Discerning
the essential structure of social networks is a major task. Yet, social
network data usually contain different types of errors, including
missing data that can wreak havoc during data analyses. Blockmodeling is
one technique for delineating network structure. While we know little
about its vulnerability to missing data problems, it is reasonable to
expect that it is vulnerable given its positional nature. We focus on
actor non-response and treatments for this. We examine their impacts on
blockmodeling results using simulated and real networks. A set of
‘known’ networks are used, errors due to actor non-response are
introduced and are then treated in different ways. Blockmodels are
fitted to these treated networks and compared to those for the known
networks. The outcome indicators are the correspondence of both position
memberships and identified blockmodel structures. Both the amount and
type of non-response, and considered treatments, have an impact on
delineated blockmodel structures.
Highlights
►
Blockmodeling results can be affected by both actor non-response and
treatments of non-response in complex ways. ► There is no one best
treatment of actor non-response. ► The symmetry of a network has a
dramatic but complex impacts on determining best treatments for actor
non-response. ► Overall, blockmodeling results based on structural
equivalence is very stable.
