How to do qualitative writing and analysis

1. The Science and Art of Qualitative
Writing and Analysis
Dr Dylan Kerrigan, 1 February 2023

2. Who am I?
• Anthropology PhD (American University,
Washington DC, USA)
• 10 years as a Sociology lecturer (UWI STA)
• 3 years as a Criminology Lecturer (UoL, UK)
• 8 years as a Qualitative Research Consultant
for IDB, UNDP, UNCCT and JEITT (and others)
• Currently a UN Peace and Development
Officer for the Department of Political and
Peacebuilding Affairs
• 15+ years teaching qualitative research
methods at various levels.
• www.dylankerrigan.com

3. Qualitative Data: Advantages,
Limitations & Solutions
• In-depth, contextualised, nuanced, grounded: ‘Thick description’ (Geertz, 1973).
• Embodied experience and knowledge
• Phenomenology and Inductive reasoning
• Difference between what people say they do, and what they actually say!
• Limited to small group:
• Expandable to type? Typicality, localised.
• ‘Lack of Objectivity’?
• Reflexivity renders the research process more transparent to researcher and reader
• Distance & objectivity 3

4. Analysis…what do we mean by this?
The forgotten element of QM
4
Often “grounded theory” (Glaser and Strauss).
Analysis is not objective – constructed activity
• Becker, How to think about your research while doing it.
• Continual development of a theory continually tested and developed
around a hypothesis
More commonly “analytic induction”

5. What is grounded theory?
• construction of theory through the analysis of data (induction)….its a set of rigorous
principles to develop a new theory
• As researchers review the data collected, repeated ideas, concepts or elements become
apparent, and are tagged with codes, which have been extracted from the data.
• As more data are collected, and as data are re-reviewed, codes can be grouped into
concepts, and then into categories. These categories may become the basis for new
theory
• Sorting is not about data sorting but theory sorting….big misconception
• http://www.groundedtheory.com/what-is-gt.aspx
• Classic and Glaserian versions….
5

6. What are Analysis in Qualitative Data:
• “It is not a technical exercise as in quantitative methods, but more of a dynamic, intuitive
and creative process of inductive reasoning, thinking and theorising” (Wong, 2008, n.p).
6

7. What is Analysis in Qualitative Data:
• “Qualitative data is often subjective, rich, and consists of in-depth information normally
presented in the form of words. Analysing qualitative data entails reading a large amount
of transcripts looking for similarities or differences, and subsequently finding themes and
developing categories” (Wong, 2008, n.p).
7

8. What are Analysed in Qualitative Data:
• So, it is unstructured text-based data.
• “These textual data could be interview transcripts, observation notes, diary entries, or
medical and nursing records. In some cases, qualitative data can also include pictorial
display, audio or video clips (e.g. audio and visual recordings of patients, radiology film,
and surgery videos), or other multimedia materials”(Wong, 2008, n.p.).
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9. How we
decide what
analysis to
choose ?
• We need to think about the context of our
research:
• Who is conducting data collection
• Where the data collection takes place
• Who is part of our research
• Who is responsible for the data
9

10. Qualitative Analysis and the common denominator =
Coding.
• Descriptive or Interpretive -> Wide or Narrow.
• Division of Codes: Setting/Context; definition of the situation; perspectives; ways of
thinking about people/objects; process; activities; events; strategies; relationships; social
structure; methods.
• Pattern Coding: Start off with lots of themes and ideas; some look similar or link together.
• Themes (Thematic Networks).
• Causes and Explanations.
• Relationships.
• Emerging Theories.
10

11. ‘qualitative coding is not the
same as quantitative
coding…quantitative coding
requires preconceived,
logically deduced codes into
which the data are placed.
Qualitative coding, in
contrast, means creating
categories and standardized
procedures, qualitative
coding has its own
distinctive structure, logic
and purpose’. Kelle, 1997:7
• The importance of coding in QM is
the iterative, organic nature of the
codes as they develop from the data.
11

12. Qualitative Analysis: main
approaches
• 1) Content Analysis –
• words and/or pictures. Assigns media content to categories (i.e. sex, violence) and
analyses the relationships between these categories.
• Burman and Parker (1999) call this ‘interpretative repertoires’ that demonstrate
phenomena, shared meanings, experiences by applying a basic method of extracting
themes and patterns from textual information. This is a quantitative method applied
to text.
12

