These slides are from a recent workshop for Honours students and researchers at UTS's School of Communication. Not pictured are the examples from my own research that I used to illustrate concepts. Hopefully I will be able to make a prettier version soon.
Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research
Qualitative Data Analysis I: Text Analysis - a summary based on Chapter 17 of H. Russell Bernard’s Research Methods in Anthropology: Qualitative and Quantitative Approaches for a Report for Anthro 297: Seminar in Research Design and Methods under Dr. Francisco Datar, Department of Anthropology, College of Social Sciences and Philosophy, University of the Philippines Diliman
Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research Qualitative research
Qualitative Data Analysis I: Text Analysis - a summary based on Chapter 17 of H. Russell Bernard’s Research Methods in Anthropology: Qualitative and Quantitative Approaches for a Report for Anthro 297: Seminar in Research Design and Methods under Dr. Francisco Datar, Department of Anthropology, College of Social Sciences and Philosophy, University of the Philippines Diliman
CONTENT ANALYSIS (Quantitative Research Methods)Libcorpio
Content Analysis, Quantitative Research Methods, LIS Education, Library and Information Science, LIS Studies, Information Management, Education and Learning, Library science, Information science, Library Research Methods,
Grounded Theory: A specific methodology developed by Glaser and Strauss (1967) for the purpose of building theory from data. In their book the term grounded theory is used in a more sense to denote theoretical constructs derived form qualitative analysis of data.
Braun, Clarke & Hayfield Thematic Analysis Part 4Victoria Clarke
The forth and final part of a four part lecture providing an introduction to thematic analysis and particularly the reflexive approach outlined by Braun & Clarke.
Scientific research deals with verifiable methods of collecting and analysing information regarding two or more variables (phenomena) with the primary aim of determining how they relate. In the Social Sciences, there are several ways of gathering these information. Depending on the problem under investigation and research design, data could be collected through various sources: primary and secondary using varied approaches and methods.
This study attempts an in-depth explication of the various known techniques and methods of data collection especially at its primary source (in keeping with the tenets of survey and descriptive researches). In specific terms, the study discussed in extenso the use of observation, interview and in practical terms questionnaires as instruments for data collection
Braun, Clarke & Hayfield Thematic Analysis Part 3Victoria Clarke
The third part of a four part lecture providing an introduction to thematic analysis and specifically the reflexive approach developed by Braun & Clarke.
A comprehensive presentation based on a qualitative research methodology 'Grounded Theory, presented at Government College University Lahore, Pakistan.
CONTENT ANALYSIS (Quantitative Research Methods)Libcorpio
Content Analysis, Quantitative Research Methods, LIS Education, Library and Information Science, LIS Studies, Information Management, Education and Learning, Library science, Information science, Library Research Methods,
Grounded Theory: A specific methodology developed by Glaser and Strauss (1967) for the purpose of building theory from data. In their book the term grounded theory is used in a more sense to denote theoretical constructs derived form qualitative analysis of data.
Braun, Clarke & Hayfield Thematic Analysis Part 4Victoria Clarke
The forth and final part of a four part lecture providing an introduction to thematic analysis and particularly the reflexive approach outlined by Braun & Clarke.
Scientific research deals with verifiable methods of collecting and analysing information regarding two or more variables (phenomena) with the primary aim of determining how they relate. In the Social Sciences, there are several ways of gathering these information. Depending on the problem under investigation and research design, data could be collected through various sources: primary and secondary using varied approaches and methods.
This study attempts an in-depth explication of the various known techniques and methods of data collection especially at its primary source (in keeping with the tenets of survey and descriptive researches). In specific terms, the study discussed in extenso the use of observation, interview and in practical terms questionnaires as instruments for data collection
Braun, Clarke & Hayfield Thematic Analysis Part 3Victoria Clarke
The third part of a four part lecture providing an introduction to thematic analysis and specifically the reflexive approach developed by Braun & Clarke.
A comprehensive presentation based on a qualitative research methodology 'Grounded Theory, presented at Government College University Lahore, Pakistan.
Wikimania presentation on the "Understanding Sources" projectHeather Ford
My presentation about the Ushahidi "Understanding Sources" project covering how Wikipedians verified information during the first days of the 2011 Egyptian revolution article.
