Data Analysis for Qualitative Research

Finding
Nemo
Perfect
Storm
The Shape of
Water
TheShallows
Deepwater
Horizon
Shark
Tale

Finding
Nemo
Perfect
Storm
The Shape of
Water
TheShallows
Deepwater
Horizon
Sharks
Tale

Educational research can “meet the
needs of the educational professional
who is employing research processes
and skills as a basis for
‘improvement’…”
Burton, Brundrett and Jones (2014, p. 7)

Snapshot of Reality - case
study of an individual child’s
(or teacher’s or class’s or
school’s) needs

An impact analysis – designed
around the need to establish
how effective a particular
resource, pedagogic approach,
policy, strategy etc. has been.

An multi-cycle approach –
where the research and the
intervention almost constantly
inform one another .

of massive amounts of data
Make sense

the volume of information
Reduce

Constructa framework for communication

You know you are doing qualitative
research when…’you can’t see the
kitchen table because it’s
completely covered in paper’.
Hastie and Glotova (2012)

“There are times when you have so
much data, it can get quite
overwhelming deciding how to
start making sense of everything”

Denzin and Lincoln (2011) note that [qualitative
research] is a complex, interconnected family of
concepts and assumptions surround the term,
and qualitative research, as a set of interpretive
activities, privileges no single methodological
practice over another.
Smith and Sparkes (2017)

‘[we]…have few agreed-on canons for
qualitative data analysis, in the sense
of shared ground rules for drawing
conclusions and verifying sturdiness’
Bogdan and Biklen (1982, p. 145)

working with data, organizing it, breaking
it into manageable units, synthesizing it,
searching for patterns, discovering what is
important and what is to be learned, and
deciding what you will tell others
Miles and Huberman (1984)

By it’s very nature, qualitative research generates
a plethora of messy data. The problem facing a
researcher is how to take all these data and make
sense of them in ways which a reader can trust,
and to which they can relate.

Get your data into a useable form

Begin the sorting process

Decide on the type of analysis

Develop initial categories

Checking category validity

Constructing themes

It is not always the case that there is only
one analytic approach ideally suited to a
particular research question or design. So
we are not suggesting that qualitative
analysis starts and ends with TA!
Braun, Clarke and Weate (2017)

TA is like following a hose through long
grass where you cannot see clearly the way
ahead, and the path is not direct: sometimes
you move forward; other times you coil back
on yourself.

TA offers a method for identifying
patterns (“themes”) in a dataset, and for
describing and interpreting the meaning
and importance of those

TA works really well with textual data,
both research generated (e.g., through
diaries, story completion, vignettes), and
pre-existing (e.g. policy documents or
schemes of work), or an combination

GT is a flexible, systemic,
comparative method of
constructing theory from data.
Charmaz, Thornberg and Keane (2018, p. 187)

Analysis begins early. We grounded theorists
code our emerging data as we collect it.
Through coding, we start to define and
categorise or data.
Charmaz (2010, p. 187)

In GT coding, we create codes as we study our
data. We interact with our data and pose
questions to them while coding them. Coding
helps us to gain a new perspective on our material
and to focus further data collection, and may lead
us in unforeseen directions.
Charmaz (2010, p. 187)

a form of self-reflective enquiry undertaken by
participants in social situations in order to
improve the rationality and justice in their own
social or educational practices, as well as their
own understanding of these practices and the
situations in which these practices are carried out.
Henry and Kemmis (1985, p. 1)

Think – and identify a general problem or idea
Casey et al. (2017)

Plan – and settle on an initial way of approaching
the general problem or idea
Casey et al. (2017)

Act – and teach the first lesson/session
Casey et al. (2017)

Evaluate – using observation or data gathering
tools, seek to understand how you act impacts
learning and how this impacts on your general
problem or idea.
Casey et al. (2017)

Reflect – on the action and the learning it
stimulated. Gauge the strengths and weakness of
the plan and allow yourself to begin to re-think
the problem a little (if needed) - which is step 6.
Casey et al. (2017)

Describe a six-phase model. This model risks
representing the process of TA as akin to walking
(not running; qualitative research is not that
quick!) up a flight of stairs, where your progress
from start to finish is clear and direct

The first phase of TA is familiarization – the
process of deeply immersing yourself in your data,
so you become intimately familiar with their
content. What this practically involves is reading
and rereading all data items, and making notes.
Phases 1-2: Familiarization and Coding

Coding turns the informal note taking of
familiarization into a systematic and thorough
process. Familiarization ensures you begin coding
with some sense of the sorts of things you will
code for, but it doesn’t delimit the coding.

