This document discusses qualitative research methods. It begins by distinguishing between methods and methodology, with methods being the specific tools used to collect data and methodology referring to the overall research philosophy and approach. Some common qualitative methods are discussed, including interviews, focus groups, and participant observation. The document then covers key aspects of qualitative methodology, such as philosophical underpinnings regarding ontology, epistemology and axiology. Examples of coding and thematic analysis are provided as approaches to analyzing qualitative data. Thematic analysis involves identifying common themes across a data set through a six step process of familiarization, coding, generating themes, reviewing themes, defining themes, and writing up the analysis.

1. Qualitative
Method : A
way forward
in Research
Kanhaiya Sapkota
Central Department of Geography
&
Tourism and Hospitality Management Study
Kanhaiya.sapkota@cdg.tu.edu.np
11th June, 2021

2. Method vs. Methodology
• Methodology = method + ology
• Method = tools and techniques
• Ology = Philosophy, theory, ethology
• So methodology refers to philosophy
of your research
Method
A method is simply the tool used to answer your
research questions - how, in short, you will go about
collecting your data.
 Contextual inquiry
 Focus Group Discussion
 Structured Interview
 A controlled experiment
 Survey or questionnaire
 Diary study, etc.
If you are choosing among these, you might say “what
method should I use?” and settle on one or more
methods to answer your research question.
In summary, the methodology is “HOW” you will answer
your research questions and the method is “WHAT” you
do to collect your data.
A methodology is the
rationale for the research
approach, and the lens
through which you will
analyse your research.

3. What are the
Philosophical
part of my
research?
• Ontology – The theory of
reality
• Epistemology - The theory
of knowledge
• Axiology - The theory of
value; and
• Logic - The theory of
reasoning.

4. Ontology = what is reality?, What shapes the reality?
Ontology believes: There are many believes about ontology, but
here I am talking about THREE major believes of ontology:
1. Believe that there is only one reality (singularity) – it means
that there is only one universal set of solution to any
There is only one universal truth. There is only one reality for
specific situation or specific problem.
2. There are more than one reality (multiple reality or truths are
are there). It depends the people who are perceiving the
truth or how people perceive the truth/reality/knowledge. It
depends largely on the people who are shaping the reality or
creating the truth.
3. The reality can be one or many. So both possibilities are
there.

5. Epistemology: it tells you about how we receive knowledge?
How do we know the reality or truth?
• It tells us about how we collect knowledge?
Epistemology believes:
1. Knowledge can be measured: it can be measured by any
reliable scientific rules, tools and designs. So, there is a
measurability of truth or knowledge.
2. Knowledge can not be measurable, it is interpretive. So
knowledge needs to interpreted or explained. We can
knowledge or we can collect knowledge by interpreting by
situation or any truth or realty.
3. Truth or knowledge can be measured or at the same time it
can be interpretive. Knowledge should be examined using

6. ONTOLOGY EPISTEMOLOGY RESEARCH PHILOSOPHY
There is only one reality Measured reliable tools and
designs
POSITIVISM
There are multiple realities Knowledge needs to be
interpreted or explained
CONSTRUCTIVISIM
The reality can be one and
many
Examine using scientific
or interpretation (whatever the
best tool use)
PRAGMATISM

7. RESEARCH
PHILOSOPHY
AXIOLOGY METHODOLOGY TOOLS/TECHNIQUES
POSITIVISM Value free Positivist = quantitative Survey, questionnaire,
etc.
CONSTRUCTIVISIM Value-laden Interpretivist =
qualitative
Interview, case study,
ethnography,
phenomenology,
participatory, in-depth
interview, grounded
theory, etc.
PRAGMATISM Pragmatist = mixed or
multi methods
Knowledge should be
examined using best
tool, scientific designs or
interpretation

8. • Therefore, methodology provides a guidance whether you are doing your research in a
qualitative study – interpretivist;
• Quantitative study = positivist/post-positivist
• When you clear about your methodological standpoint, then you can think about your
methods/tools/techniques, etc.
Qualitative research: It is a phenomenological inquiry in
a naturalistic setting which provide detailed information
about the participants behaviour, motivations and
preferences.
INTERPRETIVISM = CONSTRUCTIVISM

9. Qualitative Methodology
Some examples include:
 Phenomenology: describes the “lived experience” of a particular
phenomenon
 Ethnography: looks at cultural and social norms and behaviours of
a group
 Participatory: views the participants as active researchers
 Ethnomethodology: examines how people use dialogue and body
language to construct a world view
 Grounding theory*: assumes a blank slate and uses an inductive
approach to develop a new theory
(*Despite the fact that grounding theory has theory in its name, don’t
let that fool you - it is actually a methodology because it aims to
generate theory from systematic application of research.)Research

10. Which approach is right for me?
1. CONTINUITY AND CHANGE IN POPULATION MOVEMENT: FROM
INSIDE A RURAL NEPALI COMMUNITY
2. EXPLORATION OF THE SOCIO-ECONOMIC ECOLOGY OF
KATHMANDU, NEPAL: A FACTOR ANALYSIS APPROACH

11. Methods of qualitative study
In this section we will focus on:
1. Research participants
2. Research tools
3. Study area (Where will your research be conducted?)
4. Data/information collection process
5. Data/information analysis
6. Quality standard, Reliability, trustworthiness, replicability, etc.
7. Ethical concerns

12. Research tools
1. Interview
We should consider on –
a) Use easily understood language that is appropriate to your
informant.
b) Use non-offensive language.
c) Use words with commonly and uniformly accepted meanings.
d) Avoid ambiguity.
e) Phrase each question carefully.
f) Avoid leading questions as far as possible (i.e. questions that
encourage a particular response).

