports to justify themes as well as its ease of use. It
is a flexible marking method that can be used with both open and closed
coding.
Axial coding
Axial coding involves identifying and showing linkages and relationships
among themes as well as subthemes. For instance, our first subtheme in the
illustration has three codes whose relationship can be highlighted via axial
coding. We can note a latent relationship between debt relief and increased
aid as factors that increase Africa’s revenue inflows. The two are, therefore,
interlinked as factors that support poverty reduction through a direct (aid)
and indirect (reduced debt payment outflows) increase of revenue to Africa.
We can further relate the codes, subthemes and themes for example, through
latent thematic analysis we can relate job creation, in the second theme, with
reducing trade imbalances in the first theme. If trade with the west is fair,
Africa will import less finished goods and export less primary material thereby
creating more manufacturing jobs. Such logic comes from relating themes
from data that is not necessarily inside the data content but can be strongly
assumed and justified. Axial coding can occur across all the types of coding
described above but is arguably a secondary level method that best works
when open/closed coding processes have been concluded.
Summary
Thematic analysis is a very useful tool for qualitative data analysis in both the
academic and professional realms. Its versatility with other qualitative data
analysis methods makes it an important research procedure to understand.
It can be used alongside content analysis, critical discourse analysis as well
as descriptive qualitative data analysis. Coding is the process by which
themes and subthemes are extracted from a dataset. There are various types
15

of coding identifiable in the literature. These include open, closed and hybrid
coding as well as in vivo coding.
Afregarde Research
e
Afregarde Research
Ground Floor, Lakeview Building
1277 Mike Crawford Ave
Centurion 0157
www.afregarderesearch.co.za
Prepared by Abisha Kampira &
Janine Meyer
consultants@afregarderesearch.co.za
z
Afregarde Research is a social
entrepreneurship-inspired research and
publications entity providing services to
Southern Africa.
We also offer practical training courses
in Research
For any enquiries, please email Abisha
(consultants@afregarde.co.za) or visit our
website: www.afregarderesearch.co.za
16