1. Introduction to Qualitative Research Methods

1. Introduction to Qualitative Research
By
Dr. Abebe Megerso
(BSc/PH, MPH/Epid., PhD. Asst. Prof. of Epidemiology)

2. What are we going to address in this part?
• Part I: (already addressed)
– Quantitative Research Methodology (Its full content),
• Part II:
– Qualitative Research Methodology (Its full content),
– Research Ethics (the basics)
2

3. Outlines of this session
• The aim of this part of the course is to enhance our
capacity to conceptualize, design & conduct qualitative
research in the health sciences,
– Philosophical paradigms in Research,
– Definitions & Concepts of qualitative Research methodology,
– Qualitative Study Designs,
– Sampling in Qualitative Methods,
– Qualitative Data Collection Methods,
– Data Quality & Trustworthiness,
– Qualitative Data Analysis,
– Qualitative Data analysis software,
– Writing Qualitative report,
3

4. The Premises for Qualitative Research
‘Not everything that can be counted counts, & not
everything that counts can be counted’ (A. Einstein).
Qualitative research follows a philosophical
paradigm different from that of Quantitative
Research.
4

5. Philosophical Paradigms in Research,
• What is philosophy?
– Etymological definition (Origin of the word)
• From Greece words Philia (love) & Sophia (wisdom),
• Hence Philosophy = Love of wisdom (Sokrates; 470-399 BC),
– Wisdom - the ability to make sensible decisions & give good advice because of the experience &
knowledge that you have (Oxford Dictionary).
– Academic definition:
• Asocial institution dedicated to discovery, transmission & preservation of
knowledge (Plato; 424-348 BC),
• It includes:
– Metaphysics (Ontology)- the theory of Reality,
– Epistemology - the theory of Knowledge,
– Axiology – the theory of Value/Ethics,
– Methodology/Logic – the theory of Reason,
5

6. What is philosophy?...
• Philosophy is a guiding framework that links data to theory &
informs designs made in a research,
– An ‘empirical’ & ‘theoretical’ parts of knowledge production,
– It is the process of rational thinking & logical argument about a
phenomenon, e.g. Is there what is call ‘truth’? Is this philosophical?
• Adopting a philosophical perspectives prior to conducting data
collection helps guide us with a theoretical view of the world
that necessarily enriches our research endower, (McLachlan &
Garcia, 2014)
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7. Philosophical Assumptions/Parts
7

8. Ontology
• A branch of philosophy that studies the nature of reality.
• Ontology asks questions like:
– What is reality?
– Is/are reality one or multiple?
• Acknowledging the existence of multiple realities is at the
core of all qualitative researches,
• This assumption of multiple realities guides the remaining
branches of philosophy,
8

9. Ontological Approaches
• Objectivism/Realism:
– There is an ‘objective’ reality out there that we can go out & find,
– The concepts we use to understand things exist independently of us,
– There is one accurate description or explanation  Quantitative,
• Subjectivism/Relativism:
– The world is not separable from our cognition (thoughts),
– Social reality is a creation of our consciousness, projections &
interpretations,  Qualitative Researches
9

10. Epistemology
• It is a branch of philosophy that studies knowledge or
knowing,
• It addresses questions like:
– ‘What & How do we know what we claim to know?’
– How is knowledge about reality is made known?
• Some Epistemological assumptions:
– Some say: Knowledge is out there waiting to be discovered,
– Others say: people develop knowledge based on their perceptions
& experience,
– Still some say: all knowledge is relative, a mere social construction;
it is what ever we say it is.
10

11. Epistemological Approaches
• Positivism/Objectivism:
– It is possible to observe the world in a value of natural way,
without distortion through the act of observation,
• Interpretivism/Subjectivism:
– Our observations of the world are influenced throughout
conceptual personal belongings, that has social & cultural
origins,
11

12. Epistemological aspect of Qualitative Research
• Qualitative researchers act on the ontological stance of
multiple realities by creating studies that allow
participants to voice those realities,
– Therefore, subjectivism with due rigor or interpretivisim is at
the core of qualitative researches,
12

13. Axiology
• A branch of Philosophy that studies Values or ethics in
research,
• It answers the question like:
– ‘What do we value?’
– ‘What values should guide our research?’
– ‘What values or outcomes result from our research?’
– ‘Can the research be neutral or our personal values shape how we
do the research?’
– ‘Should we seek just to understand or seek to change the world
for the better?’
13

14. Axiology …
• Qualitative researchers acknowledge the value-laden
nature of their work in qualitative research & acknowledge
researchers as research instrument,
– That is, researchers are guided by strict research ethics in order
to control the subjectivity in the conduct of the research,
14

15. Methodology
• Methodology is the theory of the application of methods
or techniques,
– Methodology is overarching strategy & different from method
which is individual technique,
– It moves the researcher from the abstract to concrete & guides
actions that reflects otology, epistemology & axiology,
• It is influenced by philosophical underpinnings,
– Shapes the nature of empirical inquiry: how should I look for the
data? Where should I look for them?
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16. Philosophical Assumptions … The paradigm
16