13. Qualitative Analysis: main approaches
2)Narrative analysis
Accounts of life stories and experiences where individuals describe what happened to the. Can also use
be applied to public events. Ken Plummer’s coming out stories where people describe their sexuality is a
useful explanation of how this method works in practice.
3)Discourse analysis
Stemming from the analysis of language this also has a Foucauldian background. It is a deconstructive
reading of the meanings in a text (although discourses can be anything). Emphasis is on language and
power differentials and the hidden meanings in language. deconstructs ideas and belief systems (i.e.
linked to power, social interests).
4) Semiotic Analysis
interpretation and media texts, which are seen as sign systems to be decoded to elicit latent meanings.
13

14. Coding
• The researcher breaks the data down into small units of meaning
by labeling words, phrases, paragraphs in order to further organize
similar codes into a larger category.
14

15. What is a code?
15
A code in qualitative inquiry is most often a word or short phrase that symbolically assigns a
summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or
visual data.
The data can consist of interview transcripts, participant observation field notes, journals,
documents, literature, artifacts, photographs, video, websites, e-mail correspondence, and so on.
Just as a title represents and captures a book or film or poem’s primary content and essence, so
does a code represent and capture a datum’s primary content and essence.

16. Coding Example
1 “I notice that the grand majority of homes have chain link
fences in front of them. There are many dogs (mostly
German shepherds) with signs on fences that say “Beware
of the Dog.””
1 = SECURITY
16

17. Coding Example
1 He cares about me. He has never told me but he does. 2
He’s always been there for me, even when my parents were
not. He’s one of the few things that I hold as a constant in
my life. So it’s nice. 3 I really feel comfortable around him.
1 = SENSE OF SELF-WORTH
2 = STABILITY
3 = “COMFORTABLE”
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18. Coding
• Did you agree with the codes? Did other words or
phrases run through your mind as you read the
data? It’s all right if your choices differed from mine.
Coding is not a precise science; it’s primarily an
interpretive act. Also be aware that a code can
sometimes summarize or condense data, not simply
reduce it. The introductory examples above were
kept purposely simple and direct. But depending on
the researcher’s academic discipline, ontological
and epistemological orientations, theoretical and
conceptual frameworks, and even the choice of
coding method itself, some codes can attribute
more evocative meanings to data.
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19. Example of Coding
1 My son, Barry, went through a really 1 MIDDLE-SCHOOL HELL
tough time about, probably started the end
of fifth grade and went into sixth grade.
2 When he was growing up young in 2 TEACHER’S PET
school he was a people-pleaser and
his teachers loved him to death.
3 Two boys in particular that he chose to 3 BAD INFLUENCES
try to emulate, wouldn’t, were not very
good for him. 4 They were very critical of 4 TEEN ANGST
him, they put him down all the time,
and he kind of just took that and really
kind of internalized it, I think, for a
long time. 5 In that time period, in the 5 THE LOST BOY
fifth grade, early sixth grade, they really
just kind of shunned him all together, and
so his network as he knew it was gone.
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20. Decoding and Encoding
20
In the excerpt we just saw, a mother describes her teenage son’s troubled school years. The
codes emerge from the perspective of middle- and junior high school years as a difficult
period for most youth. They are not specific types of codes; they are “first impression”
phrases derived from an open ended process called Initial Coding.
Note that when we reflect on a passage of data to decipher its core meaning, we are decoding
when we determine its appropriate code and label it, we are encoding. For ease of reference
coding is the sole term used in coding. Simply understand that coding is the transitional
process between data collection and more extensive data analysis.

21. Example of Open Coding
• Lexie: Yes like in that class she‘ll ask us to come up and write the answer on the
overhead like when I go up there I like get red for some reason cause I don’t want to
like stand in front of the class. So I just went up there and like I started to write the
answer, but when I do that I like get red, like I don’t want to be up there like like I‘ll go:
Why do I have to go up there? So like I have to go up there anyways cause she called on
me and when I go up there I get like I’ll ask a friend when I go back to my seat: Was my
face red? Yeah. Cause I was nervous, an like when I get nervous I‘ll like start to like how
the mothers put the babies to sleep they‘ll shake their legs that there and I do that and
like my arms start to shake when I get nervous.
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22. How codes can be clustered into categories
22
Categories/theme Sub themes Codes
Embarrassed at asking questions Nervous (embarrassed / playing
with her fingers
Reluctance
Fear (I get it wrong)
Fidgeting
Sensitive
Hate asking questions
Looks down
Embarrassed to ask
Butterflies in my stomach
I don’t know them
Made me feel bad
Getting Upset Frustrated (I want to break my
pencil
Uncomfortable (Don’t like it /
They will look at me)
Insecure (Think I’m dumb)
Don’t like asking teacher
Staring at me
Know I need help
Upset that they see me asking for
help
Get embarrassed and upset
Like what would I say
Like upset and embarrassed
Embarrassed and Dumb I felt dumb I felt embarrassed

