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Qualitative Data Analysis. Tim Winchell Analytical Techniques for Public Service The Evergreen State College Winter 2011. “It wasn’t curiosity that killed the cat. It was trying to make sense of all the data curiosity generated.” - Halcolm. Qualitative Data.

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Qualitative data analysis

Qualitative Data Analysis

Tim Winchell

Analytical Techniques for Public Service

The Evergreen State College

Winter 2011

It wasn t curiosity that killed the cat
“It wasn’t curiosity that killed the cat.

It was trying to make sense of all the data curiosity generated.”


Qualitative data
Qualitative Data

“Qualitative Data… have been gathered during the conduct of

interpretive or postpositivist research studies. They exist most often

as some sort of narrative.” Examples include:

  • Written text

  • Conversation, interview, or consultative transcriptions

  • Focus group transcriptions

  • Field notes

  • Diaries

  • Legal transcripts

  • Newspaper clippings

  • Journal or magazine articles

  • Photographs

  • Maps

  • Illustrations

  • Paintings

  • Musical scores

  • Tape recordings

  • Films (McNabb, p. 368)


  • Grounded in a specific context/situation

  • Real life events/ settings; lived experience

  • Deep layers of meaning; rich description filled with differing perspectives, symbolism, metaphor, and meaning.

  • “Descriptions form the bedrock of all qualitative reporting.” (Patton, p. 438)

  • “The devil is in the details.”


  • Labor intensive

  • Requires creativity

  • Conceptual sensitivity

  • Non- formulaic (Polit & Beck, p. 570)

  • Research Bias

  • Cost of processing/coding data

  • Small sample size, many variables

  • Very limited generalizability

  • Credibility

One synopsis of the challenges
One Synopsis of the Challenges

“The challenge of qualitative analyses lies in making sense of massive

amounts of data. This involves reducing the volume of raw information,

sifting trivia from significance, identifying significant patterns, and

constructing a framework for communicating the essence of what the data


There are no formulas for determining significance. No ways exist of perfectly

replicating the researcher’s analytical thought processes. No straightforward tests

can be applied for reliability and validity.

In short, no absolute rules exist except perhaps this: Do your very best with your

full intellect to fairly represent the data and communicate what the data reveal

given the purpose of the study.” (Patton, p. 432-433)

Data management
Data Management

  • The complexity of the project drives the level of organization needed

  • Format field notes consistently

  • Index notes, so you can find documents easily

  • Make sure you can read them!

  • Have a sensible system for cross referencing your notes

  • Please remember to:

    • Maintain data confidentiality as much as possible

    • Secure your data when not in use

    • Maintain participant confidentiality

And another data back up reminder
And… Another Data Back Up Reminder!

“Thomas Carlyle lent the only copy of his handwritten

manuscript on the history of the French Revolution, his master

work, to philosopher J. S. Mill, who lent it to Mrs. Taylor. Mrs.

Taylor’s illiterate housekeeper thought it was waste paper and

burned it. Carlyle behaved with nobility and stoicism, and

immediately set about rewriting the book. It was published in

1837 to critical acclaim and consolidated Carlyle’s reputation as

one of the foremost men of letters of his day. We’ll never know

how the acclaimed version compared with the original or what

else Carlyle might have written in the year lost after the

fireplace calamity.” (Patton, p. 441- emphasis added)

Your general approach
Your General Approach?

  • Grounded

    Start data collection with few preconceived notions about what’s

    going on….no pre-formed coding scheme)

  • ….or Framed?

    Specific events, behaviors you intend to look for, with coding scheme

    already partially developed. Oftentimes use diagrams to explain ideas.

  • All analyses benefit from diagramming and concept mapping, as Babbie discusses (p. 405).

Qualitative analysis the general process
Qualitative Analysis: The General Process

  • Data Reduction

  • Coding

  • Data Display

  • Conclusion Drawing

  • These are not linear, but concurrent processes

  • The less “framed” and more grounded the process, the more they are concurrent: constant comparison

Data reduction
Data Reduction

  • First, we transform data from field notes or transcriptions

  • Write up and/or transcribe field notes and print.

  • Which of the data are most useful?

  • “Developing some manageable classification system or coding scheme is the first step of analysis.

  • Without classification there is chaos and confusion.

  • Content analysis, then, involves identifying, coding, categorizing, classifying, and labeling the primary patterns in the data.” (Patton, p. 463)


  • For extensive research projects, summarize interviews with a brief cover sheet

  • Who, what, where, when, importance, summary of key contacts

  • Coding schemes…must match the complexity of the project

  • Use similar semantics

  • Identifying concepts, patterns, memos

What is coding
What is Coding?