Wikipedia sources: On the books and on the groundHeather Ford
Slidedeck from a presentation at the Wikimedia Foundation on the 9th of April, 2012 about how Wikipedians managed and verified sources and citations in the rapidly evolving 2011 Egyptian Revolution Wikipedia article for the Ushahidi 'Understanding Sources' project. Support for this work was provided by Hivos and the OSI.
This set of slides by the Alexandria Archive Institute charts the current development of an open cultural heritage platform for the sharing of data between cultural institutions on the web.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
How to do qualitative analysis: In theory and practice
1. How to do qualitative
analysis
In theory and practice
Dr Heather Ford
@hfordsa
1
2. Steps that I take early in the
research process
1. Decide which types of coding are relevant
2. Start coding!
3. Create a start list of codes
4. Generate categories (pattern codes)
5. Test these categories against new data (start with
contrasting data early on!)
6. Write about categories/pattern codes in a memo to
explain their significance
2
4. What is a code?
‘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’
(Saldaña, 2013)
4
5. What is a code?
‘(Q)ualitative codes are essence-capturing and essential
elements of the research story, that, when clustered
together according to similarity and regularity (a
pattern), they actively facilitate the development of
categories and thus analysis of their connections.’
(Saldaña, 2013: 8)
5
6. What is a code?
‘A word or a phrase does not “contain” its meaning as a
bucket “contains” water, but has the meaning it does by
being a choice made about its significance in a given
context.’
(Blissa, Mark, and Ogborn, 1983)
6
7. What is a code?
7
coding is not just labeling, it is
linking (Saldaña, 2013: 8) - from
the data to the idea and back to
other data
8. A few coding types
1. descriptive coding: summarizes the primary topic of
the excerpt
2. process coding: a word or phrase that captures
action
3. in vivo coding: using the participants’ own language
4. pattern coding: coding for patterns in the data
5. simultaneous coding: applying multiple codes to the
same text
8
10. What gets coded?
Social life happens at four coordinates,“the intersection
of one or more actors (participants) engaging in one or
more activities (behaviors) at a particular time in a
specific place”
Lofland, Snow, Anderson, & Lofland (2006) in Saldaña
10
11. Units of social organisation
1. cultural practices (daily routines, occupational tasks);
2. episodes (unanticipated or irregular activities such as divorce,
championship games, natural disasters);
3. encounters (a temporary interaction between two or more individuals
such as sales transactions, panhandling);
4. roles (student, mother, customer) & social types (bully, geek); 5. social
& personal relationships (husband & wife, party-goers); 6. groups &
cliques (gangs, congregations, families, jocks)
7. organizations (schools, fast-food restaurants, prisons);
8. settlements and habitats (villages, neighborhoods, etc.); 9.
subcultures and lifestyles (the homeless, skinheads)
Lofland, Snow, Anderson, & Lofland (2006) in Saldaña
11
12. In combination with
1. cognitive aspects or meanings (e.g., ideologies, rules,
self- concepts, identities);
2. emotional aspects or feelings (e.g., sympathy in
health care, road rage,workplace satisfaction);
3. hierarchical aspects or inequalities (e.g., racial
inequality, battered women, high school cliques)
Lofland, Snow, Anderson, & Lofland (2006) in Saldaña
12
13. Questions to ask yourself as
you code
• What are people doing? What are they trying to accomplish?
• How, exactly, do they do this?
• What specific means &/or strategies do they use?
• How do members talk about, characterize, and understand what is going
on?
• What assumptions are they making?
• What do I see going on here?
• What did I learn from these notes?
• Why did I include them?
(Auerbach and Silverstein, 2003: 44)
• What surprised me? (to track your assumptions)
• What intrigued me? (to track your positionality)
• What disturbed me? (to track the tensions within your value, attitude and
beliefs (Sunstein and Chiseri-Strater, 2007: 106) 13
14. How many codes?
it depends...
