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| Anne Adams during a presentation |
While writing up my findings for my pilot
study, I am lucky enough to follow a workshop on grounded theory here at the
Open University. This post reflects some of the points raised by the presenter Anne Adams, so live blogging from the workshop.
Starting from previous qualitative
experiences of all the participants, Anne gets an idea of her audience. Anne
who is very energetic and clearly so knowledgeable that she is open to
questions from the floor at any moment. Anne’s approach is starting from the data.
Premise: it can start from mixed methods,
as well as purely qualitative
Important for PhD justifying the
methodology used.
Quantitative versus qualitative; either way reflective
With grounded theory the results come from
the data of the participants, so the subjectivity of the qualitative is in
dialogue with the predetermination of the quantitative.
Quantitative challenge: imposing external
system of meaning for internal subjective structures, whereas grounded theory
comes from the participants.
Qualitative challenge: generates working
hypothesis by producing concepts from data, representing participants reality
in its complex context. But here the researcher’s assumption does add to
framing the data.
So research always has challenges through
the instruments used. In ALL research challenges emerge. So being reflective is
the answer to reach validity.
Grounded theory Background
Glaser and Strauss (1967) Glaser comes from
quantitative, and Strauss from qualitative.
The theory is the end goal of their
Grounded Theory approach. (Henwood and Pidgeon, 1992, p. 101): “both qualitative
and quantitative approach…”
An important view when writing up the
verification/argumentation: important view: looking at who will examine your
research: educationalists, technologists…
Another important idea to remember is that
Grounded Theory (GT) is an iteration. So the coding is not done linear, but
iterative, where the linear is occurring in stages to find depth and meaning,
after which the whole argumentation is thought through again.
GT strengths
Phenomena complexity
Unknown phenomena
Structured/focused approach to theory
building
Integrating mixed data sources
It is a skill as a researcher that you can
continually manage to combine detail to theory in a valid justification.
Quality rules (Henwood and Pidgeon, 1992)
- Importance of fit with the data
- Integrated at all levels of abstraction
- Reflexivity (always look for the why, justification)
- Keep documentation (field notes, memo’s, notes taking during the exploration)
- Theoretical sampling and negative case analysis. The sampling is very central to the process, because the experiment is not designed, the reason for selecting your participants becomes more important and should be clearly mentioned in your PhD account.
- Theoretical sensitivity (the methodological approach, you should not go in with a prior theory – in theory – this is seen by Glaser as polluting, but there are different flavours of GT. So you need to take a position on what you use, which GT you follow, Anne went in not with a framework, but being guided by some theory (Inge, wondering if this is more Charmaz?).
- Transferability: how far can your findings be transferred beyond your group. The GT purists would say that any theory coming out can only be related to that specific group, but there is an element of transferability to different contexts. So the sample might be generalizable to other contexts. This might also be of importance to your PhD dissertation, but you must be very clear on it not to ignite more discussion than necessary.
A qualitative approach to HCI by Adams: http://oro.open.ac.uk/11911/ (2008) and another one but not typing quick
enough to get that one, scholar googled Anne Adams here.
GT application
Data in whatever form is: broken down,
conceptualised, and put back together in new ways
Analysis stage – 3 levels of coding: open,
axial and selective.
Open (concepts are identified, grouped into
categories – more abstract concepts and hooks, properties and dimensions of the
category identified. Each category might cover a specific property, and will
have a dimension – and dimension range - either frequency it occurs, or scope
that it has, the intensity with which it is mentioned, and the duration it
refers to). A rule of thumb with open coding is to look for frequency and if it
is only mentioned infrequently, than it might be fundamental (e;g. after that I
never learned anything online again). The idea of saturation is the moment that
you know you have gone far enough. Saturation will emerge where you no longer
find fundamental new ideas.
Axial: start to move up from the
categories, looking for high-level phenomena and conditions: causal conditions,
contextual condition, intervening conditions.
Phenomena action/interaction strategies and consequences identified by
your participants. Where phenomena are central ideas or events. Whereas
conditions are events that lead to occurrence or development of a phenomenon.
The context is a set of properties (location, e.g.) that pertain to the
phenomenon. Intervening conditions provides a light coming from a broader
structural contexts (e.g. is it the individual, the organisational, the
societal … which depends on the research question you started from and which
you are searching an answer to). Action/interaction strategies: devised to
manage, handle, carry out, respond to a phenomenon under a specific set of
perceived conditions. Consequences – outcomes or results of action/interaction.
(e.g. when I want o have (context) a
personal conversation (phenomenon), I encrypt the message (strategy), I think
this makes the email private (consequence). )
Selective (latter with process effects):
·
Select the core category (central
phenomenon around which all the other categories are integrated) and high level
story line (a descriptive narrative about the central phenomenon). The high
level story line comes from your core category, so just a couple of sentences
at the very most, that which goes into your abstract.
·
Related subsidiary categories
by its properties (the other things are all related – in many cases by the
properties – to your core category)
·
Relate categories at the
dimensional level (it might be related dimensionally: some a lot, some less)
·
Iterative validation of
relationships with data
·
Identify category gaps (telling
what might be related, but is NOT what you are addressing with your research –
patching holes provides the boundaries of your research)
At the end you – as a researcher – need to
find a missing part of the puzzle
Lines between each type of coding are
artificial
·
Data presented at dimensional
level
·
Action/interaction and
conditions present
The solution to come to these results are
tools.
Very important: keep relationship coding
notes in open coding/analysis without loss of detail, and code both open and
axial together.
My additional question: who area non-purist
GT theorists
Gilbert Grounded design flavour? (not sure about this, might have misheard it)
Focus is very variable, because GT has been
adapted by many disciplines resulting in different GT flavors : so best is to
look at your discipline and look at papers from that area.
Charmaz is really good for a intermediate
approach that allows staring from some theoretical assumptions.
Tool
remarks
Atlas TI fits perfectly with grounded
theory (more than NVIVO), within CREET group of licences for Atlas TI (Inge,
you must ask whether CREET has licences to offer you as a PhD student)
Using an analysis tool makes it easier to
keep the codes up-to-date through the overall process, even if you are changing
the code names. Atlas works better with multimedia files (easy coding). BUT the
tool should never stand in the way of rigor and personal work/research, if the
tool feels restrictive, it is better to use physical options that work for you
post-it notes, Word…
