Tue, Nov 18, 2003
Missing Data in Qualitative Research
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I'm currently working with MiroslavKlivansky and JoshRai on
BlueOxenAssociates' next research report -- an extensive case study of
the BlueOxen CollaborationCollaboratory, to be released next month.
The study is based on analysis of the community's archives correlated
with the results of a detailed survey of the community's participants.
The goal of the study is to discuss best practices within this
community and to propose a framework for examining communities and
collaboration. Internally, this is an opportunity to both improve the
collaboratory itself and also refine our research methodology. (AS)
We spent a significant amount of time developing the survey for the
study, which was an amazingly difficult process. We had two goals in
designing the survey. First, we wanted to gather information about
participant behavior that we couldn't gather from the data itself.
For example, we had know way of knowing how much time each participant
spent following the community's discussion. Second, we were trying to
determine whether or not the community had QWAN (QualityWithoutAName).
The problem with this question, of course, is that you can't just ask
it on the survey and expect to get meaningful responses. (AT)
While struggling with these problems, Miroslav drew our attention to
an article by Supriya Singh and Lyn Richards in a recent issue of
QualitativeResearchJournal? -- "Missing data: Finding 'central'
themes in qualitative research" (v3, n1, pp5-17). The article was
therapeutic in that it not only empathized with the challenges we were
facing, it identified them as standard steps in the research process.
Additionally, the article served as a testament to the NUD*IST
qualitative analysis tool (the predecessor to
NVivo). (AU)
Singh and Richards write (AV)
It is rare to find research accounts that do not make the emergence of a theory appear a smooth, even inevitable process. Our own experiences, and those of our students, have never fitted such smooth images, and in discussions we have often found that others are helped by our accounts of the puzzles and anxieties, and the hard detective work, which we have experienced during the analysis stage when a picture appeared to be emerging, but jigsaw pieces were evidently missing. (6) (AW)
They then explain that the initial research question will inevitable
evolve, and hence, there will always be missing data. They also add
that survey questions will not always garner the desired information,
and hence, the research process must be iterative in order to fill in
the blanks. The authors go on to describe the research process for
two studies with which they were involved, and explain how they
reacted when they discovered missing data. (AX)
Both authors used the NUD*IST tool extensively, and apparently, the
results of their projects "contributed to the further development of
the software" (15). I have not had a chance to experiment with NVivo
yet, but I hope to do so soon. It sounds like an intriguing tool. (AY)
/articles |
Posted at 9:06pm
EEK Speaks
A blog about collaboration, community-building, and the various
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Associates, with occasional digressions on food and other vital
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