Cohesive subgroups, core-periphery and hierarchical networks are studied.
•
Seven actor non-response treatments are developed and applied to valued networks.
•
The known blockmodel and the blockmodels for the treated networks are compared.
•
Ignoring the problem of non-respondents is completely inappropriate.
•
The median of k-nearest neighbours based on incoming ties performs the best.
Abstract
Social
network data usually contain different types of errors. One of them is
missing data due to actor non-response. This can seriously jeopardize
the results of analyses if not appropriately treated. The impact of
missing data may be more severe in valued networks where not only the
presence of a tie is recorded, but also its magnitude or strength.
Blockmodeling is a technique for delineating network structure. We focus
on an indirect approach suitable for valued networks. Little is known
about the sensitivity of valued networks to different types of
measurement errors. As it is reasonable to expect that blockmodeling,
with its positional outcomes, could be vulnerable to the presence of
non-respondents, such errors require treatment. We examine the impacts
of seven actor non-response treatments on the positions obtained when
indirect blockmodeling is used. The start point for our simulation are
networks whose structure is known. Three structures were considered:
cohesive subgroups, core-periphery, and hierarchy. The results show that
the number of non-respondents, the type of underlying blockmodel
structure, and the employed treatment all have an impact on the
determined partitions of actors in complex ways. Recommendations for
best practices are provided.
