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Qualitative Data Analysis
By:
Associate Professor Dr. Chew Boon Cheong
Faculty of Technology Management & Technopreneurship
Universiti Teknikal Malaysia Melaka (UTeM)
4th
April 2018
Introduction
What is qualitative analysis?
It is the non-statistical explanation, description,
interpretation and observations.
Theorizing, analysis and synthesis are tightly interwoven.
The key activity of qualitative analysis is (a) the observe
and search for patterns, then (b) explanations, description,
interpretation for those patterns, (c) present and conclude.
The writing process itself is significant for structuring
analysis.
Committed to the naturalistic perspective, and to the
interpretative understanding of human experience through
words, gesture expression.
Gesture expression
Source: Quick and Dirty Tips
Source: familybusinessperformance
Source: kenanaonline
The Biggest Challenges of
Qualitative Data Analysis
 HOW TO
-make sense of massive amounts of data,
-reduce the volume of information,
-identify significant patterns and
-present the data
-make conclusion
Analytical Skills
•Standing back form the information given, look at it objectively.
•Examining it in detail from many angles (theory, secondary data, stakeholders)
•Checking closely whether each statement follows logically from what went
before.
•Looking for possible flaws in the reasoning, the evidence, or the way that
conclusions are drawn (theory, secondary data, logical thinking)
•Comparing the same issues from the point of view of other authors (theory,
secondary data, stakeholders)
•Being able to see and explain why different people arrived at different or the
same conclusions
•Checking for hidden assumptions
•Checking for attempts to lure the reader into agreements
•Avoid biasness, prejudgement, judging on the face value.
Source: Brenna, M. 2005.
The Process of
Qualitative Data Analysis
 Think of managing your qualitative
analysis process like cleaning your
closets – the same basic steps apply!
….LIKE CLEANING A
CLOSET
Source: Billups, F. D.
1. Take everything out of the closet
2. Sort everything out – save or toss?
3. Look at what you have left and organize into
sub-groupings (shirts, pants)
4. Organize sub-groups into clusters of similar
things that belong together (t-shirts, working
shirts, long pants, short pants)
5. As you put things back, how would you group
them to maximize functionality?
6. How do the groups make it work together?
(interpretation, presentation)
Source: Billups, F. D.
After you have done your
Data Collection (interview, questionnaire,
observation)
Transcription
Generally qualitative data analysis involves four
essential steps:
1. Data cleaning (answer your research
questions)
2. Data reduction: (answer your research
questions)
3. Data Analysis/Interpretation ‘coding’ on
Theme (theoretical framework, research objectives)
4. Data Display/Presentation Theme, citation
FOUR BASIC STEPS
-As you review your data (primary and secondary
data), you find that some of it is not usable or
relevant to your study…
-Guide by your research question
Step 1: Data Cleaning
Source: Billups, F. D.
Sample
Transcript of Interview Data
 Hello, good morning. Wow is very windy out there.
 Thanks for inviting me today. Yes, a hot coffee would be nice, oh with cookie, that is
lovely.
 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 and 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…
 Hahahahaha, you are really funny
Source: Billups, F. D.
Data Cleaning
 Are some portions of this transcript unusable or
irrelevant? (purple)?
 What is my research question? The keywords I mean.
 What is the theoretical framework? The parameters I
mean.
 Get a sense of the data holistically, read several
times (immersion)
 Classify and categorize repeatedly, allowing for
deeper immersion (to find double meaning)
 Write notes in the margins (memoing,
classification)
 Preliminary classification schemes emerge,
categorize raw data into groupings (research
questions-keywords, theory parameters)
Step II: Data Reduction
Source: Billups, F. D.
Sample
Transcript of Interview Data
 Hello, good morning. Wow is very windy out there.
 Thanks for inviting me today. Yes, a hot coffee would be nice, oh with cookie, that is
lovely.
 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 and 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…
 Hahahahaha, you are really funny
Source: Billups, F. D.
Question
How many category you can build from this
transcript?
What are these categories?
 Code according to the
(a)Category of Data (yes, no)
(b)Keywords of Research Question
(c)Parameters of Theory
 Function of Coding
1. You want to explain
2. You want to make comparison (male/female)
Coding Process
3 ways to do this:
(a)Keywords of your research
objectives/research questions
(b)Parameters of your theory
(c)Themes emerge from primary/secondary
data
Step III:
Data Analysis and Interpretation
 How do you cite? Single/collective (when
to use which?)
 Reporting Verbs google “bcchew reporting
verbs”
 Numbers could be used in qualitative data
analysis (%, ratio)
 Figures, tables, charts, diagrams could be
used in qualitative data analysis
Step IV: Data Presentation
How do you present?
-Theme
-Storyline
-Chronology
-critical incident (like mind-map)
…Step IV: Data Presentation
Take Note
Recording and Managing Qualitative Data
Before data can be analyzed, they must be gathered and
recorded into a form that makes analysis possible.