In short, codes are shorthand descriptors of:

  • Setting and context

  • Subjects’ perspectives, which could include their thinking about people and objects

  • Processes, activities, and/or strategies

  • Relationships and social structures

  • Any preassigned coding schemes

    (Bogdan & Biklen, 1992, p. 166-172, as quoted in Creswell, p. 193)

  • Creswell recommends analyzing data using codes readers would expect to learn more about, find surprising, and address larger theoretical issues in the literature. (p. 193)


  • Start categorizing early… Or …..

  • Dive deeper into the data and avoid making judgments too early… make tentative observations about what might be happening….

  • To further analyze what is happening:

  • Write memos to yourself

  • Use “concept mapping” (Babbie, p. 405)

  • Build preliminary typologies

  • Try to use outcome/ process matrices (Patton, p. 468-477)

Open coding one approach
Open Coding….One Approach

  • Start with a sample of the data

  • Read responses carefully…

    Keep research questions in mind

  • Make rough categories of these descriptors that seem to belong together and code them with a key word.

    • Utilize constant comparison- similarities and differences.

    • Work to saturation.

Farm to school example
Farm to School Example

Why do local farmers participate in the local farm to school program?

  • Resp.1: It makes the most business sense to me….

  • Possible code: ‘business sense,’ busin.

  • Resp. 2: “It gives me great pride to think of my organic produce being consumed locally by my family members, friends, and church members and their children.

  • Possible code: “service,” serve

Farm to school example1
Farm to School Example

Business Sense (Busin.)

Service (Serve)

1. Most business sense

3. Reduces transport costs

3. Ability to hire more

4. Reduces environmental impact- transport

6. Stability of local school district market

1. Belief in organic produce being consumed locally

1. Organic production for nuclear family, friends, & church members & their children

2. Service to local community

5. Some contribution to local school district (lower prices received)

Write ongoing memos and abstracts

Write ongoing memos and abstracts

Comprehending the basic goal of this stage
Comprehending: The Basic Goal of this Stage

  • Identify important phenomena

  • Identify broad themes

  • Document codes that emerge

  • Begin to speculate about what might be happening

  • Write ongoing memos and abstracts

Axial coding
Axial Coding

  • Explore the relationships between and among codes

  • Look for:

  • “Contexts

  • Causal Conditions

  • Phenomenon central ideas

  • Strategies for addressing the phenomenon

  • Intervening conditions

  • Action/ interactions

  • Consequences” (Gibbs video)

  • Develop subcategories, linked by a “paradigm.”

  • Paradigm includes conditions, actions/ interactions, and consequences (Polit & Beck, p. 584)

Employee self care example how could agencies promote employee self care in their organizations
Employee Self Care ExampleHow could agencies promote employee self care in their organizations?

Organizational Changes (OrgCh.)

Employee Changes (EmpCh.)


Management Training

Supervisory Best Practices

Employee Awareness

Health Education Initiatives

Medical Coverage Incentives

Individual Health Surveys/ Contracts/ Teaming

Employee Best Practices

Selective coding
Selective Coding

  • Identify core phenomenon

  • Develop story line around the core concept(s)

  • Compare and contrast the core concept(s) to other selective coding categories (Gibbs video)

  • Findings are integrated and refined

  • Include diagrams (Polit & Beck, p. 584)

Data display
Data Display

  • Playing with typologies and displays is a part of the analysis process

  • See Miles and Huberman, Qualitative Data Analysis

  • Make sense of the data by playing with visual means of representing the patterns that are emerging from the analysis

  • Process and outcome flow charts/ matrices

Interpretation by definition
“Interpretation, by definition

involves going beyond the descriptive data. Interpretation means attaching

significance to what was found,

making sense of findings,

offering explanations,

drawing conclusions,

extrapolating lessons,

making inferences,

considering meanings,

and otherwise imposing order on an unruly but surely

patterned world.” (Patton, p. 480)

Theorize cause and effect
Theorize: Cause and Effect?

Classic Conditions for Establishing Cause and Effect

  • Variables Covary

  • Covariance is not spurious

  • Logical time order

  • A lucid explanation is available

  • Or …clusters of phenomena, identify things that tend often to show up together, even if the causal connection is not clear

Analysis of medical errors
Analysis of Medical Errors

  • “Figure 1 classifies the stage in the diagnostic testing process and the transition points within and between stages at which errors can occur, and presents representative occurrences that fall into each of them.” (Harris, et al.)


  • Triangulate from multiple sources or methods

  • Use several researchers as a reliability check.

  • Use rich, thick description in order to provide for the shared experience

  • Clarify research bias up front

  • Look for disconfirming evidence

  • Spend prolonged time in the field to develop an in-depth understanding

  • Use peer debriefing

  • Use an external auditor to review findings (Creswell, p. 196)

  • Complete several case studies. (Yin, 2003)

  • Review finding with participants.

