L4 4 qualitative research techniques and open question.ppt

11/05/24
Research Seminar no. 4
Qualitative research strategies and
data analysis techniques

Closed research question
starts with theory.
confirms a hypothesis.
tends to use quantitative
research techniques.
Deductive: closed
research question
Open research question
starts with data.
infers conclusions from data.
tends to use qualitative
research techniques
Inductive: open research
question
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philosophy slides
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Phenomenology
 Seeks to understand phenomena
as they are perceived by social
actors in context
 Accurate and in-depth
description of experience
 Relatively unstructured approach
 Starts empirically, not with theory
4
Edmund Husserl, 1859-1938

Ethnography
 Traditionally used by anthropologists
to study ‘exotic’ cultures
 Detailed analytical and descriptives
studies of everyday culture
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›Everything can be significant!
›Visual depictions may capture seemingly
unremarkable signs.

Research strategy or design
 A research design is the link between the
research question, conceptual model and
the empirical reality.
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Research
objectives and
questions
Theoretical
perspectives and
models
Research resources
(e.g. time, funds)
Research design (the
logic of the inquiry)
Identity of empirical
field (e.g. site
selection)
Data collection
process
Research
management process
• Variables
• Primary data
• Secondary
data
•Target case(s)
• Unit(s) of
analysis
• Pilot
• Data analysis
• Reporting
Source: Yin 1993

Methodological choices
and implications
 Variety of options
 Case studies
 Action research
 Varieties of Grounded Theory building
 Visual methods
 … and so on… or combinations
 For more detailed methods and techniques, see
literature.
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Qualitative research strategy :
One of the options:
case study design

A frequently used
definition by Yin
A case study in an empirical inquiry that
investigates a contemporary phenomenon in depth
and within its real-life context, especially when the
boundaries between phenomenon and context are
not clearly evident (Yin 1981)
+high number of variables of interest.
+relies on multiple sources of evidence, with data to
converge by triangulation.
+benefits from the prior development of theoretical
propositions to guide data collection and analysis. (Yin
2009)
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Case studies rely on
theoretical instead of
statistical generalizability
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› Source: Hillebrand, B.; Kok, R.A.W.; Biemans, W.G. (2001): Theory-Testing Using
Case Studies: A Comment on Johnston, Leach, and Liu. Industrial Marketing
Management 30(8).

Characteristics of the
case study
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1. The problem statement is based on ‘what’, ‘why’, or ‘how’ questions.
2. One or more cases are included in the research project
3. One or more units of analysis are involved in the research project
4. The unity character of the phenomenon that is to be examined should
remain intact. That is, all relevant factors are examined in their relative
context.
5. There should be strived for in depth information by collecting a broad
sequence of data.
Source: Richardson in Wester & Maso, 1991

You can only study a limited
number of cases
 Case study analysis focuses on a small number of
cases that are expected to provide insight into a
causal relationship across a larger population of
cases.
 This presents the researcher with a formidable
problem of case selection. (Gerring 2007)
 Which cases should be chosen?
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Five rationales for single-
case designs
1. The single case represents the critical case in testing a well-
formulated theory
2. The single case represents an extreme case or a unique
case
3. The single case is the representative or typical case
4. Revelatory case
5. Longitudinal case
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Source: Yin (2009)

1 Typical case
 How to match people’s capaciticities to the tasks
to be done can be illustrated by studying High
tech IT companies like Google
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2 Extreme case
Studying a non-authorian way of direction a
company can be done by looking at the famous
example :
Semco in Brasil
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3 Revelatory case
The Iceland bankrupt is a revelatory case of how the
bank system is (not) working
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4 Longitudanal case
Studying Air France-KLM is interesting in terms of
how a company is developing
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A mixed-methods approaches to case
selection
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Source: Seawright & Gerring 2008

Multiple case studies are generally considered
more “robust” but resource-intensive
 “While there is no ideal number of cases, a
number between 4 and 10 cases usually works
well.” (Eisenhardt 1989: 545)
 Most important: what is the added value of a next
case for your research
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Ex: Take Unilever:
as a MNC: single case
as Business units: multiple
cases
as Departments within
Business units:
embedded multiple cases
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Single vs. multiple case study
designs
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Source: Yin (2009): 46

A study on Corporate Social
Responsibilty at Nestle: try to compare
1. Compare with another company
on how they work
2. Compare what Nestle is doing with
the theories on CSR
3. Comparre how different Business
Units of Nestle work with CSR
4. Compare with……
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The essence of a case
study: To compare
Scient. emp. =
Scientific empirical

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Qualitative research strategy :
One of the options:
Action Research

