Qualitative research analysis
A dialectic process
Process is dialectic not linear:
– Learn something
– Collect data
– Make sense
– Go back and get new experiences
– Refine interpretation (more analysis)
– And …
Steps of QA
Is not a cookbook fashion
Should be done artfully, even playfully
Translation of field work into a text
(communicating understanding to reader)
Steps of QA, data preparation
– Transcribing data
Who will transcribe
Transcribe all data?
Organize field notes
Research question and theoretical framework
– Positivist view
– Interpretive view point
Importance of researcher’s point of view
Multiple levels of meaning
– Feminist view point
Listen to the data, special moments:” you know what I mean”
Read and think about texts
– Mark up and highlight important sentences
– Write down ideas
– Emphasize on description
– Code data, don’t wait for all data
Coding and analyzing
Open coding: finding segments in textual
data and giving a label (code)
– What is going on?
– What are people doing?
– What is the person saying?
– What do these actions and statements take for
– How do structure and context serve to support,
maintain, impede, or change these actions and
I don't think that the ideal woman has to look like anything
personally. I think the ideal woman has personality and character,
its how you act. My looks don't bother me, it's just my
personality. My personality. I wanna have a good personality and
have people like me, if they don't like me for my personality, or
just because of my looks, then they must be missing out on
something. Um, when you have it [self-esteem] so much that you
don't care what people .. think about you. I man, I flaunt my self-
esteem, not like 'Oh yeah, dahdadada: I just sit up real straight
and that shows self-esteem right there.
I'm a woman, I'll wear stuff to school that's like . . . wacked.
I have earrings that are about this big, and that shows my self-
esteem, I don't care what you say. about them, , , Oh well, that's
what I think, I don't care, I don't fit in anywhere anyway, I'm my
own self so why can't I act like that, why can't I dress like that?
I don't think that the ideal woman has to Ideal woman
look like anything personally. I think the Importance of personality
ideal woman has personality and character,
its how you act. Physical appearance is secondary
My looks don't bother me, it's just my Importance of personality
personality. My personality. I wanna have a
Importance of personality
good personality and have people like me, if
they don't like me for my personality, or just Missing out on noticing personality
because of my looks, then they must be
missing out on something.
Um, when you have it [self-esteem] so much Self-esteem
that you don't care what people .. think Don't: care what others say
about you. I man, I flaunt my self-esteem, Flaunting myself
not like 'Oh yeah, dahdadada: I just sit up Sits straight
real straight and that shows self-esteem
I'm a woman, I'll wear stuff to school that's
like . . . wacked. Wears what she wants
I have earrings that are about this big, and Wears big earrings
that shows my self-esteem, I don't care what Doesn't care what others say
you say. about them, , , Oh well, that's what
I think, I don't care, I don't fit in anywhere
anyway, Internal self-assessment: own person
I'm my own self so why can't I act like that, Internal self-assessment:
why can't I dress like that? wears what she wants
From level of code to the level of category
Descriptive codes within one’s data and
hoping to generate a set of key concepts
Reading then marking then coding (open
coding) segments then immersion and
induction then look for common ways or
"A new idea for a code.
Just a quick hunch.
As a dialogue amongst researchers.
To question the quality of the data.
To question the original analytic framework.
What is puzzling or surprising about a case?
As alternative hypotheses to another memo.
If you have no clear idea but are struggling to find
one. To raise a general theme or metaphor.
Similar and different ways to talk about an
– Integrates the theme with data and literature
– Look like a paper
– Positivist and interpretive framework
– Issue of interpretation and storytelling
Data reduction and collapse
Validity and reliability of
Validity as craftsmanship
– Are you telling a convincing story?
– Try theorizing from your data interpretations.
– Have you reached your findings with integrity-have you checked your
– Look for negative cases.
– Make your interpretations available for discussion (agreement and debate)
among "legitimate knowers"
– How do your findings impact those who participated in the research,
– How do your findings impact the wider social context in which the
– research occurred?
– Is there "internal consistency" (Neuman, 2003)? Does the data add up?
Computer assisted software for
– Word processors
– Text retrievers
– Text-base managers
– Code and retrieve program
– Code-based theory-building programs
– Conceptual network building programs
– Textual mapping software
Uses of computer software
Making notes in the field
Writing up or transcribing field notes
Editing: correcting, extending, or revising field notes
Coding: attaching keywords or tags to segments of text to permit later retrieval
Storage: keeping text in an organized database
Search and retrieval: locating relevant segments of text and making them
available for inspection
Data "linking": connecting relevant data segments to each other, forming
categories, clusters, or networks of information
Memoing: writing reflective commentaries on some aspect of the data as a basis for
Content analysis: counting frequencies, sequence, or locations of words and
Data display: placing selected or reduced data in a condensed, organized format,
such as a matrix or network, for inspection
Conclusion-drawing and verification: aiding the analyst to interpret displayed
data and to test or confirm findings
Theory building: developing systematic, conceptually coherent explanations of
findings; testing hypotheses
Graphic mapping: creating diagrams that depict findings or theories
Preparing interim and final reports
Fears and critics
Computer programs will separate the qualitative
researcher from the creative process
The line between quantitative and qualitative analysis will
be blurred by imposing the logic of survey research onto
qualitative research and by sacrificing in-depth analysis
for a larger sample.
Computer software programs will determine the types of
questions asked and specific data analysis plans
Computer programs for analyzing qualitative data require
the researcher to be more explicit in the procedures and
analytical processes they went through to produce their
data and their interpretations.
Loss of confidentiality through the use of multimedia data.
Writing up of qualitative data
– Traditional writing
– Take the form of a scholarly publication
– Presentation of respondents is a “true” reflection
– Detail about authors are absent
– Concrete details of what, how often, what order and
– Identify typical activities
– members point of view and interpretation of events
– Meaning of setting from members’ perspective
– Reflexive style