17. Definitions & Concepts of Qualitative Research
• Qualitative research is a strategy for systematic collection,
organization & interpretation of textual data,
• It is a systematic approach to describe life experiences & give
meanings,
• It is a research strategies used to feature contexts of human
behaviors, opinions, values, norms …
• It seeks to describe & analyze culture & behavior of humans & their
groups from the point of view of those being studied,
17

18. Definitions & Concepts …
• Qualitative research is an inquiry process of understanding
based on district methodological traditions of inquiry that
explore human problem in which the researcher builds a
complex & holistic picture, analyses words, reports
detailed views of informants & conducts the study in a
natural setting,
(Creswell JW, 1998)
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19. Goal of Qualitative Research
• The goal of qualitative research is the development of
concepts which help us to understand social phenomena
in natural (rather than experimental) settings, giving due
emphasis to the meanings, experiences, & views of all the
participants.
(Pope & Mays, 1995)
19

20. Goal of Qualitative Research …
• It is to view events, actions, norms & values from the
perspective of the people you study,
• It aims to providing a comprehensive & holistic
understanding of the social setting in which the research is
conducted,
(WHO, 1994)
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21. Dimensions of qualitative methods
• Understanding context (e.g.)
– How economic, political, social, cultural, environmental &
organizational factors influence health understanding of people?
– How people make sense of their experiences of health & disease?
• Understanding interaction(e.g.)
– How the various actors involved in different public health
activities interact each other?
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22. Qualitative Vs. Quantitative
22

23. Qualitative Vs. Quantitative …
23

24. Basic Differences: Qualitative Vs. Quantitative
24

25. Possible alignment b/n Qualitative &
Quantitative Research
1. Sequential: one precedes the other on the same research
question,
2. Concurrent: both occur at the same time on similar or the
same research question,
3. Neither: both address diffident but related research
questions ,
– this is the case in PhD dissertations,
25

26. Qualitative Research: The
Non-Linear Approach
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27. Use of Qualitative Research
• Describes & explains processes in local contexts,
• Describes chronological flow of events,
• Useful in conceptions/generation &/or revision of conceptual
frameworks,
• Determine the socio-political & ecological context in which actions &
behaviors take place,
• Designing & evaluating study questionnaires (research instruments),
• Designing locally & culturally sensitive intervention programs,
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28. Principles for Designing Qualitative Research
1. Natural setting
2. Holism
3. The human as research instrument
4. Emergent design
5. Saturation or redundancy of information,
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29. Natural setting
• Meaning does not exist in a vacuum but within a context,
• Qualitative research aims at discovering the meaning that
people assign to events, activities or phenomena,
• Natural context influences the perspectives, experiences, &
actions,
• Participants in the qualitative approach need to be free
from any control & data are collected in their natural
environment,
29

30. Holism
• Qualitative research assumes that the whole is more than
the sum of individual parts,
– It is not like simple frequency 0r proportions,
• Understanding contextual meaning requires a holistic
approach - taking into account a multiple contextual factors
(social, historical, physical, etc.)
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31. Human as Research Instrument
• The researcher need to be involved in every step of the
research process from initiation of the process through data
collection & analysis to report writing
• The researcher need to cope with the circumstances- be
responsive, flexible, adaptive & a good listener
• Major decisions about study design are often emergent
31

32. Emergent Design
• A qualitative research process is flexible, emergent &
iterative,
– every thing cannot be fully spelled out at the start,
• Aims at learning from every step of the research,
• Several circles take place before finishing the set of
interviews – data analysis can take place concurrent with
data collection,
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33. Saturation or Redundancy
• In qualitative research, there is no statistical calculation of
required sample size at the beginning of the study,
• A stage where additional interview or observation is not
believed to add new information - enough is enough,
• A point at which the research circle is discontinued,
33

34. Research Question
• Well defined research question are key to good research
b/s:
– Define the research purpose,
– Determine appropriate design & method,
– Guide study planning,
– Frame analysis & findings,
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35. Major Qualitative Research Design
• Qualitative research designs include:
– Ethnography
– Phenomenology
– Grounded theory
– Narrative Approach
– Case study: describing experience,
– Pragmatic approaches (e.g. Interpretive phenomenology)
• But, there many other variants which can not fit exactly in
to any of these designs,
35

36. Choice of Qualitative study Designs
36

37. Ethnography
• Ethnography means “portrait of people”
– The goal is to tell the whole story of a group’s daily life, to identify
the cultural meanings, beliefs & patterns of the group,
• Studies culture of organizations, programs & groups of people
with common social problems such as smoking & drug
addiction (shared experience),
• It helps to develop cultural awareness & sensitivity &
enhances the coverage & quality of services e.g. in a given
organization,
37

