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SOCNET Members,

Can any of you provide me with a few good papers on handling missing data
in network studies?  Specifically, if I'm studying collaboration and some
subject opts out of the research, or if I'm conducting weekly surveys and
some respondents fail to complete a survey for a given week or we choose to
skip certain respondents in certain weeks to reduce burden on the subject.
I'm also looking for a possible power analysis for these research
decisions/situations.

I am consulting on a project investigating how to improve collaboration and
innovation by altering office space layout, building off a paper I
published with Kerstin Sailer in the Journal of Social Networks.
http://www.sciencedirect.com/science/journal/03788733/34/1

For the project, subjects will be evaluated in their current office
configuration, then moved to an open plan, designed for greater
collaboration.  There are several sources of network data that I will
collect.  I will have billing data, which ties subjects to projects that
they work on (co-project network).  I will have access to email meta-data
(email network).  And I will be able to survey subjects on their
interactions.

It does not appear that I will be able to survey all respondents each
week.  The project lead has asked me about power analysis to quantify the
effect of missing respondents.  In other words, how many people do I need
to survey to have useful network data.

I have explained ergodicity to my research team and how inference can be a
problem, given the dependent nature of networks.  I don't really need any
more info on that angle.

What I would appreciate is any papers that discuss the statistical effect
of missing data in an ergm, stochastic actor-oriented model, or basic SNA.

As I recall, similar questions have been raised on this forum before.  I'm
sorry I couldn't locate those posts.

I'd also be interested in any papers on similar studies focused on
increasing collaboration and innovation through office space design.  I'm
already familiar with Kerstin Sailer's excellent work in this area, but
would be interested in other efforts.

Thanks.

Ian

Ian McCulloh, Ph.D.
Johns Hopkins University

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