WHAT IS ACTION
RESEARCH?
The fundamental aim of action research is to improve practice
rather than to produce knowledge.
However it is possible to produce knowledge!
Elliott (1991)
Based on: Lewin, Kurt Action research and minority problems (Journal of Social Issues, 2:
34-46, 1946).
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Managers can develop
also theory
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Ex: TenKate vetten
 Ten Kate produces sustainable animal fats and proteins for
producers of food and animal feed, for the oleochemical
industry and for use in a range of industires.
 What does the management learn from selling their products in
the Chinese market and what if they enlarge this market to
Vietnam?
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Reflecting in and on Action (Schőn
1987)
In Action:
practising, influences decisions and care at that moment,
Promotes skilled and flexible responses of the expert
practitioner
On Action:
After the event, views different interventions, adds
knowledge
Promotes development of professional skills and
knowledge

Levels of Depth within
Reflection
 Level 1
 Descriptive Writing
 Level 2
 Descriptive account with some reflection
 Level 3
 Reflective writing 1 -
 Level 4
 Reflective writing 2 -
Ref: Moon,J. (2004) A Handbook of Reflective and Experiential Learning: Theory and Practice. London: RoutlegeFalmer
Surface
Deep
where there is a willingness to be critical of the
action of self or others with some self questioning
where a metacognitive stance is taken, the view of
others is taken into account and the role of emotion
in the art of reflection is understood.

Varieties of Grounded Theory
Development
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….. often begins with specific observations and measures,
tries to detect patterns and regularities in the empirical
world, formulates some tentative hypotheses to explore,
and finally ends up developing some new part of a theory.
35
Research with an open question (see also lecture 2).

Don’t use research to tell you what you already
know…use it to tell you what you don’t know.

The Big Picture Induction
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Mass of Data
A theoretical
explanation
Distilling the
essential features
Clarification and
synthesis
Adapted from Richards (2009, pages 139-40)

The X-Ray View
 Construct and test an
explanation
 Reduce the complex
picture to what matters:
to the bones and sinews
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The Animated Model
Develop a model of the interplay of factors
 by teasing out the relationships between elements
of your study and
 by explaining process behind it.
May aim at elaboration of a situation or construction
of a more generalizable theory.
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Examples:
 What caused the financial crisis in 2008?
 Why went Lehman Brothers bankrupt in 2008
 Why did the oil tanker Exxon Valdez sink in 1989
which led to one of the biggest oil spills ever?
 What created the melt down in nuclear plant in
Fukushima?
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Thinking on data in the
context of an open
research question:
 In this context all kind of information can be classified as
data
 As long as you can describe it
 You need sources
 show it or at least have the option to show it to the public
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Research on Labour relations
in huge construction projects
New power station in the Eemshaven
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Ex: Thinking on data in the context of an
open research question:

New power station;labour
relations
 Transport union FNV, one of the parties to the new
collective agreement, launched legal
proceedings against the company hiring the
Polish workers (800) arguing that they were
entitled to the same rates of pay as Dutch workers
covered by the agreement.
 What is the source?
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New power station;labour
relations
 Polish workers building a power station on the
coast of the Netherlands at Eemshaven are to
receive pay rises of between 5% and 20% after
agreement was reached in the Dutch haulage
sector guaranteeing equal pay for foreign
workers.
 What is the source?
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Sources of Data
 Open-ended questions
 Logs, journals, or diaries
 Observations
 Stories
 Case studies
 Individual ‘interviews’/Oral exams
 Discussion groups/Focus groups
 Websites https://www.essent.eu/content/about_essent/about_essent/energy_generation/power_plants/index.html

New power station but
also elsewhere
so compare with other
sites!
 This phenomenon can also be found in the agricultural
sector, goods transportation, the shipbuilding industry,
supermarket distribution and large construction sites such as
those at Eemshaven and Maasvlakte 2, a major civil
engineering project.
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Coding
 Coding is analysis
 Codes are tags or labels for
assigning units of meaning to the
descriptive or inferential
information compiled
 It is the meaning that matters
Mass of Data
A theoretical
explanation

Processes of Analysis: coding
 To do this we classify, sort, and categorize,
 we break the data down and channel it into
relevant categories for analysis.
 This enables us to begin to make connections,
and comparisons between different parts of the
data
Mass of Data
A theoretical
explanation

Process of Analysis
1. Description
2. Classifying and Categorizing
3. Finding Concepts
4. Connecting Concepts
› Analysis is not necessarily sequential, rather the
process often involves a series of repetitive
movements back and forth between the different
aspects of analysis and the data

Grounded Theory (1)
Definition:
• The intent of grounded theory is to generate or
discover a theory – an abstract analytical
schema of a philosophy that relates to a
particular situation.
We use sensitizing concepts
• Sensitizing Concepts are “theory-
embedded” notions used by the
researcher to collect empirical data
November
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2024

Data analysis in grounded theory
studies (after Strauss & Corbin, 1998)
 OPEN CODING. Form initial categories of information
about the phenomenon being studied from the initial data
gathered.
Within each category, you look for several subcategories
(referred to as properties) and then for data to
dimensionalize.