23. Thematic Analysis (Attride Sterling)
23
Using web-like illustrations to summarise main themes in the text
Basic themes – small themes evident in the text that are not meaningful when
they stand alone [also known as “open coding”]
Organizing themes – a cluster of basic themes that are more revealing when take
together [also known as “sub themes” or “categories”]
Global themes – macro thinking of overarching themes that tell us what the
whole text mean [also known as ”major theme”]

24. The
differences
between
codes and
themes
• Several qualitative research texts recommend that
you initially “code for themes.” That, to me, is
misleading advice because it muddies the water. A
theme is an outcome of coding, categorization, and
analytic reflection, not something that is, in itself,
coded.
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25. Coding
• Data analysis starts from the minute you begin to conceptualize your project. On some
level you are thinking about the kind of questions you want to ask, the people you want
to talk to, the ways you will collect data, and the ways you will analyze data. While you
conduct the interviews, you are instantly analyzing data to ask follow-up questions.
When you are transcribing you are listening to the data and making cognitive connection
between the data and your research questions. Finally, when you are chunking data into
codes and assigning a label, you are also conducting analytical thinking grounded within
the context of the research purpose and questions. Therefore, analysis occurs in all
stages of qualitative research
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26. Codes, Categories, and Themes
26
Codes are tags or labels attached
to words, phrases, paragraphs,
pictures, video clips, documents,
song lyrics, or any other type of
qualitative data to assign a unit
of description, meaning,
inference, relationship, etc.
Think of codes as buckets that
the researcher creates in order to
hold units of data/tell the story.

27. www.le.ac.uk 27
For those who have tried coding and thematic analysis previously, what do you find
most difficult about doing coding and thematic analyses?

28. First Step – What is the Coding Framework?
• The project involves coding a chapter from the Judicial Education Institute of
Trinidad and Tobago on user experiences of the court system.
• The report revolves around the Criminal Justice System in T&T and the
experiences faced by a variety of individuals who use it and the personal feelings
they describe about the local system for justice. [this is where your literature
review becomes important]
• A coding framework will be developed based on this context
• Throughout the report various codes will be developed based on the context of
the report.
• Following coding, various subthemes will be developed and produced from the
codes. This can then be discussed in a broader discussion chapter.
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29. Next steps
• Read the chapter a number of times and makes initial impression notes in the
margins
• Get your head in gear for targeted and focussed concentration.
• Find somewhere quiet to do coding (if at all possible)
• Also, remember the difference between deductive coding and inductive coding.
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30. www.le.ac.uk
Let’s do some initial coding….
Chapter 6.
“It’s almost a year, and we have not really reached anywhere.” The
frustration in her voice was clad in a slow paced, almost collected tone,
which revealed just how over it she really was. There was very little
that felt like family to her even when at the Family Court. The tired
mother of four found solace in another court user as they both sat in
the waiting area. It seemed as though all she needed was someone to
listen as no one would listen to her; not even by way of her attorney,
“To me this senior attorney is always talking… To me he has been given
an opportunity to talk… But to say my attorney getting an opportunity
to voice and really stand up for me and thing, I don’t think it was fair.
The only thing I could think is the other attorney being favoured
because she is senior .”

31. www.le.ac.uk 31
Let’s do some more initial coding….
The man with whom she’d been talking shook his head and
poised himself for more distressing details. He asked her
why everything was taking so long. She paused, took a huge
gulp of air and with paced anger said, “Why it is every time
the Judge, it seems as though she keep putting the matter
off. To me, she keep asking for different applications and
stuff and all these things cost money. And we doing
applications and stuff and to me I not seeing any results
coming out of it. She’ll postpone and we’ll go back to court
and when we go back to court I’m thinking today I’m going
to see some results but nothing.”