38. Phenomenology
(Edumend Hisserl, 1900)
• It is a study of structure of consciousness as experienced
from the first person view point,
(Stanford Encyclopedia of Philosophy)
• It is a science of phenomena as distinct from being (Ontology); that
division of any science that describes & classifies phenomena,
(Oxford English Dictionary)
38

39. Phenomenology …
• Describing things that are part of the world in which we
live: events, situations, experiences or concepts,
• Phenomenological research investigates individuals’ lived
experience of events for example the experience of caring
for someone with AIDS/ terminal cancer
– meaning of caring in that context
– the components of caring
– the impact of caring: negative & positive
39

40. Types of Phenomenology
1. Transcendental constructive:
– Studies how objects are constituted in
pure or transcendental consciousness,
2. Naturalistic Constructive:
– How consciousness takes in the world
of nature,
3. Existential:
– Studies concrete human existence,
4. Generative Historicist:
– How meaning is generated in
historical process of collective
experience over time,
5. Genetic:
– Studies the genesis of meanings
of things,
6. Hermeneutic:
– Studies interpretive structure of
experience,
7. Realistic:
– Studies the structure of
consciousness assuming real
world,
40

41. Grounded Theory
• A qualitative research that add to the existing body of
knowledge through developing new theories about a
phenomenon,
– Example: the theory of grief process that manifested a series of
stages & each stage was characterized by certain responses:
denial, anger, acceptance & resolution,
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42. Case studies
• Case studies are in depth investigations of a single or small
number of units,
• The most common uses of the case study method is
evaluation of a service,
• Case study may offer rich & in depth of information not
usually offered by other methods,
• Ranges in complexity:
– From the simplest: description of a single event or occurrence,
– To a complex: analysis of a social situation over a period of time,
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43. Sampling in Qualitative Research
• You do not have to interview everyone (in a community,
hospital, neighborhood) to get a “good” sample,
• The specific set of people to be researched, interviewed etc.
• How do I choose?
– What is my research objective?
– Of what kind of people is my subset comprised?—does my
sample adequately reflect the diversity & variation of my
population
– Saturation: am I still get new information & results?
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44. Homogenous Sampling
• Similar groups of people with similar backgrounds,
• Reduced variation,
• Simple analysis,
• Used often in focus groups,
– Example: If you are studying parenting program participation,
the whole group might be single moms,
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45. Typical Case Sampling
• Shows what is typical-excludes deviant or intense cases,
• Helps to give an overview to people with no background,
• Helpful for very large or complex projects,
• Shows critical issues that should be addressed based on
who you sampled,
– Example: You want to start a health program related to diabetes
in Adama, including general DM patients,
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46. Extreme or Deviant Case sampling
• Unusual cases related to your research, the outliers,
• Notable successes or failures; top of the class/bottom of
the class,
• Information on something unique i.e. (studying assaulted
women & you find women that have killed their abusers),
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47. Maximum Variation Sampling
• Picking a wide range, very diverse group,
• Looking for patterns & themes among a varied group,
• Document shared dimensions & unique variations,
– Example: “if you deliberately try to interview a very different
selection of people their aggregate answers can be close the
whole population’s”,
47

48. Intensity Sampling
• Not as exceptional as extreme/deviant
• Still the outliers
• Information-rich cases
• Usually researcher has previous knowledge of variations so
that he/she knows what is intense vs. extreme,
– Example: studying depression vs. psychosis
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49. Criterion Sampling
• All cases that meet the certain criteria will be included,
• All cases must be information rich to be included,
• Very helpful in measuring quality assurance: shows a system
defect or weakness,
– Example:
• To study academic institution quality of service: quality aware experts
should be sampled,
• Study on causes of infection after medical abortion; criteria, women with
an infection after a medical abortion
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50. Stratified purposeful sampling
• Helps to make comparisons,
• Samples of different subsets,
• Lends credibility to research,
– Example: If you want to study university students, pick a certain
number of students from each of the 4 years (sample of freshmen,
sophomores, juniors, & seniors)
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51. Critical Case sampling
• Looks at instances that will produce the most important
information,
• Must know what is a critical case,
• Should have good applicability & logical generalization,
– Example: You want to know how well people understand a new tax
law.
– Ask very educated people -- if they do not understand it, then
probably no one will.
– Or ask a very uneducated population, if they understand it, most
people will.
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52. Snowball Sampling
• Start with a few respondents & then ask them who else
might have or know about the issue under study,
• Football players, gardeners, mental health problems,
• Problem with such sampling method is that respondent
with higher network may recruit more sample which may
cause bias in the result,  false information saturation,
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53. Opportunistic sampling
• Flexible; as new information is received the sampling
group or site may change,
• See new chances to deepen or broaden the sample,
– Example: interviewing homeless people at a shelter, one man
tells you where most of the homeless people sleep, so you add
this site to where you interview,
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54. Quota sampling
• Quota sampling, sometimes considered a type of
purposive sampling, is also common.
• In this sampling, we decide while designing the study how
many people with which characteristics to include as
participants.
• Characteristics might include age, place of residence,
gender, class, profession, marital status, etc.
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55. Volunteer sampling
• Sample is made up of people who ‘volunteer’ to be involved
in research,
• Usually done through advertising,
• Helpful when the population you want to reach is divided
throughout a community,
– Example: Studying women about domestic violence & you ask
them to phone a number
– Can be quite biased: only women who go to the places you
advertise, who can read, with phones, with time, who think the
research is important, who feel safe talking
55