Data analysis in grounded theory
studies (after Strauss & Corbin, 1998)
 AXIAL CODING. Involves assembling the data in new
ways after open coding. A coding paradigm (logic
diagram) is then developed which:-
 identifies a central phenomenon;
 explores causal conditions;
 specifies strategies;
 identifies the context and intervening conditions;
and
 delineates the consequences.

Three requirements for
data analysis:
1. It should meet the goals of your project,
answering your research question.
2. It should offer analysis, not just description.
3. It should offer at least a new local theory or
explanation.
11/05/24
Adapted from Richards (2009, page 138)

Problems in using ‘pure’ grounded
theory
 It is not possible to start a research study without some pre-
existing theoretical ideas and assumptions / ´sensitizing concepts´.
 There are tensions between the evolving and inductive style of a
flexible study and the systematic approach of grounded theory.
 It may be difficult in practice to decide when categories are
‘saturated’ or when the theory is sufficiently developed.

Visual and audio data
 Non-textual data in qualitative research
 Video data
 Images as qualitative data
 Audio data in qualitative research
56

Visual Methods
1. Images can be used to capture what is hard to
put into words.
2. Images can make us pay attention to things in
new ways..
3. Images can enhance empathic understanding
and generalizability through trustworthiness.
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Adopted from Weber (2008)

Images as qualitative
data
 In qualitative research, images can serve as a potent tool for
understanding human experiences, behaviors, social dynamics,
and cultural phenomena.
 They provide an alternative lens through which to view the world,
offering a rich, nuanced perspective that textual data may
sometimes fail to capture.
 The use of images in research can include photographs,
drawings, diagrams, maps, or any other visual materials that can
help researchers gain insight into their subject of interest.
58

Collecting images
 Collecting image data involves either creating images as part of
the research process or using pre-existing images.
 In the former approach, researchers or participants may take
photographs, make drawings, or generate other visual materials
during the study.
 This method can offer a particularly rich source of data because
the act of creating an image can bring forth subconscious
thoughts, emotions, and perspectives.
 Pre-existing images, on the other hand, can include anything from
historical photographs to social media images, advertisements,
and artworks.
 The key here is to ensure that these images are relevant to the
research question and can contribute meaningful insights.
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Image analysis
 Analyzing images in qualitative research involves the careful
examination and interpretation of visual data to
 discern patterns, themes, and meanings.
 Researchers generally employ a two-step process: description
and interpretation.
 In the description phase, researchers meticulously document the
physical details of the image, such as its color, size, form, and the
subjects or objects it contains.
 This stage is purely observational, with researchers providing a
factual account of what is present in the image.
 These descriptive details can be jotted down in memos,
and qualitative data analysis software such as ATLAS.ti makes it
even easier to analyze images as the images can be viewed and
any segment of the image can be selected and coded.
60

Image analysis (cont.)
 During the interpretation phase, researchers delve deeper,
attempting to understand the meaning behind the visual
elements in the image.
 They may analyze the relationship between different parts of the
image, consider the use of symbols, study the context in which
the image was created or is viewed, and reflect on their own
responses to the image. Ex: Hieroglyph Egypt inside Pyramids and
caves.
 Here, researchers often draw on existing theories or frameworks to
guide their interpretation, which can be developed through
memo-writing and coding.
61

Image analysis (cont.)
 Two common analytical frameworks used for images include
semiotics, which looks at the signs and symbols within the image,
and discourse analysis, which examines the image in the context
of social and cultural narratives.
62

Considerations for image
analysis –pros/cons (+ )
 Incorporating images into qualitative research can have
numerous benefits.
 They can provide a wealth of detail and convey aspects of
reality that are difficult to capture in words alone, including
emotions, moods, atmospheres, and tacit knowledge.
 Images can facilitate participants' expression and engagement,
particularly when dealing with sensitive topics, complex
concepts, or with populations who might struggle with verbal
communication.
63

Considerations for image
analysis ( -)
 However, the use of images also presents several challenges.
 Analysis can be time-consuming and requires a certain level of
expertise in visual literacy.
 The interpretation of images is inherently subjective, and images
can sometimes be ambiguous or open to multiple interpretations.
 Moreover, cultural differences can impact how images are
perceived and interpreted, which researchers must carefully
consider in multicultural studies.
64