32. www.le.ac.uk 32
Let’s do some more initial coding….
She opened her bag and looked into her purse to count how much change she
had left, making sure it was enough for whatever the day had in store for her.
She snapped it close and turned to the man and with a soft tone so no one
else would hear her, “Like this last time, I’m thinking I mean I’m not working.
I’m thinking like to go to trial that will be a lot more legal fees to come. So I’m
thinking now I’m at a point where I need to get a legal aid or something like
that. I don’t know if it’s really possible to represent myself or what. Even
though I think I can do it better than my attorney. My case, is just being
truthful.” Her frustration became so much, and the cost of everything too
burdensome, that the idea of self-representation was strong in her mind. But
even her mind was slowly becoming incapable of the resilience that she
needed.

33. The Inductive Process behind emergent
coding
• You begin with multiple sources of data
• You look at each piece of the data individually
• You ask an analytical question to code the data such as “What is going on here?” or “What are the
critical events?” or “How does the participant react in different spaces?” These questions are based
on your research questions
• You chunk the data in their smaller analytical units
• You label those analytical units as codes
• You then gather the codes in some analytical way that responds to your research questions
• Label the gathering of like codes as categories
• Write around the categories to discover themes
• Themes should hold true for data within and across categories as they are generalizable statements
of the participants’ experiences
• Put together the narrative of the participants’ experiences using a deductive form of representation
• Data analysis is inductive, but data representation is deductive as in you describe the story around
your theme using examples from your data

34. www.le.ac.uk 34
Now lets group some of our codes into subthemes…

35. Now lets group some of our subthemes into themes
35

36. Misconceptions
about thick
descriptions
Some things to avoid when writing up thick
descriptions:
Thick description is not about verbal diarrhea.
Thick description should be a combination of
personal interpretation grounded within the
cultural context of the interpretation.
Avoid making the descriptions muddy.

37. More on Thick Descriptions
• Examples from ‘REFLECTIONS OF AN INTERESTED OBSERVER: Ethnographic Musings
on the Court User’s Experience in T&T’
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38. What is Textual Analysis?
38
How do we turn text into data for analysis?
Why are there so many different types of qualitative analysis?
Which one should I use and why?

39. Content Analysis
• A detailed and systematic examination of a particular body of material for the
purpose of identifying patterns, themes or biases’ (Leedy and Ormrod, 2013: 148).
• Quantitative and/or Qualitative
• Manifest and/or Latent Content Analysis
• Useful link:
• https://www.bing.com/videos/search?q=how+to+do+content+analysis&view=detai
l&mid=0A1EEE0B0FAF92648AB80A1EEE0B0FAF92648AB8&FORM=VIRE
39

40. Content Analysis – is this for your study?
Advantages of content analysis
1. transparent research methods uses an
objective coding scheme that can be
replicated
2. can be used to track trends and
patterns over time – especially in
media analysis
3. the researcher is excluded from the
analysis process, unobtrusive methods
and non-reactive method.
4. Flexible methods that can applied to all
kinds of documents
Disadvantages of content analysis
1. CA is as good as the schedule and
manual produced
2. These coding manuals inevitably
involve interpretation
3. Difficult to answer why questions
through CA. results in speculation
4. Accusations that CA is too concerned
with measurement and not theory.
Need to remember to relate findings to
theoretical ideas.
40

41. www.le.ac.uk
- This type of content analysis is about
searching out underlying themes in the
materials analysed.
- The processes through which the themes
are extracted is often implicit. The
extracted themes are usually displayed in
quotations.
- Search for themes in the central text.
Then apply the themes to all the text once
some familiar ones have been
established.
- Categories of interest and original
questions guide the study but there is a)
constant discovering and new themes
emerging and b) constant comparison of
relevant situations and meanings. There
is therefore a movement back and forth
between conceptualization, data
collection and analysis and interpretation.
Ethnographic content
analysis

42. Content Analysis: Method.
42
Set research question
(subject to be
investigated).
Identify specific body of
material to be studied.
Sample: Depends on size
-> Representative.
Define the
characteristics or
qualities to be examined
and counted (labels and
themes).
Researcher examines
the material for the
characteristics decided
in step 4 (coding).
Analyse and interpret
results.
Write up your findings
(research and theory).

