Qualiative Methods: Nuts and Bolts

Qualitative Methods :Nuts and Bolts
Dr.R.RAMNATH
Associate Professor
Department of Education
Alagappa University

History
• The first forms of social studies
• It was diminished its importance the
1950s and 1960s
(as quantitative science reached its peak of
popularity)
• Regain recognition only in the 1970s.
Qualitative Research 3

History
The phrase ('qualitative research‘) was until 1970
restricted as a discipline of
anthropology or sociology.

History
• During 1970s and 1980s QR began to be used as
significant method in other disciplines
– education
– social work
– women's studies,
– disability studies,
– information studies,
– management studies,
– nursing service studies,
– psychology,
– communication studies, and
– consumer products industry.

Qualitative Research?
Any research arrives finding
Not by means of statistical procedures
or
other means of quantification."
(Strauss & Corbin, 1990)

Why?
• Care for people and promote change in behavior
• Required to understand in-depth concepts
such as
– experience,
– perceptions,
– believes,
– motivations and
– intentions.
• So, listening and observing need due
recognition rather administering questions.

Qualitative Quantitative
Non-probability based sample Probability-based sample
Non-generalizable Generalizable
Answers Why? How? Answers How many? When?
Where?
Formative Tests hypotheses, latter phases
Data are “rich” and time-
consuming to analyze
Data are more efficient, but may
miss contextual detail
Design may emerge as study
unfolds
Design decided in advance
Researcher is the instrument Various tools, and instruments
employed

Distinctions from quantitative
sampling
is
purposive
The role of the
researcher is
key. (process
and analysis)
Data analysis
differs
considerably
Must code
data and
discern
themes in a
consistent and
reliable way
Explorato
ry
(i.e.,
hypothesis-
generating)

Distinctions from
quantitative
Highly useful in
policy and
evaluation
research,
Yield useful
insights about
program
implementatio
n
A specialized form
of QR is
cognitive
testing

Data Distinctions from quantitative
Data speaks to
content validity -
measures measure what
a researcher thinks they
measure?
Data cannot always be
graphed or
displayed as a
mathematical
term.
Data may be useful
to explain puzzling
quantitative results.
Data may be used to
generate additional
variables to include in
an analysis.

Road Map
• Research Question
guides the choice
• Choice of methods is
situational
• Requires resources
and time
• Can be amended for

Main features
1. Exploration
2. Inductive approach
3. Interactive and Reflective
4. Holistic
5. Flexible

1.Exploration
Essential feature to understand the
perceptions and actions of
participants.
– Exploration is to discover new people,
customs, and to learn from them.
– Exploration is a difficult task that
requires training and
experience.

2.Inductive approach
– To develop concepts .
– Open to ideas which emerge from
listening or observing people.
– Useful when little is known about the
topics one wants to study.

3.Interactive and Reflexive process
– Try to study phenomena in a detached way.
– Use interaction between researchers and
participants to get closer to the topic (TRUST) .
– The researcher is an instrument of data
collection.
– The researchers probe, facilitate, and note
tone, hesitations and repetition in
participants responses.

– Examining not only what people say and
do, but also
why they say and do
so.
Interactive and Reflexive process

4.Holistic Exploration
– Participants are
allowed to put their responses in
context
instead close ended

5.Flexible
Relies on personal, intimate and
private world of participants.
– So flexible,
– imaginative,
– creative and
– varied strategies
are used to facilitate this process.

Phenomenological
studies
– It examine human experiences through
the descriptions that are provided by the
people involved. “ live experiences ”.
– Subjects are asked to describe thin
experience as they perceive them.

Ethnography
• Interview people who are most knowledgeable
about the culture ( key informants )
• Data are collected through participant
– observation,
– interviews,
– genealogy,
– demography, and
– life histories.

Grounded Theory
– Data are collected and analyzed and then a
theory is developed that is grounded in the
data.
– Uses both an inductive and a deductive
approach to theory development.
– Constructs and concepts are grounded in the
data and hypotheses are tested as they arise from
the research.

Case
Studies
– In-depth examinations of people or groups
of people.
– A case study could also examine an
institution.