Data can be recorded in text, audio or videotape,
photographically, and field notes by memory.
Managing qualitative data can be overwhelming at times.
Making detailed lists of participants' information, along with
the dates of their interviews, the date transcription was
completed will help keep your data orderly.
Create a file system early on so that you do not drown in
piles of paper.
There are some qualitative data analysis softwares.
Analyzing Qualitative Data
 There are some generic strategies that are part of almost every approach to data
analysis.
1. immersion in the data
2. doing preliminary and informal analysis (during interview, transcribe the record
early right after the interview)
3. making analytic memos (during interview, transcribe)
4. finding codes or themes (theoretical framework)
5. connecting the codes or themes into categories (theoretical framework, research
objectives)
6. searching for confirming and disconfirming evidence
7. building a conceptual framework that explains the findings
Lets Do A Hands-on Example
Exercise
Figure 1: Framework by Kumar & Srivastava (2013)
Research Objective
1. To investigate the factors contribute to
Malaysian customer loyalty on their telco
(mobile telecommunication company)
One of the questions
(interview, questionnaire)
1. How long you have been the customer
of this telco? –question for screening and
selection.
1. How does the service quality offered by
the telco (of your choice) won the loyalty
of you as its user for so many years?
What are the problem found?
Lets try a new one
What if we have more information
about the Service Quality?
Zeithaml, Parasuraman and Berry
One of the questions
(interview, questionnaire)
1. How long you have been the customer
of this telco? –question for screening and
selection.
1. How does the service quality offered by
the telco (of your choice) won the loyalty
of you as its user for so many years?
Tools and Techniques
 Microsoft Excel to display your data
 Design well your data collection
techniques (structured or semi-structured,
guided by theory, listen during data
collection)
References
 Chew, B.C. 2016. Qualitative Research. In: Borges, W.
G. 2016. Business Research Methods. S.J. Learning.
Ch.15. pp.227-242.
 Billups, F. D. Qualitative Data Analysis.
https://www.google.com/search?
q=qualitative+data+analysis+ppt&oq=qualitative+data+
analysis+ppt&aqs=chrome..69i57j0l5.6374j1j4&sourcei
d=chrome&ie=UTF-8
 Brenna, M. 2005. Qualitative Data Analysis. Research
Methods and Project Management. [online].
 Chapter 13. Qualitative Data Analysis. [online].
www.uky.edu/~kdbrad2/EDP656/Notes/PowerPoint/Ch

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Qualitative Data Analysis

  • 1. Qualitative Data Analysis By: Associate Professor Dr. Chew Boon Cheong Faculty of Technology Management & Technopreneurship Universiti Teknikal Malaysia Melaka (UTeM) 4th April 2018
  • 3. What is qualitative analysis? It is the non-statistical explanation, description, interpretation and observations. Theorizing, analysis and synthesis are tightly interwoven. The key activity of qualitative analysis is (a) the observe and search for patterns, then (b) explanations, description, interpretation for those patterns, (c) present and conclude. The writing process itself is significant for structuring analysis. Committed to the naturalistic perspective, and to the interpretative understanding of human experience through words, gesture expression.
  • 5. Source: Quick and Dirty Tips
  • 8. The Biggest Challenges of Qualitative Data Analysis  HOW TO -make sense of massive amounts of data, -reduce the volume of information, -identify significant patterns and -present the data -make conclusion
  • 9. Analytical Skills •Standing back form the information given, look at it objectively. •Examining it in detail from many angles (theory, secondary data, stakeholders) •Checking closely whether each statement follows logically from what went before. •Looking for possible flaws in the reasoning, the evidence, or the way that conclusions are drawn (theory, secondary data, logical thinking) •Comparing the same issues from the point of view of other authors (theory, secondary data, stakeholders) •Being able to see and explain why different people arrived at different or the same conclusions •Checking for hidden assumptions •Checking for attempts to lure the reader into agreements •Avoid biasness, prejudgement, judging on the face value. Source: Brenna, M. 2005.
  • 10. The Process of Qualitative Data Analysis
  • 11.  Think of managing your qualitative analysis process like cleaning your closets – the same basic steps apply! ….LIKE CLEANING A CLOSET Source: Billups, F. D.
  • 12. 1. Take everything out of the closet 2. Sort everything out – save or toss? 3. Look at what you have left and organize into sub-groupings (shirts, pants) 4. Organize sub-groups into clusters of similar things that belong together (t-shirts, working shirts, long pants, short pants) 5. As you put things back, how would you group them to maximize functionality? 6. How do the groups make it work together? (interpretation, presentation) Source: Billups, F. D.