56. Convenience Sampling
• “Because they are there”: people closely surrounding you
• Weakest rationale & low credibility
• Highly biased: think the sample represents the whole
• Cheap, fast, easy
• Information poor,
– Example: When you ask your neighbors or co-workers to fill out
the questionnaire
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57. Key Informant Sampling
• Provide deep, important information about what is
happening in the community as a whole; knowledgeable,
expert
• They have access to this information based on personal
skills, profession, position in the community,
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58. Confirming or Disconfirming Sampling
• Usually done after some data has been collected & some
analysis has been done,
• It allows you to find more cases that add depth to the
research & “confirm” the results, OR
• Find cases that do not fit the expected results,
• Help researches find the limits of the study,
• Helps lend credibility to the study,
58

59. Random Purposeful sampling
• Small, random sample when the group is too large,
• Adds credibility to research,
• Because the sample is small => the result’s credibility is
NOT representativeness or generalizability,
– Example: study at a large drug rehabilitation center with 500
patients; the researcher randomly picks 50 without regard for
success/failure Combination or Mi,
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60. Combination or Mixed Purposeful
• Using more than one sampling technique,
• Meet several needs of the research,
• Makes the research more flexible,
• Triangulation,
– Example: using snowball sampling until a critical case is found
60

61. Qualitative Sampling techniques (summary of Some)
61

62. Qualitative Data Collection Methods
62

63. Interview
• An interview is asking questions, listening to, & recording
the answers
• Most commonly utilized data collection method in
qualitative research,
• When to use interview?
– To explore individuals perspectives & experiences,
– Address sensitive topics,
– Concerns about fear of reprisal/ punishment,
– When structured survey approach do not work
63

64. When to use individual interviews?
• When individual’s experience & unique interpretation of it
is of interest
• When a topic may be too sensitive to discuss in a group
• When respondents are too dissimilar to be meaningfully
grouped
• Types of interviews:
– In-Depth Interviews- IDIs
– Key Informant Interview (KII)
64

65. In-Depth Interviews- IDIs
• Qualitative interview is a process of two people
understanding each other:
– an emotionally loaded situation where researcher must be
sensitive & firm
– purpose is often to understand the lived daily world from the
subjects owns perspective - accessing insider’s perspective
– Interchange of views
– Exchange of sentiments, observations, ideas, opinion…
– Forms of interactions & level of reflections may differ depending
on the persons involved
65

66. Key Informant Interview (KII)
• Key Informant interview is an in-depth interview with a key
informant
– Key informant is an individual selected due to his/her
knowledge, previous experience & social status
• Selection is not random & there is potential for researcher
bias,
66

67. Types of Qualitative Interviews
• Can be any of the following or their combinations:
– Structured interviews
– Unstructured interviews
– Semi-structured interviews
67

68. Structured interviews
• Often used in quantitative research
• Same set of questions are asked, in the same order, using
the same words, to different interviewees
• Structured interviews are convenient for comparing
responses
68

69. Unstructured interviews
• Interviews without predetermined set of questions -
researchers & interviewees talk freely
• Often used in combination with observation
• Interview is flexible & highly responsive to individual
differences & emerging new information
• Researchers have to generate relevant questions based on
their interaction with the interviewees – very difficult &
requires experience
69

70. Semi-structured interviews
• Researchers prepare interview guides that consist of a set of
questions to initiate discussion
• Researchers generate other questions (probes) in
interesting areas of inquiry during the interviews
• Widely used as the qualitative interview method
70

71. Key Issues for Interview Guides
• It must be a framework for the interviewer,
• List of main questions & probes,
• Open, non directive,
• Interviewer may diverge to pursue an emergent idea in
detail,
• Interviewer may reword questions, drop/add questions &
change sequence,
• Things to avoid:
– Influencing respondents by asking leading questions,
– Moving quickly from question to another,
– Interrupting the informants, 71

72. Focus Group Discussion
• A qualitative data collection method in which one or two
researchers & several participants meet as a group to
discuss a given research topic
– Six to ten discussants per group for 30 min to 2hrs at convenient
places
– Encourages group interaction – participants can influence & be
influenced by other participants
– Complement other methods - Important to develop culturally
relevant questionnaire
72