pro line by line: Charmaz (2008) says it reduces chance
of bias
Stern (2007) looks for the ‘cream on the top’ of the data
Friese (2012) recommends 120-300 codes total; others
like Litchtman (2010) suggests 20-100; Crewell (2013)
starts with 5-6 provisional codes
14
15. Advice from Saldaña
• Be organized
• Exercise perseverance
• Learn to deal with ambiguity
• Exercise flexibility
• Be creative
• Be ‘rigorously ethical’
• Develop an extensive vocabulary
15
16. Next steps
e.g. decide what types of codes to
use, print out my data and start
figuring out how to code it
18. When you get new data
through interviews, field visits,
participant observation etc
write up your observations in
memos, using contact sheets
and/or interim case analysis forms
18
19. What is a memo?
‘(A memo is) the theorizing write-up of ideas about
codes and their relationships as they strike the analyst
while coding... it can be a sentence, a paragraph or a
few pages... it exhausts the analyst’s momentary
ideation based on data with perhaps a little conceptual
elaboration’ (Glaser, 1978: 83)
19
20. What can memos do?
• pulling together incidents that appear to have
commonalities what is intensely puzzling or
surprising about a case
• alternative hypotheses in response to someone
else’s memo (or analysis)
• proposals for a specific new pattern code
• integrating a set of marginal or reflective remarks
already made on field notes
• when the analyst does not have a clear concept in
mind but is struggling to clarify one
• around a general theme or metaphor that pulls
together discrete observations
Miles and Huberman, 1994: 73)
20
21. Contact sheet
Should take less than an hour to fill out
Contains: date of contact, key concepts, linked to
specific places in field notes
Essential for revising your initial framework
(Miles and Huberman, 1994: 51)
21
22. Answers questions like:
What people, events, or situations were involved?
(using numbers or other identifiers to anonymize if
necessary)
What were the main themes or issues in the contact?
Which research questions and which variables in the
initial framework did the contact bear on most centrally?
What new hypotheses, speculations, or hunches about
the field situations were suggested by the contact?
Where should the field-worker place most energy during
the next contact, and what kinds of information should
be sought?
(Miles and Huberman, 1994: 51)
Contact sheet
23. Interim case analysis forms
• Main themes, impressions, summary statements
• about what is going on in the site
• Explanations, speculations, hypotheses about what
is going on in the site
• Alternative explanations, minority reports,
disagreements about what is going on in the site
• Next steps for data collection
• Implications for revision, updating of coding scheme
Based on Miles and Huberman, 1994: 78
23
24. Research outline
Auerbach & Silverstein (2003, p.44) recommend that
you keep a copy of your research concern, theoretical
framework, central research question, goals of the
study, and other major issues on one page in front of
you to keep you focused and allay your anxieties
because the page focuses your coding decisions.
25. Next steps
e.g. create a research outline and
print it for my desk, write a
template for a contact sheet for
each interview
27. 27
"The majority of projects arrive at a good conclusion by steady steps
through analysis processes rather than a grand moment of discovery.
Arrival will be confirmed by growing confidence that you really know
what’s going on. It happens, in other words, over time, through thinking
and working with the data.”
(Richards, 2009:143)
29. 2nd cycle coding
The process that enables you to move from
multiple codes in the 1st cycle/s of coding to
a few major themes/categories/concepts or
at least one theory/narrative.
Saldaña, 2013: 12
30. 2nd cycle coding
methods
• 1st cycle: In Vivo, process and initial coding
• 2nd cycle coding:
• Focused coding: finding thematic/conceptual
similarity;
• Axial coding: relations between a category’s
properties and dimensions;
• Theoretical coding: discovering the central/core
category that identifies the primary research
theme;
• More in Saldaña, 2013, ch.5
33. The “top 10” list
• extract just 10 quotes or short passages from
your data that strike you as most
vivid/representational of your study;
• print each on a separate page;
• arrange them in various orders: chronologically,
hierarchically, telescopically, episodically,
narratively, from the smallest detail to the
bigger picture etc
34. The study’s “trinity”
The 3 major
codes/categories/themes/concepts that
stand out.
Steps:
1. Write each on a separate piece of paper
and arrange them in a triangle;
2. Which is the apex or dominant item and
why? In what ways does this apex
influence and affect or interrelate with the
other codes etc?
3. Explore other three-way combinations.
35. Codeweaving
Codeweaving is the actual integration of key code words
and phrases into narrative form to see how the puzzle
pieces fit together.
Steps:
1. Codeweave primary codes/categories/themes
into as few sentences as possible;
2. Write several variations to investigate how the
items interrelate, suggest causation, indicate
process or work holistically to create a broader
theme.
