  Cognitive Social Structure (CSS) network studies collect relational data on
respondents' direct ties and their perception of ties among all other
individuals in the network. When reporting their perception networks,
respondents commit two types of errors, namely, omission (false negatives) and
commission (false positives) errors. We first assess the relationship between
these two error types, and their contributions on the overall respondent
accuracy. Next we propose a method for estimating networks based on perceptions
of a random sample of respondents from a bounded social network, which utilizes
the Receiving Operator Characteristic (ROC) curve for balancing the tradeoffs
between omission and commission errors. A comparative numerical study shows
that the proposed estimation method performs well. This new method can be
easily integrated to organization studies that use randomized surveys to study
multiple organizations. The burgeoning field of multilevel analysis of
inter-organizational networks can also immensely benefit from this approach.

Computation;Applications