73. Use of Focus Group Discussion
• Use FGD when:
– When group interaction will help address your research
question: bring out diverse points of view & contrast them in
real time,
– Characterizing social & cultural norms,
– When breadth of data is more important than depth,
– Topic is NOT sensitive (exploring potentially sensitive topics),
– Sharing & comparing information,
73

74. FGD Vs. Group Interview
• Focus groups rely on interaction within the group based on
topics,
• The key characteristic which distinguishes FGD is the
insight & data are produced by the interaction among
participants,
74

75. FGD Moderator skill
• Strong interviewing techniques,
• Keen observational skill,
• Ability to control & guide discussion,
• Ability to suppress own personal views,
• Respect for participants (active listening, eye contact,
concern for comfort),
• Tips for good FGD:
– Create rapport among group members first,
– Establish safe space, engage the hesitant,
– Be prepared to redirect.
75

76. FGD: Advantages
• Do not discriminate against people who can not read &
write,
• Encourages participants reluctant to be interviewed,
• Help researcher to know expressions & jargon,
76

77. Observation
• Purpose is to get close enough to study subjects to grasp
their point of view; perspectives held by study populations
• The researcher participate in & observe sociocultural
context & obtain insight about daily life
• Two types:
– Non-participant observer,
– Participant observer,
77

78. Non participant observation
• Observing without participating – collecting data without
interacting or reacting visibly to participants activity (non
reactive technique)
• Technique helps to see how something happens rather than
how study participants perceive it happening:
– E.g. how are clients received, how long is the waiting time …
• Quality of the data will depend on your ability to watch &
listen without interrupting the natural flow of activity,
78

79. Participant Observation
• Observer is directly interact with study population & their
activities
• Requires competence in getting closer to people without
making them feel uncomfortable with your presence
• Researcher participate in & obtain insight about daily life in
a given socio-cultural context: dual purpose
– E.g. mystery client technique
79

80. Advantages of Observation method
• Provides deep understanding of the general setting
• Allows to observe whether people do what they say they do
• Useful to access knowledge of subjects -subconscious
knowledge that they would not be able to verbalize in an
interview setting
• Useful to capture a phenomenon & its specific components
in greater detail
80

81. Disadvantages of Observation Method
• Time consuming
• Require good skill in local language
• Requires good memory & ability to take note
• Enormous data may be generated
• Time lag between observation & note taking is likely
• Expensive,
• Ethical concerns vs. Hawthorn effect,
81

82. Qualitative Analysis
• Qualitative analysis is perhaps the most difficult & integral part of a
qualitative study.
• On the whole, qualitative analysis has fewer set customs to follow than
quantitative analysis, as it:
 does not rest upon probabilistic interpretations;
 does not necessarily seek “generalizability”; &
 is often an ongoing process that informs the course of data
collection during a study.
82

83. Terms in Qualitative Data Analysis
• Code (characteristics): A single item or event in a text
– Similar to an individual response to a variable or indicator in
quantitative research,
• Concepts or themes: Idea categories that emerge from
grouping of lower-level data points,
• Theory: A set of interrelated concepts, definitions, &
propositions that presents a systematic view of events or
situations by specifying relations among variables,
83

84. Terms in Qualitative …
• Coding: the process of attaching labels to lines of text so
that the researcher can group & compare similar or related
pieces of information,  Data reduction,
• Coding sorts: compilation of similarly coded blocks of text
from different sources into a single file or report,
• Indexing: process that generates a word list comprising all
the substantive words & their locations within the texts,
84

85. Features of Qualitative data analysis
• Analysis is Circular not Linear (unlike in quantitative
research, this part takes 80-90% of the time)
• Iterative & Progressive: ongoing,
• Data collection & analysis is simultaneous,
• Complex & Time Consuming
– Volume of data - ensure all data are used
– Flexibility, interactive nature
– Interpretation & meaning,
• Level of analysis varies
85

86. • All qualitative data analysis involves the same four essential
steps:
1. Raw data management- ‘data cleaning’
2. Data reduction, I, II – ‘chunking’, ‘coding’
3. Data interpretation – ‘coding’, ‘clustering’
4. Data representation – ‘telling the story’,
‘making sense of the data for others’
Four Basic Steps
86

87. Data Analysis Spiral
87

88. Data Analysis Spiral …
88

89. • What is raw data management?
– The process of preparing & organizing raw data into meaningful
units of analysis:
• Text or audio data transformed into transcripts,
• Image data transformed into videos, photos, charts,
– As you review your data, you find that some of it is not usable or
relevant to your study…
Step 1: Raw Data Management
89