The practical process of coding involves closely
reading the data, and “tagging” with a code
each piece that has some relevance to to your
research question. Text can be tagged with one
or more codes

Initial/open coding starts the chain of theory
development. Codes that account for our data
take form together as nascent* theory that, in
turn, explains these data and directs further
data gathering.
Initial/Open Coding
Charmaz (2010)
*Beginning

Initial/open coding proceeds through our examining
each line of data and then defining actions or events
within – line-by-line coding. This coding keeps us
studying our data…this form of coding helps us to
remain attuned to our subjects’ views of their realities,
rather than assume we share the same views and worlds.
Initial/Open Coding
Charmaz (2010)

Charmaz (2010) noted “that I keep the codes active.
These action codes give us a insight into what people are
going, what is happening in the setting i.e. deciding to
relinquish, accounting for costs, weighing the balance,
relinquishing identity, making identity trade off.”
Initial/Open Coding
Charmaz (2010, p. 188)

These three phases involve the core analytic work
in TA: organizing codes and coded data into
candidate themes, reviewing and revising those
themes, and developing a rich analysis of the data
represented by the final themes.
Phases 3-5: Theme development, refinement and naming

The process of theme development is about
clustering codes to identify “higher level” patterns
– by which we generally refer to meanings which
are broader and capture more than one very
specific idea – you want you themes to have layers.

It is critical to understand that a “theme” is more
than just some coherent, patterned meaning
across the dataset – it also has to tell you
something important about the data, relevant to
your research question.

Refinement or reviewing involves working first with the
coded data, and then going back to the whole dataset.
The process is about checking two things: first, with
your analysis “fits well” (or well enough) with the data
and you are not misrepresenting them, inadvertently,
through poor coding;

And second, whether the story you’re telling is a
compelling and coherent way of addressing your research
question… revision can range from minor tweaks to a
complete restart of the data analysis – you have to be open
to the possibility that you need to “let’s go” of some or all
of your analysis if the review raises problems.

You also have to decide what you going to call each each
thing. Prenatal range from the prosaic to the creative –
to some extent, how creative you can be will depend on
the purpose of the research. Ultimately, you want to
name the catches the essence of the theme, but beyond
this, is up to you.

Memo writing is the intermediate step between coding
and the first draft of the completed analysis. This step
helps to spark thinking and encourages us to look at our
data and codes in new ways. It can help us to define
leads for collecting data – both for further initial coding
later theoretical sampling.
Memo Writing
Charmaz (2010, p. 189)

As we grounded theorists refine categories and develop
them as theoretical constructs, we likely to find gaps in our
data and holes in our theories. Then we go back to the field
and collect the delimited* data to fill those conceptual gaps
and holes – we conduct theoretical sampling
Theoretical Sampling
Charmaz (2010, p. 192)
*limits

The aim of this sampling is to refine ideas, not to increase
the size of the original sample. Theoretical sampling helps
us to identify conceptual boundaries and pinpoint the fit
and relevance of the categories. Although we often sample
people, we may sample scenes, events, or documents,
depending on the study and where the theory leads us.
Theoretical Sampling
Charmaz (2010, p. 192)

A first step here is to obtain a general sense of the sets of
information and reflect on their overall meaning to get
a first impression, from the ideas and their tone about
the overall depth, credibility and the use of the
information. Make notes in the margin and record
general thoughts at this stage.
Read Through
Koshy (2010, p. 113)

Creswell’s (2009) suggests you: code what readers
expect to find based on past literature and
common sense; code what is surprising and
unanticipated; code for the unusual which may be
of conceptual interest to readers.
Coding
Koshy (2010, p. 113)

Generate a description of the setting or people as well
as categories of themes for analysis. These need to
appear as headlines. Decide how the description will
be represented in the qualitative narrative. This step
involves making a interpretation what deriving
meaning for the day.
Generate descriptions
Koshy (2010, p. 113)

Even the most rigorous and scholarly
research on an important research topic
might be considered unsuccessful if it fails to
engage the intended audience.
Writing as inquiry
Kirk and Casey (2012, p. 338)

Research writing is never a neutral process. The
data researchers is including written text –
whether they be results from a questionnaire,
voices from the interview observations from field
– actively selected from a larger bank data.
Writing as inquiry

Selection of data for inclusion in text is the outcome of an
intentional process of reporting findings and evidencing
arguments. At the heart of effective research writing is a
process of selection, of what to include and what to omit,
the process that ultimately reflects what authors believed to
be worth saying about the topic
Writing as inquiry

Data Analysis for Qualitative Research

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