Dear David,
What an intriguing post! I have a question, but I might be opening a can
of worms (maybe we should transfer this to another post of its own). You
wrote:
-snip-
"The point to make here is that these approaches to collecting data—science
and design—derive from the different epistemologies at work in these
different disciplines: adding to knowledge on the one hand, and making
decisions in a problem solving context on the other."
-snip-
You are of course right for a practical/professional setting, but these two
epistemological cultures inevitably mix (and sometimes clash) in an
academic environment, especially in graduate programs. Disciplines and
professions are inherently tied but analytically separate entities. So
suppose a hypothetical situation in which a PhD student in a graduate
program in design is creating a "design" (product, service,
interface, software, you name it...) as a part of her/his PhD. In that
case, I (as a social scientist) would probably not like the idea of 6-10
ten participants in testing , as he/she would not be able to capture enough
variation. So maybe a small N during the actual design phase of this
entity, but a bigger N -by big I do no imply huge numbers,btw- during
testing? Because, this student is essentially aiming to create somehow
generalizable (I am aware that generalizable is a dangerous word) knowledge
out of this "project".
Again, I realize this is a huge and potentially contested topic, but as one
of the co-directors of a interdisciplinary graduate program I keep bumping
into this potentially problematic situation time and again. I don't have
the answers myself, and I do not want to force my own epistemological
prejudices on anybody but also I don't want students to walk away with
problematic research designs.
Yours,
ali
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