13. Type of questions
• Primary – opening questions used to initiate discussion on a
new theme or topic.
• Secondary – are prompts that encourage the information to
follow up or expand on an issue already discussed.
Ordering questions and
topics
a) Funnel structure; and
b)Pyramid structure
We generally use THREE structures of
interviewing
a) Structured interviewing –(Objective
reality)
b) Semi-structured interviewing; and
(Subjective reality)

14. 2. Oral History
3. Focus Group Discussion (focused interview)
4. Historical Research and Archival Sources
5. Using Questionnaires
There are four distinct types of question content:
a) Attributes – Attribute questions aim to establish respondents'
characteristics (for example, age or income bracket, owner-
occupier or private renter, etc.)
b) Behaviour – With behaviour questions, we are interested in
discovering what people do (for example, recreation habits, extent
of public transport use, etc.)
c) Attitudes – Questions about attitudes are designed to discover
what people think is desirable or undesirable (for example,
judgement on integrating social housing with owner occupied
housing, etc. )
d) Belief – Questions about beliefs aim to establish what people
belief to be true or false (for example, beliefs on the importance of

15. Data/information analysis
• Transcribing
Interview
• Content Analysis
• Discourse Analysis
• Thematic Analysis
• Textual Analysis, etc.
In this presentation I will
focus only on:
a) Transcribing Interview, and
b) Thematic Analysis

16. Transcribing Interview
How to transcribe an interview (in five steps):
1. Choose your preferred transcription method
2. Transcribe the audio (using transcription software)
3. Add speaker designation and time stamps
4. Clarify the transcript where needed
5. Proofread the transcript
Transcription methods
Before you start transcribing, you first need to determine what transcription
method you want to use. The best method depends on the goal of your
transcription.
1. Verbatim transcription
2. Intelligent verbatim transcription (most common)
3. Edited transcription
4. Altering the transcript

17. Verbatim transcription
• Write down every single word, including pauses, the expression of
emotions such as laughter, stuttering, and hesitations such as “uh”.
• This type of transcription is mostly used in the legal profession or in
research where you’re not only interested in what is said but also
how it is said.
Intelligent verbatim transcription (most common)
• Write down every word, but without irrelevant fillers like “uhm”,
“yeah”, “you know” etc. To improve readability, you can also fix
grammar mistakes, broken sentences and long paragraphs.
• This method is more readable than verbatim transcription, but
data - such as emotions, pauses and hesitation - is lost in the
process.

18. Edited transcription
A summarized and edited version of an intelligent verbatim transcript. In addition
to omitting fillers like “you know”, irrelevant sentences can be omitted if it doesn’t
change the meaning of the story.
Altering the transcript
• If the audio quality is bad or the conversation itself needs clarification, you are
allowed to make changes in the transcript. For instance:
• Adding a clarifying comment: “I showed him that this option [raising
prices] would be beneficial for profitability.”
• Marking unclear / missing audio with ellipses: “I showed him … would be
beneficial for profitability”
• Emphasizing words: “Increasing prices is needed for profitability”
Analyzing interview transcripts
• After transcribing the interview(s) it is time to start analyzing. There
are several techniques for doing this - coding and categorizing is
one of them.

19. Thematic Analysis
Thematic analysis is a method of analyzing qualitative data. It is usually
applied to a set of texts, such as interview transcripts. The researcher closely
examines the data to identify common themes – topics, ideas and patterns of
meaning that come up repeatedly.
When to use thematic analysis
Thematic analysis is a good approach to research where you’re trying to find out
something about people’s views, opinions, knowledge, experiences or values
from a set of qualitative data – for example, interview transcripts, social media
profiles, or survey responses.
Some types of research questions you might use thematic analysis to answer:
1. How do graduate students perceive faculty members in a departmental
setting?
2. What are nonexperts’ ideas and opinions about climate change?

20. • To answer any of those questions, you would collect data from a
group of relevant participants and then analyze it.
• Thematic analysis allows you a lot of flexibility in interpreting
the data, and allows you to approach large data sets more easily
by sorting them into broad themes.
• However, it also involves the risk of missing nuances in the data.
Thematic analysis is often quite subjective and relies on the
researcher’s judgement, so you have to reflect carefully on your
own choices and interpretations.
• Pay close attention to the data to ensure that you’re not picking
up on things that are not there – or obscuring things that are.