Case Studies
• Not used to test hypotheses but to be
generated.
• Data may be collected in case studies
through
– questionnaires
– interviews,
– observations, or
– written accounts by the subjects.

l 38
Types
Intrinsic Case Study
Unusual Case Study an intrinsic, unusual case.
Instrumental Case Study
Issue Case Study a case that provides insight into
an issue or theme
Multiple Instrumental Case Study
(Collective Case Study)
Case
Case
Case
Issue
Study several cases that
provide insight into an
issue (or theme)

Historical Studies
• To examine the roots of Educational
Technology through historical research.
• Historical studies concern the
– identification,
– location,
– evaluation, and
– synthesis of data from the past.

Data collection methods
– interviews,
– observations,
– group discussion,
– analysis of video
recording,
– letters,
– diaries and
– other documents.
Open–
ended
always

• Diary accounts
• Documents
• Case studies
• Photographs
• Audio recordings
• Video recordings
• Transcriptions
• Descriptive traits
• Descriptions
• Observations

Data collection in specific
• QR typically rely on four methods
(participation in the setting)
– direct observation ,
– in depth interviews, and
– analysis of documents and materials
– focus groups and key informant
interviews.

In addition to the
aforementioned
– Life histories.
– Diaries.
– Personal collections of letters and
photographs.
– official documents
–shadowing.

Some key terms in QR data
• gate keeper
• key
informant
• Snowballin
g
 rich-thick
description
 grand tour
questions
 data saturation

Reliability and Validity?
• The reliability and validity are often viewed with
skepticism.
• The generalizability of findings is not present
to the same extent as in quantitative studies.
• Qualitative research are generally not
replicated because knowledge of the findings
of one study could bias the results of another
study, (Each situation is unique).

6a. Coding

A Code?
• A word or short phrase
• Symbolically assigns a summative, salient,
essence-capturing, and/or evocative
attribute for a portion of language-
based or visual data.

Types
• Deductive Coding
• In deductive coding, researchers start with a predefined set
of codes or a codebook developed before analyzing the
research data.
• This set could be based on the research questions or an
existing research framework or theory.
• For example, if the research question is why a consumer
purchased a specific product, the researcher might predefine
a list of codes that includes price, quality, brand, etc. With this
list in mind, the researcher would then read
through the research data and simply assign
the predefined codes.

Inductive Coding
• Inductive coding involves building a list of codes or a
codebook from scratch based on the research data.
• Inductive coding is often more difficult but can be less prone
to bias than deductive coding, because the
researcher does not start the analysis process with any
preconceived notions about what they might read or hear.
• In practice, research studies often combine deductive and inductive
coding, starting with a predefined list of codes but then inductively
modifying and adding to that list as analysis ensues.

Initial
and
Line by Line
Approach

Initial Coding
In vivo
coding
participants' exact words or phrases as
codes to capture their lived experiences
Process
coding
communicate an action in the data
Descriptive
coding
summarise extracts by using a single word or
noun (general idea, image ,video etc.)
Structural
coding
first round coding method according to
research questions (extracting variables,
functions, classes, or modules from existing
code to reduce repetition)

Simultaneous Coding
A single excerpt of qualitative
data is coded with
multiple codes.

Line by line coding
• Once we’ve got an overall idea of our data, are
comfortable navigating it and have applied some initial
codes, you can move on to line by line coding.
• Line by line coding is pretty much exactly what it sounds
like – reviewing your data, line by line, digging
deeper and assigning additional codes to each line.
• With line-by-line coding, the objective is to pay close
attention to your data to add detail to your codes.
• For example, if you have a discussion of beverages and
you previously just coded this as “beverages”, you
could now go deeper and code more

Thematic Analysis Coding
• Find recurring patterns and themes
Pattern Coding
Pattern coding, you group similarly coded excerpts
under one overarching code to describe a pattern.
Focused coding / Selective coding
With Focused coding, you create a finalized set of
codes and categories from your first coding pass (often from
“open coding”). You then re-code the qualitative data according to
this final code list with the intent to not deviate from it. In comparison
to an initial “open coding” pass which allows for emergent and
changing codes, focused coding is a method where you don’t intend
to change the code list any further.