90. Raw Data Sample
Transcript of Interview Data Raw Data Overview
• I always wanted to get my doctorate but I never felt I had the time; then I
reached a point in my career where I saw that without the credentials, I
would never advance to the types of positions I aspired to..but I doubted I
could do the work. I wasn’t sure I could go back to school after so much
time. And did I have the time, with working & a family? These were the
things I struggled with as I looked for the right program.
• Um, ..finally starting the program with others like me, it felt surreal. Once
you switch gears from being an established administrator at a college to
being a doc student, you realize you lose control over your life. You are not
in charge in that classroom, like you are in your office. But also, once you
say you are a doc student, people look at you differently. And people at
work began to take me more seriously, ask for my opinion as if I now
possessed special knowledge because I was going for the doctorate. It was
the same information I had shared previously but somehow it had a
special quality? Its like magic!
• I can’t think of a particular example right now…
• Are some portions of
this transcript
unusable or
irrelevant? (blue)
90

91. • Get a sense of the data holistically, read several times
(immersion),
• Classify & categorize repeatedly, allowing for deeper
immersion,
• Write notes in the margins (memoing),
• Preliminary classification schemes emerge, categorize raw
data into groupings (chunking),
Step II: Data Reduction I
91

92. • Develop an initial sense of usable data & the general categories you
will create,
• Preliminary set of codes developed, cluster raw data into units that
share similar meanings or qualities,
• Create initial code list or master code book,
Winnowing
92

93. Chunks-Clusters Sample
Transcript of Interview Data
Chunking? Clusters?
• I always wanted to get my doctorate but I never felt I had the
time; then I reached a point in my career where I saw that
without the credentials, I would never advance to the types of
positions I aspired to..but I doubted I could do the work. I
wasn’t sure I could go back to school after so much time. And
did I have the time, with working & a family? These were the
things I struggled with as I looked for the right program.
• -finally starting the program with others like me, it felt surreal.
Once you switch gears from being an established administrator
at a college to being a doc student, you realize you lose control
over your life. You are not in charge in that classroom, like you
are in your office. But also, once you say you are a doc student,
people look at you differently. And people at work began to
take me more seriously, ask for my opinion as if I now
possessed special knowledge because I was going for the
doctorate. It was the same information I had shared previously
but somehow it had a special quality? Its like magic!
• Which sections of data are
broadly similar? (red for
credentials, blue for
personal struggles, green
for shift in identity)
• Which ‘chunks’ can be
clustered together to relate
to a broad coding scheme?
93

94. –The process of reducing data from chunks into clusters &
codes to make meaning of that data:
• Chunks of data that are similar begin to lead to initial clusters &
coding:
– Clusters – assigning chunks of similarly labeled data into clusters &
assigning preliminary codes
– Codes – refining, developing code books, labeling codes, creating
codes through 2-3 cycles
Step II: Data Reduction II
94

95. • Initial coding may include as many as 30 codes,
• Reduce codes once, probably twice,
• Reduce again to & refine to codes that are mutually
exclusive & include all raw data that was identified as
usable,
Coding Process
95

96. • A Priori coding
– Codes derived from literature, theoretical frames,
• In Vivo (inductive or grounded)
– Codes derived from the data by using code names drawn from
participant quotes or interpretation of the data,
• “Its like magic” is a phrase that could form the basis for a code category
Coding approaches
96

97. • Descriptive to Interpretative to Pattern Coding
– Moves from summary to meaning to explanation,
OR
• Open to Axial to Selective Coding
– Moves from initial theory to developing relationships between codes
for emerging theory,
OR
• First cycle to second cycle coding
– Moving from describing the data units to inferring meaning
Coding Levels
97

98. Coding Sample
Transcript of Interview Data
Chunking? Clusters? Coding?
• I always wanted to get my doctorate but I never felt I had the
time; then I reached a point in my career where I saw that
without the credentials, I would never advance to the types of
positions I aspired to..but I doubted I could do the work. I
wasn’t sure I could go back to school after so much time. And
did I have the time, with working & a family? These were the
things I struggled with as I looked for the right program.
• -finally starting the program with others like me, it felt surreal.
Once you switch gears from being an established administrator
at a college to being a doc student, you realize you lose control
over your life. You are not in charge in that classroom, like you
are in your office. But also, once you say you are a doc student,
people look at you differently. And people at work began to
take me more seriously, ask for my opinion as if I now
possessed special knowledge because I was going for the
doctorate. It was the same information I had shared
previously but somehow it had a special quality? Its like magic!
• Chunking to coding:
• Red for credentials – codes
include career goals CG,
career advancement CA
• Blue for personal
struggles- codes include
self-doubt SD, time
management TM
• Green for shift in identity –
codes include student role
SR, identity at work IW,
shift in control SC
98

99. • Descriptive to Interpretative to Pattern Coding
– Moves from summary to meaning to explanation
OR
• Open to Axial to Selective Coding
– Moves from initial theory to developing relationships between
codes for emerging theory
OR
• First cycle to second cycle coding
– Moving from describing the data units to inferring meaning
Coding Levels (revisiting)
99

