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Having a little dilemma here which I guess others before me have
confronted. Being self-taught in everything SNA, I pose my question to
this email list, hoping for some tutoring on the subject!
I'm currently doing a reg. equivalence analysis on energy flows (energy
content in four fuel commodities) between the countries of the world -
data is valued, directional with quite a large value span among the flow
values. Using the REGE-algorithm in the Ucinet package, 3 iterations,
selecting the number of partitions based on an Anova Density check for
different number of partitions (as used in Luczkovich et al).
When using 99 countries in my dataset, I get an optimal split at 11
partitions (i.e. positions containing role-equivalent actors). Two of
these are singleton positions, i.e. containing only singular countries,
and two positions contain only two countries each. All these 6 countries
are fairly small and uninteresting, covering only 0.27% of total world
population, 0.04% of total world GDP, and 0.03% of total flow values in
Thus, what I would like to do is to remove these 6 countries from my
dataset and repeat the analysis with only 93 countries. When doing so, I
get an optimal number of positions at 8, the two smallest of these
positions containing 3 and 4 countries respectively. I find this 1) much
easier to analyze, 2) much easier to visualize (as a reduced/image
graph), 3) giving a higher resolution (more partitions) regarding the
positions containing the bulk of countries, and 4) removing countries
that I feel could "disturb" the REGE algorithm in finding the major
positions, removing countries that though might be unique but not very
significant with respect to their coverage (as given by share of total
flow values and attributional measures such as population and GDP).
However: how on earth can I motivate this? Can I just simply argue that
"well, first I included these 6 countries, but as these countries
resultet in 4 unique positions containing only these countries, I chose
to remove these countries from the dataset and try without them - they
are so small and insignificant anyhow..."? I could probably find some
criteria for removing these based on their attributes, net degrees or
similar, but that would not be very scientifically honest now, would it?
How have other people done in analyses that yields a bunch of trivial
and singleton positions, i.e. positions that only contain 1-2 actors
that are of fairly minor importance anyway? Suggestions?
(And sorry for using this email list as a classroom here - I have
nowhere else to turn to...)
Carl Nordlund, BA, PhD student
Human Ecology Division, Lund university
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