Axial coding
Relate codes or categories to one another. You’re looking for
relationships and links between what you found in earlier rounds of coding.
Theoretical coding
Conceptualize a hypothesis of a theoretical framework through sorting
and organizing codes. You structure the codes and categories that emerged
from qualitative data into a theory.
Elaborative coding
Apply a theory from a previous research study and observe whether or
not your current codes and categories relate. You can think about it as
elaborating on pre-existing theories.
Longitudinal coding
Organize your existing codes and categories in a way that enables you
to compare them over time.
Content analysis coding
Examine and understand the content of textual data. There are two

Conditions of Coding to Analysis
• What actions are shown in the data?
• What are the aims of these interactions and excerpts? What are the
participants potentially trying to achieve?
• How do participants interpret what is happening, and how do they
speak about it? What does their language reveal?
• What are the assumptions made by the participants?
• What are the participants doing? What is going on?
• Why do I want to learn about this? What am I trying to find out?
• Why did I include this particular excerpt? What does it represent and
how?

Code categorisation
• Categorisation is simply the process of reviewing everything you’ve
coded and then creating code categories that can be used to guide
your future analysis. In other words, it’s about creating categories for
your code set.
• Let’s take a look at a practical example. If you were discussing different
types of animals, your initial codes may be “dogs”, “cow”, and
“goat”.
• In the process of categorisation, you could label (categorise) these
three animals as “mammals”.
• Whereas we could categorise “flies”, “crickets”, and “beetles” as
“insects”.
• By creating these code categories, you will be making your data more
organised, as well as enriching it so that you can see new connections
between different groups of codes.

Tips & tricks for
quality coding
• Before we wrap up, let’s quickly look at some general advice, tips and
suggestions to ensure your qualitative data coding is top-notch.Before you begin
coding, plan out the steps you will take and the coding approach and
technique(s) you will follow to avoid inconsistencies.
• When adopting deductive coding, it’s useful to use a codebook from the start of
the coding process. This will keep your work organised and will ensure that you
don’t forget any of your codes.
• Whether you’re adopting an inductive or deductive approach, keep track of the
meanings of your codes and remember to revisit these as you go along.
• Avoid using synonyms for codes that are similar, if not the same. This will
allow you to have a more uniform and accurate coded dataset and will also help
you to not get overwhelmed by your data.
• While coding, make sure that you remind yourself of your aims and coding
method. This will help you to avoid directional drift, which happens when
coding is not kept consistent.
• If you are working in a team, make sure that everyone has been trained and
understands how codes need to be assigned.

Open, Axial, and
Selective approach

Overview of coding process: Open,
Axial and Selective Coding

Open Coding
• Open coding is the first level of coding. In open coding, the researcher is
identifying distinct concepts and themes for categorization. The first level of
data is organized by creating initial broad thematic domains for data
assemblage.
• “The first step [open coding] aims at expressing data and phenomena in the
form of concepts.
• Units of meaning classifying expressions (single words, short sequences of
words) in order to attach annotations and "concepts” (Flick, 2009).

Axial
Coding
• Axial coding is the second level of coding. In contrast to open
coding, which focuses on identifying emergent themes,
• Axial coding further refines, aligns, and categorizes the themes.
• “Axial coding identifies relationships between open codes, for the
purpose of developing core codes. Major (core) codes emerge as
aggregates of the most closely interrelated (or overlapping) open
codes for which supporting evidence is strong” (Strauss, 1998).

Selective
coding
• The third level of coding. It enables the researcher to select and
integrate categories of organized data from axial coding in
cohesive and meaning-filled expressions.
• “Selective coding continues the axial coding at a higher level of
abstraction [through] actions that lead to an elaboration or
formulation of the story of the case” (Flick, 2009).
• Central to enabling the story or case to emerge from the data
categories is the process of enabling further refinement of the data,
selecting the main thematic category, and then in a systematic
manner aligning the main theme to other categories that have been
selectively coded. “

Linear Model

Non-Linear
Model

Analysis
– an examination of words rather than numbers as is
done in quantitative studies.
– Then form an impression,
– and report their impression in a structured form.
– These impressions can be the final conclusion of the
analysis using some methods.