3. Search for evidence in your data to prove &
disprove your statements and revise.
36. 36
• Why are metaphors powerful tools in qualitative
analysis?
• Cognitive linguistics tell us that our cognitive
apparatus is fundamentally metaphorical, and central
to the development of thought.
• Lakoff & Johnson (1980) e.g. argue that we perceive
and act in accordance with metaphors. That metaphors
are matters of thought and not of language.
36
Metaphors
37. 37
Metaphors
Metaphors as analytical tools:
According to Miles & Huberman (1994) metaphors can
serve as
a)data-reducing devices;
b)pattern-making devices;
c)decentering devices; and
d)can connect findings to theory.
37
38. 38
Metaphors as a) data-reducing devices:
“They are data-reducing devices, taking several particulars
and making a single generality of them. For instance the
"scapegoat" metaphor pulls together into one package facts
about group norms treatment of deviants, social rituals, and
social rationalizations. This ability is not to be sneezed
at."
(Miles & Huberman, 1994:250-52)
38
Metaphors
39. 39
Metaphors as b) pattern-making devices:
“For example, in the school improvement study, we found at
one site that the remedial learning room was something like
an "oasis" for the pupils [...] (A teacher used the word
spontaneously, and we began to see the pattern.) The metaphor
"oasis" pulls together separate bits of information: The
larger school is harsh (like a desert); not only can students
rest in the remedial room, but they also can get sustenance
(learning); some resources are very abundant there (like
water in an oasis); and so on.”
(Miles and Huberman, 1994:252)
39
Metaphors
40. 40
Metaphors as c) decentering devices:
“..metaphors will not let you simply describe or denote a
phenomenon, you have to move up a notch to a more inferential
or analytical level.”
(Miles and Huberman, 1994:252)
40
Metaphors
41. 41
Metaphors as d) a means of connecting findings to
theory:
“The metaphor is halfway from the empirical facts to the
conceptual significance of those facts; it gets you up and
over the particulars en route to the basic social processes
that give meaning to those particulars. [...] In doing that,
you're shifting from facts to processes, and those processes
are likely to account for the phenomena being studied at the
most inferential level.”
(Miles and Huberman, 1994:252)
41
Metaphors
42. A good reporting checklist
• Clearly explain the steps you followed during the analysis process
(transparency).
• Develop a good plot that will allow you to present an interesting story that
encompasses plausible answers to your research questions.
• Illustrate with verbatim quotes from data, but do not expect data to speak for
themselves.
• Make sure any quotes have been stripped of identifiable elements if
applicable.
• Cover all different perspectives and exceptions: a rich account.
• Acknowledge limitations and convince the reader by discussing other
possible explanations.
• Relate findings back to literature.
46. Elements of a theory
3 main characteristics:
1. predicts and controls action through an if-
then logic;
2. explains how and/or why something
happens by stating its cause(s);
3. provides insights and guidance for
improving social life.
47. • Many theories are provisional therefore
language should be tentative;
• “Theory is in the eye of the beholder”
(p114)
• “A theory is not so much a story as much as
it is a proverb. It is a condensed lesson of
wisdom we formulate from our experiences
that we pass along to other generations.”
(p250)
Elements of a theory
48. Theories as
“categories of categories”
• Look for possible structures...
• Taxonomy: categories of equal importance;
• Hierarchy: from most to least (frequency, importance, impact
etc);
• Overlap: share some features while retaining some unique
properties;
• Sequential order: progresses in a linear way;
• Concurrency: two or more categories operate simultaneously
to influence and affect a third;
• Domino effects: categories cascade forward in multiple
pathways;
• Networks: categories interact and interplay in complex
49. Categories and analytic
memos as sources for
theory
• Memo-writing to complete the sentence: “The theory
constructed from this study is...”
• Give it time to ‘brew and steep’
• Or try a key assertion (Erickson, 1986): a summative and
data-supported statement about the particulars of a
research study rather than a generalizable and
transferrable meanings
• e.g. “Quality high school theatre and speech experiences
can not only significantly influence but even accelerate
adolescent development and provide residual, positive,
lifelong impacts throughout adulthood” (p5)
• i.e. you don’t always have to develop theory