100. • ‘Chunks’ of related data that have similar meaning are
coded in several cycles,
• Once coded, those ‘chunks’ become clustered in similar
theme categories,
• Create meaning for those clusters with labels,
• Themes emerge from those clusters,
• Interpret themes to answer research questions,
Step III: Data Interpretation & Themes
100

101. Themes
• Like codes, themes have labels/types:
 Ordinary themes – themes that a researcher might expect
to find
 Unexpected themes – themes that are surprises
 Hard-to-classify themes – themes that contain ideas that
do not easily fit into one theme or that overlap
 Major & minor themes – themes that represent the major
ideas & the minor secondary ideas
101

102. Themes Sample
Transcript of Interview Data
How do broad sections emerge
into thematic groupings?
• I always wanted to get my doctorate but I never felt I had the
time; then I reached a point in my career where I saw that
without the credentials, I would never advance to the types of
positions I aspired to..but I doubted I could do the work. I wasn’t
sure I could go back to school after so much time. And did I have
the time, with working & a family? These were the things I
struggled with as I looked for the right program.
• -finally starting the program with others like me, it felt surreal.
Once you switch gears from being an established administrator
at a college to being a doc student, you realize you lose control
over your life. You are not in charge in that classroom, like you
are in your office. But also, once you say you are a doc student,
people look at you differently. And people at work began to take
me more seriously, ask for my opinion as if I now possessed
special knowledge because I was going for the doctorate. It was
the same information I had shared previously but somehow it
had a special quality? Its like magic!
• How do you compile the
clusters into emerging
themes? (red for
credentials, blue for
personal struggles, green
for shift in identity)
• Begin to see themes
emerge: Getting the
degree, becoming a new
person, personal
achievement…
102

103. • Interpretation or analysis of qualitative data simultaneously
occurs,
• Researchers interpret the data as they read & re-read the
data, categorize & code the data & inductively develop a
thematic analysis,
• Themes become the story or the narrative,
Step IV: Data Representation
103

104. • Most common types of analytic approaches:
 Domain/Content
 Thematic
 Grounded theory/Constant comparative
 Ethnographic/cultural
 Metaphorical/ hermeneutical
 Phenomenological
 Biographical/narrative analysis
 Case Study, Mixed Methods, Focus Groups
Qualitative Data Analysis Types
104

105. Quality in Qualitative Research
105

106. Strategies for enhancing Validity
in qualitative Studies
• Prolonged engagement
– Spending sufficiently long period in the field:
• to build trust with the study participants
• To acquire cultural competence & become familiar with the context
• Researcher need to get a deep & complex understanding of the
phenomenon under study,
– Requires persistent engagement
– observe & interact in various contexts over time
106

107. Strategies for enhancing Validity …
• Triangulation
–Make conclusions based on multiple types of evidence
• Multiple data sources
• Multiple data collection techniques
• Multiple data analysis techniques
• Sorting Contradictory evidence
• emerging evidence help modifying existing theory
107

108. Computer Application software for Qualitative Data
Analysis
• Computer Application software are just aids, but no a
replacement for human mind, they do not do the analysis,
– They are to ease the burden of cutting & pasting by hand, creation
& insertion of codes in to text files, indexing, construction of
hyperlinks, & selective retrieval of text segments,
– But, relying too much on computers shortcuts will impede the
process by distancing the researcher from the text,
108

109. Qualitative Data Analysis Software …
• Qualitative Data Analysis Software provides tools to assist
with qualitative research such as transcription analysis,
coding & text interpretation, recursive abstraction, content
analysis, discourse analysis, & grounded theory
methodology.
109

110. Choice of the Software
• The choice to use manual, simple or more complex
software depends on:
– Type & amount of the data to be analyzed,
– Time to learn Vs. time to analyze,
– Level of analysis (Simple or detailed),
– Desired “closeness” to the data,
– Any desired quantification of results,
– Individual or working as a team,
– Any cost constraints or access to the software,
110

111. Common qualitative software
• Commercial:
– Atlas ti 6.0 (www.atlasti.com)
– HyperRESEARCH 2.8 (www.researchware.com)
– MaxQDA (www.maxqda.com)
– QSR Nvivo (www.qsrinternational.com)
– Weft QDA (www.pressure.to/qda) …
111

112. Some of the Top Free Qualitative Data Analysis
Software
• Ms Word
• FreeQDA,
• QDA Miner Lite,
https://www.youtube.com/watch?v
=TLoMllTCqtc&t=618s&pp=ygUDUU
RB
• RQDA,
• ConnectedText,
• Visão, Aquad,
• Weft QDA,
• Cass&re,
• CATMA,
• Compendium,
• ELAN,
• Tosmana,
• fs/QCA
112

113. QDA Miner Lite
• QDA Miner Lite is a free computer assisted qualitative analysis
software, which can be used for the analysis of textual data such as
interview & news transcripts, open-ended responses, etc. as well as
for the analysis of still images.
• It offers basic CAQDAS features such as:
– Importation of documents from plain text, RTF, HTML, PDF as well as data
stored in Excel, MS Access, CSV, tab delimited text files,
– importation from other qualitative coding software such as Altas.ti,
HyperResearch, Etnograph, from transcription tools like Transana &
Transcriber as well as from Reference Information System (.RIS) files.
• It also provides intuitive coding…
113