Ex. Image: What do we
know about management
teams
 What can we learn from how
they are presented?
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L4 4 qualitative research techniques and open question.ppt

Key Aspects of Images (I)
 They are not objective, they are visual
representations of reality, not direct encodings of
it!
 They have materiality and immediateness (they
can represent objects).
 They make things explicit.
 They are influenced by the social, cultural and
historical contexts.
69

Key Aspects of Images (II)
 The relationship between reality and image has
the potential for systematic distortion.
 Senders can shape / manipulate perceptions,
e.g. advertisements, propaganda.
 The ‘digital revolution’ makes the line between
reality and fiction hard to detect!
 Receivers’ interpretation is highly contextual.
72


ahold1.jpg
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73
ahold1.jpg
Albert Heijn: executive team

Albert Heijn: executive team
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74

When to Use Visual
Methods?
 Visual methods generally produce ‘thick
descriptions’.
 Best if:
 Phenomenon is complex.
 Research aims at in-depth detailed understanding.
75

Analysis
 Take into account the context!
 How has the image been constructed and
positioned?
 Examine textual features
 How is the image arranged, what colours are used?
 Make explicit intertextual references
 Become aware of connotations, tone, meanings
76

Forms of Image-based
Research
 Researcher-found visual data
 Cartoons, newspaper photographs
 Researcher-created visual data
 Respondent-generated visual data
 Drawings, photographs, videos provide
understanding of repondents’ worlds
 Representational imagery
 Broader manifestations of culture
Images can be used independently or to
supplement to other forms of data.
77

How do managers organize
their meetings?
 What can we learn from how
they are presented in their
board meeting place ?
 (If needed an extra example)
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References
 Spencer, S. (2011). Visual Research Methods in the
Social Sciences: Awakening Visions. Oxon:
Routledge
 Weber, S. (2008). Using Visual Images in Research.
In Knowles, J.G. and Cole, A.L. (eds) Handbook of
the Arts in Qualitative Research: Perspectives,
Methodologies, Examples and Issues. London:
Sage
85

Audio data in qualitative
research
 How do varieties of a language sound different from each other?
 Do passengers on a train or an airplane respond differently when
the announcement is spoken by a man or a woman?
 At what decibel level does random noise become a problem and
disrupt people's work or conversations?

To answer these questions, audio data offer a unique and
valuable source of information.
86

Audio data in qualitative
research
 Audio recordings provide access to elements such as tone, pace,
volume, and pauses, which may be lost in written transcriptions.
 Audio data can come from interviews, focus groups, and
naturalistic recordings.
 You can also take audio data from non-traditional data sources
like voice notes, podcasts, music, and more.
87

Audio data collection
 Audio data are typically collected through recording devices
or applications.
 Often, researchers record in-person or remote interviews and
focus groups.
 However, audio data can also be collected by recording
naturalistic settings, such as meetings, public spaces, or events,
to understand social dynamics, communication patterns, or
ambient soundscape.
 In certain research projects, participants may be invited to
contribute audio diaries or voice notes, offering personal
narratives and reflections in their own time and space.
88

Audio analysis
 Unlike images or text, audio data are time-based and
sequential, which poses unique challenges and opportunities for
analysis.
 Researchers can choose between verbatim transcription, where
the audio is converted into text for analysis, or direct analysis of
the audio data, where the focus is more on the sonic and aural
aspects.
89

Audio analysis: transcriptions
 In transcriptions, the audio data are transformed into text, which
can then be coded and analyzed using traditional qualitative
analysis methods.
 The transcription should be as detailed as possible, capturing not
only what is said but also how it is said, including nuances such as
hesitations, laughs, emphasis, or overlaps in conversation.
 However, transcription inevitably loses certain elements of the
original audio, such as tone, accent, or background noises.
90

Direct analysis of audio
data
 Direct analysis of audio data focuses on the auditory
experience.
 Researchers listen for patterns in sounds, silences, tones,
accents, pace, or rhythm.
 This type of analysis can be especially valuable in
sociolinguistic studies, conversation analysis, or research
focusing on soundscapes or musical elements.
 Listening and re-listening to the audio are crucial steps in this
process.
91

Ethical Considerations in Data
Collection
• Protect dignity, privacy and well-
being
• Insist on voluntary participation,
informed consent and confidentiality
• Avoid: Outsider arrogance,
dishonesty, and asymmetrical
relationship between researcher and
participants
92

L4 4 qualitative research techniques and open question.ppt

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