43. Narrative Analysis
43
NA relates to analysing story forms. A researcher has a story of some kind
(life story /account of an experience pf public events etc.). the story has a
beginning, middle and end.
Stories connected to relations of power. They highlight the significance of
story telling in everyday interaction and as central method of how we
make sense of each other and the world.
Analysts of the narrative structure of stories has developed through a set
of concepts that help to decipher the elements of the story. Need to look
at how the story unfolds and events take place.

44. Constructing Stories
44
When you’re choosing a narrative
inquiry for your qualitative
research, you’re interested in
constructing stories from the
participants’ sharing of incidences,
subjectivities, meaning making,
and other data sources.
During interviews, you would want
to offer the participant as much
time and space needed to fully
narrate their stories without
inserting too much of yourself.

45. Strategies for analysing stories:
45
The Plot: the plot to a story
emerges from the unusual
twists in the narrative that
draws attention to certain
events.
The Story: temporally
sequenced narrative that is
being told. Conventional
lifestory narrative – ‘I was
born…I grew up’…
The Events: in the story
that move from one
transition to another.
The text and subtext: what
is present and what story is
absent
Kernels: narrative moments
which give rise to changes
in the direction of the
events. What structures
the story.
Satellites: relatively minor
events which highlight or
surround the kernels adds
detail to them or fleshes
them out.
The actors; agents that
perform the actions.

46. Why use narrative analysis?
• NA is useful to explore how identities are constructed in stories.
• Analysis of narrative structures (formal properties) of stories. The concern with story
elements, plot, setting and characteristics.
• Analysis of social role of stories the ways they are reproduced, the ways they are read,
how they change, their role in the political process, stories as speech acts
46

47. Discourse
Analysis:
interpretation
‘The study of rhetorical and argumentative
organisation of talk and texts’ (Silverman, 2011: 468).
‘Discourse analysts examine all forms of verbal and
textual materials: spoken and written accounts
letters, scientific journals, newspaper reports…the
object is to describe the way that such discourse is
constructed and to explore the functions served by
specific constructions at both the interpersonal and
societal level’ (Gilbert, 2008: 445).
47

48. The importance of language
48
To use DA you must be interested in systems of meanings and uncovering the power relations used in different
discourses.
A discourse can be found in almost anything including all types of social and political practices and institutions.
DA is a generic term for all research concerned with language and its cognitive context – essentially the
linguistic units.
DA is ‘analysis of language beyond the sentence’ – DA looks beyond the story forms which are the basis of NA.
Crucial differences is that when studying discourse the primary concern is with manifestations of power with
the discourses of social practices.

49. Why would we use DA?
• Constructs social relationships among participants
• Creates verbal presentations of events, activities and relationships.
• Constructs relations of parts to whole of the text and between text and context. We
need to analyse the data at these three levels.
• What is key is that DA must analyse language and the context of the relationships, just
analysing the language is content analysis.
49

50. What is the coding framework?
– (1) unpacks evidence of ‘language in use,’ and how
it disguises structural privilege and inequalities; (2)
‘discloses the related D/discourses’ used to reinforce
and construct such meaning; and (3) ‘retrieves the
political work,’ or rather the social goods – power,
status, valued knowledge – being thought about,
argued over and distributed in society, “as
instantiated within text-making.”
Discourse Analysis Example
What is a “stanza”?
“a language
unit that deals
with a unitary
topic or
perspective
within an
ongoing
discourse”
(Gaudio &
Bialostok
2005:56).
50

51. Stanza 1
1.B001 I think a great deal of what we see in life,
2.B002 our perceptions, are based on
3.B003 what we expect to see,
4.B004 and I'm a big believer in that,
5.B005 not to get, negate racism,
6.B006 but you get back
7.B007 from the world what you put out
Stanza 2
8.B008 When I go outside
9.B009 and I feel lousy about myself,
10.B010 I get a different reaction
11.B011 from everyone that I meet
12.B012 than I do
13.B013 when I feel great about myself.
Stanza 3
14.B014 So it really,
15.B015 there is some validity to the,
16.B016 in my mind,
17.B017 to you do get out,
18.B018 get back from the world

52. B020 Life doesn't turn out
B021 the way anybody wants it.
B022 In a nutshell,
B023 whoever you are, whatever skin color
B024 you have,
B025 whatever your predicament in life is,
B026 you have two options.
B027 One is to look at life
B028 with the brightest and most positive attitude
B029 and empower yourself,
B030 or be angry
B031 and negative
B032 and look for things
B033 so that you can excuse your,
B034 blame your problems
B035 on something else.
B036 And that is disempowering
B037 and stagnating.
B038 So in a nutshell,
B039 again there is racism,
B040 but a lot has to do with
B041 how you
B042 approach life.

