Omission and Commission Errors in Network Cognition and Network Estimation using ROC Curve

TL;DR

This paper examines errors in perceived social networks, analyzes their impact on accuracy, and introduces an ROC curve-based method for improved network estimation from sampled perceptions.

Contribution

It proposes a novel ROC curve-based approach for estimating networks from sampled perceptions, balancing omission and commission errors.

Findings

The method performs well in numerical comparisons.

It effectively balances false positives and negatives.

Applicable to organizational and inter-organizational network studies.

Abstract

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.