21. Approaches to thematic analysis
Once you’ve decided to use thematic analysis, there are different approaches to consider.
There’s the distinction between inductive and deductive approaches:
• An inductive approach involves allowing the data to determine your themes.
• A deductive approach involves coming to the data with some preconceived themes you expect to find
reflected there, based on theory or existing knowledge.
• Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find
in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?
There’s also the distinction between a semantic and a latent approach:
• A semantic approach involves analyzing the explicit content of the data.
• A latent approach involves reading into the subtext and assumptions underlying the data.
• Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal
about their assumptions and social context (latent)?

22. • There are various approaches to conducting thematic analysis,
but the most common form follows a six-step process:
1. Familiarization
2. Coding
3. Generating themes
4. Reviewing themes
5. Defining and naming themes
6. Writing up

23. Step 1: Familiarization
• The first step is to get to know our data. It’s important to get a
thorough overview of all the data we collected before we start
analyzing individual items.
• This might involve transcribing audio, reading through the text
text and taking initial notes, and generally looking through the
data to get familiar with it.
Step 2: Coding
• Next up, we need to code the data. Coding means
sections of our text – usually phrases or sentences – and
up with shorthand labels or “codes” to describe their content.
• Let’s take a short example text. Say we’re researching
perceptions of climate change among the people aged 50 and
up, and we have collected data through a series of interviews.

24. Coding qualitative data
Interview extract Codes
Personally, I’m not sure. I think the climate is
changing, sure, but I don’t know why or how.
People say you should trust the experts,
but who’s to say they don’t have their own
reasons for pushing this narrative? I’m not
saying they’re wrong, I’m just saying there’s
reasons not to 100% trust them. The facts
keep changing – it used to be called global
 Uncertainty
 Acknowledgement of climate
change
 Distrust of experts
 Changing terminology

25. • At this stage, we want to be thorough: we go through the
transcript of every interview and highlight everything that jumps
out as relevant or potentially interesting.
• As well as highlighting all the phrases and sentences that match
these codes, we can keep adding new codes as we go through
the text.
• After we’ve been through the text, we collate together all the
data into groups identified by code.
• These codes allow us to gain a condensed overview of the main
points and common meanings that recur throughout the data.

26. Step 3: Generating themes
• Next, we look over the codes we’ve created, identify patterns among them, and start coming up
with themes.
• Themes are generally broader than codes. Most of the time, we’ll combine several codes into a
single theme. In this example, we might start combining codes into themes like this:
Turning codes into themes
Codes Theme
 Uncertainty
 Leave it to the experts
 Alternative explanations
Uncertainty
 Changing terminology
 Distrust of scientists
 Resentment toward experts
 Fear of government control
Distrust of experts
 Incorrect facts
 Misunderstanding of science
 Biased media sources
Misinformation

27. • At this stage, we might decide that some of our codes are too
vague or not relevant enough (for example, because they
don’t appear very often in the data), so they can be discarded.
• Other codes might become themes in their own right. In this
example, I decided that the code “uncertainty” made sense as
a theme, with some other codes incorporated into it.
• Again, what we decide will vary according to what we’re trying
to find out. We want to create potential themes that tell us
something helpful about the data for our purposes.

28. Step 4: Reviewing themes
• Now we have to make sure that our themes are useful and
accurate representations of the data.
• Here, we return to the data set and compare our themes
it.
• Are we missing anything? Are these themes really present in
data? What can we change to make our themes work better?
• If we encounter problems with our themes, we might split
up, combine them, discard them or create new ones: whatever
makes them more useful and accurate.
• For example, we might decide upon looking through the data
that “changing terminology” fits better under the “uncertainty”
“uncertainty” theme than under “distrust of experts,” since the
data labelled with this code involves confusion, not necessarily

29. Step 5: Defining and naming themes
• Now that you have a final list of themes, it’s time to name and
define each of them.
• Defining themes involves formulating exactly what we mean by
each theme and figuring out how it helps us understand the
data.
• Naming themes involves coming up with a succinct and easily
understandable name for each theme.
• For example, we might look at “distrust of experts” and
determine exactly who we mean by “experts” in this theme. We
might decide that a better name for the theme is “distrust of
authority” or “conspiracy thinking”.

30. Step 6: Writing up
• Finally, we’ll write up our analysis of the data. Like all academic texts,
writing up a thematic analysis requires an introduction to establish our
research question, aims and approach.
• We should also include a methodology section, describing how we
collected the data (e.g. through semi-structured interviews or open-
survey questions) and explaining how we conducted the thematic
itself.
• The results or findings section usually addresses each theme in turn. We
describe how often the themes come up and what they mean, including
examples from the data as evidence. Finally, our conclusion explains the
main takeaways and shows how the analysis has answered our research
question.
• In our example, we might argue that conspiracy thinking about climate
change is widespread among older people, point out the uncertainty
which many peoples view the issue, and discuss the role of
in respondents’ perceptions.

31. Thank you
Kanhaiya Sapkota
Central Department of Geography
Tribhuvan University, Kirtupur
Professional email: Kanhaiya.sapkota@cdg.tu.edu.np
Kanhaiya.sapkota@thp.tu.edu.np
Personal email: kanhaiya.sapkota@gmail.com