53. Type Focus of Interest
Sociolinguistics
Emphasises linguistic structures, including the
relationship between linguistics and social
categories (i.e. class, gender).
Narrative Discourse
Analysis
Relationship between narrative and real events
(i.e. personal narratives, life histories). Textual
devices at work in the construction of processes.
Critical Discourse
Analysis
Examines how reality is constructed in/through
discourse (verbal and written).
Ideological significance.
Foucauldian Discourse
Analysis
Discursive resources, construction of subjects
within various forms of knowledge, institutions
and power.
Conversational Analysis
Structure of talk-in-interaction; Machinery in
operation within talk (i.e. inferences, turn-taking
and interactional trouble spots).

54. Difference Between Discourse and Content Analysis.
• ‘Traditional approaches to the role of the media…were largely content analytical;
quantitative studies of stereotypical words or images…Discourse analytical
approaches, systematically describe the various structures and strategies of text or
talk, and relate these to the social or political context’
• (Van Dijk, 2000 cited in Davies, Francis and Jupp, 2011: 248).
54

55. Writing the self into analysis: the importance of
reflection
• How the individual affects the process of analysis.
• Influences from your own terms of reference, context, history, identity.
• Importance of reflexivity during the process of analysis
• Team analysis - checks and balances for bias – strength of coding frames etc.
• Rigour in writing – we are assessed in terms of our written contributions on rigour in
methods – this specifically includes writing clearly about analysis in methods.
• Reliability and Multivocality
55

56. Tips for analysis in thesis writing / oral
examination
• Make extra effort to read and write about how you analysed your data.
• This will be a ‘biggy’ for any examiner who has taught or conducted qualitative research
• Include your coding frames in the method chapter or Appendix
• Refer to your analysis process in your discussion of how you came about your findings.
• Key journals to keep up with :
• 1) International Journal of Social Research Methodology http://www.tandfonline.com/loi/tsrm20
• 2) Qualitative Inquiry http://journals.sagepub.com/home/qix
• TOC (table of contents) – alerts
56

57. Tips for you: 7 Steps to manage your database
57
1) Keep copies of important information. A data management system
should also be backed up and backups updated as data preparation and
analysis proceeds.
2) Arrange field notes or researcher commentary in a chronological,
genre, cast-of-characters, event or activity, topical or quantitative data file
schema.
3) Create a system for labelling and storing interviews. This includes a
unique name or case identifier for each file that communicates crucial
information about the file to researchers.

58. Tips for you: 7
Steps to manage
your database
58
4) Catalogue all
documents and artefacts.
5) Provide for the safe
storage of all materials.
6) Check for missing data.
7) Develop a process for
reading and reviewing text

59. Tips for you:
Your
Qualitative
Analysis
59
Stick to one model, or type of data analysis
The data analysis shall reflect the context of your
research
Support your models and data analysis with appropriate
sources
Ask for help when you feel being stuck or overwhelmed
BUT before that – Have a think about your
topic/evidence/what questions you wish to answer!
AND Ethical issues!

60. Say Hello to NVivo
“Intelligence” Text Search Word Tree in NVivo

61. What is Nvivo?
• NVivo is a software tool that complements the work of human researchers working on
qualitative and mixed methods (and multi-methodology) research. As such, it enables
the ingestion of various types of digital data, the coding of that data, and then various
types of queries and analyses of that coding and data. The tool may be used for one part
or a few discrete parts of a research project, an entire research project, or even multiple
research projects (such as with the same dataset).

62. What is
difference
between paper
and pen coding
and computer
assisted coding?
• Some researchers suggest the findings
arrived at through manual coding using
paper and pens will differ from manual
coding through a computer.
• Is this true or not?

63. Getting Started
• While NVivo is considered state-of-the-art in its type and class, it also has a reputation
for complexity and challenge. The learning curve for this tool is non-trivial; however, it is
not deserving of the reputation of insurmountable difficulty. It helps to think about the
software tool based on its essential functionalities:
• (multimedia) data and metadata management
• data curation
• data cleaning
• coding and annotation of data
• data queries, and
• native data visualizations.