  • If it’s just you, double or triple check your data and conclusions


  • Be true to the data

  • Don’t get too carried away by particularly eloquent, memorable, or “simple” respondents—this creates a cognitive bias

  • Always check and recheck both the data and conclusions you draw from it

Qualitative validity
Qualitative Validity

Traditional Criteria for Judging Quantitative Research

Alternative Criteria for Judging Qualitative Research

Internal validity

External validity






Confirmability(Trochim, 2006)

Drawing conclusions
Drawing Conclusions

  • Summary of data and results of coding analysis

  • Patterns and themes

  • Clusters of similar findings?

  • Case comparisons

  • Powerful metaphors

  • Any data for which your theory can’t provide a reasonable explanation?

Final thoughts
Final Thoughts

  • Data Management and Analysis work hand in hand

  • Coding is technical work, which is improved upon with advanced practice, study, and interpretation

  • Remember to consult additional resource materials

    (Some are listed at the end of the PowerPoint)

  • Utilize the Internet judiciously

  • Qualitative data software resources are reviewed in many publications and on-line

Workshop case tesc alumni relations
Workshop Case: TESC Alumni Relations

Research Interest

  • Why do colleges and universities have alumni programs?

    Research questions

  • What are TESC graduates’ perceptions of TESC’s alumni programs?

  • What kind of alumni program do they want?

  • How do they recall their experience as TESC students?

  • What connects them to the College?

  • What nourishes that connection?

  • What can AR do to improve those connections?

Workshop methods results overview
Workshop Methods/ Results Overview

  • Draft questions; approval from Alumni Relations

  • Zoomerang online survey

  • 1647 responses

  • One researcher

  • Pluses: clear conclusions, grounded in data

  • Minus: not validated by second researcher

Workshop exercise
Workshop Exercise

  • Code 2 or 3 pages of the data from the responses to the Alumni survey question.

  • “What was the best part of your experience at Evergreen?”

  • Code individual responses

  • What are the most common codes?

  • What do these data tell you/us about these alumni ? About Evergreen?


YouTube Search “qualitative research coding”

Graham R. Gibbs Qualitative Research Coding Series

  • Open Coding:

  • http://www.youtube.com/watch?v=gn7Pr8M_Gu8

  • http://www.youtube.com/watch?v=vi5B7Zo0_OE&feature=related

  • http://www.youtube.com/watch?v=n-EomYWkxcA&feature=related

  • http://www.youtube.com/watch?v=AwmDRh5l7ZE&feature=related


YouTube Search “qualitative research coding”

Graham R. Gibbs Qualitative Research Coding Series

  • Axial Coding:

  • http://www.youtube.com/watch?v=s65aH6So_zY&feature=related

  • Selective Coding:

  • http://www.youtube.com/watch?v=w9BMjO7WzmM&feature=related

  • Grounded Theory:

  • http://www.youtube.com/watch?v=4SZDTp3_New&feature=related

  • http://www.youtube.com/watch?v=dbntk_xeLHA&feature=related

    Morgan, D. L. (1997). Focus Groups as Qualitative Research (2nd Ed.). Sage Publications: Thousand Oaks, CA.

Software resources
Software Resources

Computer Programs:

See Babbie, p. 406-416

Data analysis strategies for qualitative research- Research Corner http://findarticles.com/p/articles/mi_m0FSL/is_6_74/ai_81218986/?tag=content;col1

Software for qualitative research


Software for qualitative research



  • Babbie, E. (2010). The Practice of Social Research (12th Ed.). Wadsworth Publishing: Belmont, CA.

  • Creswell, J. W. (2003). Research Design: Qualitative, Quantitative, and Mixed Methods Approached (2nd Ed.). Sage Publications: Thousand Oaks, CA.

  • Harris, et al. Mixed Methods Analysis of Medical Error Event Reports: A Report from the ASIPS Collaborative


  • McNabb, D. E. (2002) Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches. M.E. Sharpe: Armonk, NY.

References ii
References II

  • Miles, M. B., & A.M. Huberman. (1994). Qualitative Data Analysis. (2nd Ed.). Sage Publications: Thousand Oaks, CA.

  • Patton, M. Q. (2002). Qualitative Research & Evaluation Methods (3rd Ed.). Sage Publications: Thousand Oaks, CA.

  • Polit, D. F., & Beck, C. T. (2004). Nursing Research: Principles and Methods (7th Ed.). Lippincott Williams & Wilkins: New York, NY.

  • Trochim, William M. K. (2006). Research Methods Knowledge Base. http://www.socialresearchmethods.net/kb/qualapp.php

  • Yin, R. K. (2003) Case Study Research (3rd Ed.). Sage Publications: Thousand Oaks, CA.


Making Sense of Qualitative Data

TESC MPA Program ATPS Winter 2010Geri/Gould/McBride