  • 13. After you have done your Data Collection (interview, questionnaire, observation) Transcription
  • 14. Generally qualitative data analysis involves four essential steps: 1. Data cleaning (answer your research questions) 2. Data reduction: (answer your research questions) 3. Data Analysis/Interpretation ‘coding’ on Theme (theoretical framework, research objectives) 4. Data Display/Presentation Theme, citation FOUR BASIC STEPS
  • 15. -As you review your data (primary and secondary data), you find that some of it is not usable or relevant to your study… -Guide by your research question Step 1: Data Cleaning Source: Billups, F. D.
  • 16. Sample Transcript of Interview Data  Hello, good morning. Wow is very windy out there.  Thanks for inviting me today. Yes, a hot coffee would be nice, oh with cookie, that is lovely.  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 and 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…  Hahahahaha, you are really funny Source: Billups, F. D.
  • 17. Data Cleaning  Are some portions of this transcript unusable or irrelevant? (purple)?  What is my research question? The keywords I mean.  What is the theoretical framework? The parameters I mean.
  • 18.  Get a sense of the data holistically, read several times (immersion)  Classify and categorize repeatedly, allowing for deeper immersion (to find double meaning)  Write notes in the margins (memoing, classification)  Preliminary classification schemes emerge, categorize raw data into groupings (research questions-keywords, theory parameters) Step II: Data Reduction Source: Billups, F. D.
  • 19. Sample Transcript of Interview Data  Hello, good morning. Wow is very windy out there.  Thanks for inviting me today. Yes, a hot coffee would be nice, oh with cookie, that is lovely.  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 and 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…  Hahahahaha, you are really funny Source: Billups, F. D.
  • 20. Question How many category you can build from this transcript? What are these categories?
  • 21.  Code according to the (a)Category of Data (yes, no) (b)Keywords of Research Question (c)Parameters of Theory  Function of Coding 1. You want to explain 2. You want to make comparison (male/female) Coding Process
  • 22. 3 ways to do this: (a)Keywords of your research objectives/research questions (b)Parameters of your theory (c)Themes emerge from primary/secondary data Step III: Data Analysis and Interpretation
  • 23.  How do you cite? Single/collective (when to use which?)  Reporting Verbs google “bcchew reporting verbs”  Numbers could be used in qualitative data analysis (%, ratio)  Figures, tables, charts, diagrams could be used in qualitative data analysis Step IV: Data Presentation
  • 24. How do you present? -Theme -Storyline -Chronology -critical incident (like mind-map) …Step IV: Data Presentation
  • 26. Recording and Managing Qualitative Data Before data can be analyzed, they must be gathered and recorded into a form that makes analysis possible. Data can be recorded in text, audio or videotape, photographically, and field notes by memory. Managing qualitative data can be overwhelming at times. Making detailed lists of participants' information, along with the dates of their interviews, the date transcription was completed will help keep your data orderly. Create a file system early on so that you do not drown in piles of paper. There are some qualitative data analysis softwares.
  • 27. Analyzing Qualitative Data  There are some generic strategies that are part of almost every approach to data analysis. 1. immersion in the data 2. doing preliminary and informal analysis (during interview, transcribe the record early right after the interview) 3. making analytic memos (during interview, transcribe) 4. finding codes or themes (theoretical framework) 5. connecting the codes or themes into categories (theoretical framework, research objectives) 6. searching for confirming and disconfirming evidence 7. building a conceptual framework that explains the findings
  • 28. Lets Do A Hands-on Example
  • 29. Exercise Figure 1: Framework by Kumar & Srivastava (2013)
  • 30. Research Objective 1. To investigate the factors contribute to Malaysian customer loyalty on their telco (mobile telecommunication company)
  • 31. One of the questions (interview, questionnaire) 1. How long you have been the customer of this telco? –question for screening and selection. 1. How does the service quality offered by the telco (of your choice) won the loyalty of you as its user for so many years?
  • 32. What are the problem found?
  • 33. Lets try a new one
  • 34. What if we have more information about the Service Quality? Zeithaml, Parasuraman and Berry
  • 35. One of the questions (interview, questionnaire) 1. How long you have been the customer of this telco? –question for screening and selection. 1. How does the service quality offered by the telco (of your choice) won the loyalty of you as its user for so many years?
  • 36. Tools and Techniques  Microsoft Excel to display your data  Design well your data collection techniques (structured or semi-structured, guided by theory, listen during data collection)
  • 37. References  Chew, B.C. 2016. Qualitative Research. In: Borges, W. G. 2016. Business Research Methods. S.J. Learning. Ch.15. pp.227-242.  Billups, F. D. Qualitative Data Analysis. https://www.google.com/search? q=qualitative+data+analysis+ppt&oq=qualitative+data+ analysis+ppt&aqs=chrome..69i57j0l5.6374j1j4&sourcei d=chrome&ie=UTF-8  Brenna, M. 2005. Qualitative Data Analysis. Research Methods and Project Management. [online].  Chapter 13. Qualitative Data Analysis. [online]. www.uky.edu/~kdbrad2/EDP656/Notes/PowerPoint/Ch