114. Features of Qualitative Data Analysis Software
• Content Search:
– This feature allows users to conduct qualitative research using
the research methods mentioned in the above section.
– The most effective software tools use a wide range of search
methods to gather qualitative data.
– They can extract content from video sources, audio files, text
documents, graphics, & other sources of qualitative data.
114

115. Features of Qualitative Data Analysis Software ...
• Data Visualization & Reporting:
– The best qualitative data analysis software programs allow users
to visualize all forms of electronic data including interviews,
surveys, pictures videos, & bibliographical data.
– Most qualitative data analysis applications allow data analysists to
create reports depending on the needs of the organization.
– Even non-technical users can create line charts, scatter plots, &
geographical maps.
115

116. Features of Qualitative Data Analysis Software ...
• Storing & Coding:
– QDA software also includes coding tools that allow data analysts
& other software users to perform different forms of coding
such as keyword & text coding.
– They allow you to systematically code data in different formats &
categories.
116

117. Features of Qualitative Data Analysis Software ...
• Data Linking:
– This feature allows users to form clusters, networks or categories
of data.
• Data Mapping:
– QDA software makes it possible to map data to support theories
& depict findings.
117

118. Writing Qualitative Result
• Qualitative research generates rich information-thus
deciding where to focus & the level of sharing is very
challenging,
– Academic vs. programmatic or practice …
• The format varies depending on the purpose of the report &
the method of analysis preferred,
– Content, thematic, narrative, discourse …
• Use of quotes for description, direct link to data &
credibility,
– Limit the report to points relevant to your research questions,
118

119. Result writing in Qualitative Research …
• You are trying to articulate your findings in a manner
comprehensible to others,
• To convince your readers, you need to explain
– The appropriateness of the data,
– The correctness of their interpretation,
– The robustness of your evidence,
– The logic of your reasoning,
119

120. Result writing in Qualitative Research …
• How you write up your results may be guided by:
– Your philosophical assumptions,
– Your methodological approach,
– Political considerations,
– The data themselves (e.g. use of internet resources vs.
narratives),
120

121. Styles of qualitative writing
• There are four identified writing styles, (Boeije, 2010)
– Realistic,
– Confessional,
– Subtle realistic,
– Narrative,
121

122. The Realistic style
• The researcher is mostly invisible:
– Use of the passive voice,
– Research seems invisible to the process of research (common in
scientific writing),
122

123. Confessional style
• Researchers profile themselves explicitly as the person
who did the research,
– Personal reports including side-tracks, errors, experiences &
emotions,
123

124. Subtle-realistic style
• Researchers make clear statements on social reality but
takes responsibility for them,
– Do not take themselves out of the picture,
– Understand that it is their interpretation that matters,
– Reflect on their work, aware of quality issues & are self-critical,
124

125. Narrative style
• Reporting often includes postmodern theory & related
epistemology,
– May experiment with the ‘plotting’, the author’s stance & the
characters of the participants, voices & rhetoric.
– An attempt to reflect the complex nature of research & power
issues that surround social research,
125

126. Things to consider in writing results
• Your relation to & presentation of participants
– Overly distant to stay objective …
– Take on & advocate the view of the people you study,
– Personal biases (like some people more than others)
126

127. Structure of the final report
• Some scholars in the field recommend the following:
(Morse & Field,1996)
– Start when you know what you want to write,
– Start by writing your results,
– Construct the reference list,
– Write the methods section,
– Rewrite the literature section to set up your findings,
– Write the discussion connecting your literature & results,
– Write your introduction,
– Finally, write your abstract,
127

128. Result/Interpretation
• The story line – think of your paper as a story, but don’t
keep the results till the end,
• Rather think about how you can communicate the ‘story’
after you’ve described your results early on,
• Main thing is to convey things in a manner that is logical &
persuasive,
128

129. Result/Interpretation …
• Interpretation is the act of identifying & explaining the core
meaning of the data,
• It is organizing & connecting emerging themes, sub-themes
& contradictions to get the bigger picture-what it all means
• In this section, provide the interpretations & implications,
• Be based only on the findings reported in the result section;
if any new idea emerges, go back to the result & include
there,
129

130. Methods
• Was coding use? If so, what types & how were they
developed?
• Was software used? For what purpose(s)?
• How were themes/concept identified in the data?
• How did you interpret them?
• How were conclusions drawn?
130

131. Introduction
• What does the research area entail?
• What are the current issues?
• How has the area been examined, what methods were
used & were they appropriate?
• Which aspects have not been sufficiently investigated?
• How does your research question fit in & what have you
found?
131

132. Thank you !!!