64. NVivo key terms
• Sources are your research materials including documents, PDFs, datasets, pictures, audio, video
and memos.
• Source classifications let you record information about your sources—for example,
bibliographical data.
• Coding is the process of gathering material by topic, theme or case. For example, selecting a
paragraph about water quality and coding it at the node ‘water quality’.
• Nodes are containers for your coding that can represent themes, topics or other concepts—they
let you gather related material in one place so that you can look for emerging patterns and ideas.
• Cases are containers for your coding that represent your ‘units of observation’—for example,
people, places, organizations or artifacts.
• Case classifications allow to you record information about people, places or other cases—for
example, demographic data about people.

65. Autocoding
Autocoding is when NVIVO emulates a human coding pattern. If a researcher wants, he or she may
code a percentage of the contents (at least 10%) and then enable machine-coding that emulates
the human-coding.
He or she may then go back and see what information was coded by “NV” and decide whether to
accept that coding or not. In other words, there is human oversight over every aspect of the
work…even that achieved by the computer. (In such cases, the human has to have created the full
codebook already and offered sufficient coded examples for each of the nodes.)

66. Linking to Memos
• Researchers may right click on a node and link that node to a memo to add more
information (beyond Node Properties). A “memo” is a document that is created
within NVivo. Memo-ing (what some call "jotting") enables on-the-fly commenting as
well as the collection of cumulative memos for deeper understandings. On a team,
memos may be used to surface insights about the research and may be used to hone
the research process.
• Memos are exportable, and they are importable

67. Coding in NVIVO
In NVivo, researchers code the concepts to “nodes.”
These are indicated by round blue-filled circles. In the
sources folders, researchers access the various files
(documents, images, audio, video, data sets, and
other file types), and they highlight texts that they
find relevant by linking them to particular categorical
or other types of nodes. Sub-nodes may be used for
"subcoding" of second-order codes. NVivo enables
the visualizing of nodes in a variety of ways, including
a tree structure with nested nodes.
The coding function is the main (non-automated)
affordance of NVivo for researchers. Some
researchers use this tool only for manual coding, and
these may never use the machine-analysis aspects of
NVivo. There are many appropriate ways to use NVivo
based on context and researcher preferences

68. Types of Code

69. Types of Code

70. Coding in NVIVO

71. Coding in NVIVO
• Coding then involves the act of researcher sense-making for data reduction and simplification.
• Coding nodes (categories) enable the collation of related data as concepts or types of
phenomena.
• Within NVivo, these nodes may be placed in matrices for matrix coding queries.
• Nodes may also be used for text queries (like word or phrase frequency counts, text searches,
and others). Nodes may also be used to create node structures and spatial visualizations,
which show hierarchical or associational or other relationships between nodes (as phenomena
or as concepts).
• Finally, nodes may be extracted as “codebooks.” Researchers are able to see what codes they
used and how these codes inter-relate.

72. THE END

73. References
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application. Macmillan International Higher Education.
• Gaudio, R.P., 2003. Coffeetalk: Starbucks™ and the commercialization of casual
conversation. Language in Society, 32(5), pp.659-691.
• Flick, U. ed., 2013. The SAGE handbook of qualitative data analysis. Sage.
• Hart, C., 2008. Critical discourse analysis and metaphor: Toward a theoretical framework.
Critical discourse studies, 5(2), pp.91-106.
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74. References
• Fairclough, N. (2004). Critical discourse analysis in researching language in the new capitalism:
Overdetermination, transdisciplinarity, and textual analysis. In L. Young and C. Harrison (eds.),
Systemic Functional Linguistics and Critical Discourse Analysis: Studies in Social Change. London:
Continuum. pp. 103-122.
• Gaudio, R.P. (2003). Coffeetalk: Starbucks and the commercialisation of casual conversation.
Language in Society 32: 659-691.
• Gee, J.P. (2005). An Introduction to Discourse Analysis: Theory and Method. London: Routledge.
• Linde, C. (1987). Explanatory systems in oral life stories. In D. Holland and N. Quinn (eds.), Cultural
Models of Language and Thought. Cambridge: Cambridge University Press. pp. 343-366.
• Tracy, S.J., 2019. Qualitative research methods: Collecting evidence, crafting analysis,
communicating impact. John Wiley & Sons.
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