• Frequently a massive amount of data, in the from
of words … The task of analyzing all these data
can be over whelming.
• The researcher begins interpreting data as
data are collected.
• Content analysis is a common method of
analyzing data.

Transcriptions
Verbatim transcription: Transcribing every word, pause,
stutter, and filler words, such as “ahs” and “ums”.
Intelligent transcription: Transcribing every word, but
making an interpretation to exclude pauses, status, and
filler words and potentially cleaning up the grammar.
Edited transcription: It’s cleaned up and edited to
increase readability and clarity. Can edit grammar,
remove run-on sentences, and potentially
summarize parts for the sake of clarity.

1.IPA
Interpretive phenomenological analysis (IPA) explores how
individuals make sense of their experiences.
It is an inductive research method that begins with specific
examples and uses them to develop broader themes.
IPA researchers collect data (e.g. via interviews) and analyze it to
uncover themes and meanings to help them understand how
individuals make sense of their experiences.
IPA requires the researcher to bracket whatever a priori
assumption they have about the experience or phenomenon.
Bracketing is when researchers reflexively engage with the data
to document their own biases or beliefs about what participants
say.

2. Thematic Analysis
Thematic analysis involves reading through a data set
(such as transcripts from in-depth interviews or focus
groups) and identifying patterns of meaning across the
data to derive themes.
Like IPA, thematic analysis also involves an active
process of reflexivity, where a researcher’s subjective
experience plays a central role in meaning-making
from data.

3. Content Analysis
The goal of qualitative content analysis is to
identify the underlying themes and patterns
in the data that can help us to understand the
participants' experiences, thoughts, and feelings.

Document
Analysis Techniques
Document analysis is a data collection technique used in
qualitative research that involves the examination and
interpretation of written or visual materials, such as
letters, diaries, photographs, or videos, to gain insights
into people's experiences and behaviors.
Once the data has been collected, it is analyzed using
various qualitative data analysis techniques. These
techniques include coding, categorization, and
interpretation.
Coding
It involves breaking down the data into smaller
segments and assigning codes or labels to these
segments.

Inductive

Deductive

Stages of data analysis
1. Familiarization immersion in the raw data
(or typically a pragmatic selection from
the data) by
• listening to tapes,
• reading transcripts,
• studying notes and so on,
in order to list key ideas and recurrent themes

2. Identifying a thematic framework
– identifying all the key issues, concepts, and themes
by which the data can be examined and referenced.
– This is carried out by drawing on a priori issues and
questions derived from the aims and objectives of
the study as well as issues raised by the respondents
themselves and views or experiences that recur in the
data.
– The end product of this stage is a detailed index of
the data, which labels the data into manageable
chunks for subsequent retrieval and exploration

3. Indexing applying the thematic framework or
index systematically to all the data in textual
form by
– annotating the transcripts with numerical codes from
the index,
– usually supported by short text descriptors to
elaborate the index heading.
– Single passages of text can often encompass a
large number of different themes, each of which has
to be recorded, usually in the margin of the
transcript

4. Charting rearranging the data according to the
appropriate part of the thematic framework to
which they relate, and forming charts.
– For example, there is likely to be a chart for each key
subject area or theme with entries for several
respondents.
– Unlike simple cut and paste methods that group
verbatim text, the charts contain distill summaries of
views and experiences.
Meaning Unit + Condensed Meaning
Unit

5. Mapping and interpretation using the charts to
– define concepts,
– map the range and nature of phenomena,
– create typologies and
– find associations between themes with a view to
providing explanations for the findings.
The process of mapping and interpretation is influenced
by the original research objectives as well as by the
themes that have emerged from the data themselves

4. Synthesis or Review Analysis

Measures& Variables through synthesis

PRISMA
Preferred Reporting Items for Systematic
Reviews
and Meta-Analyses

Limitations of Q R
1. It is anecdotal (stories told for dramatic
quality without critical evaluation)
2. Unscientific
3. Producing findings that are not
generalizable
4. Impressionistic (vague)
5. Subjective

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