The systematic errors that are induced by a combination of human memory limitations and common survey design and implementation have long been studied in the context of egocentric networks. Despite this, little if any work exists in the area of random error analysis on these same networks; this paper offers a perspective on the effects of random errors on egonet analysis, as well as the effects of using egonet measures as independent predictors in linear models. We explore the effects of false-positive and false-negative error in egocentric networks on both standard network measures and on linear models through simulation analysis on a ground truth egocentric network sample based on facebook-friendships. Results show that 5-20% error rates, which are consistent with error rates known to occur in ego network data, can cause serious misestimation of network properties and regression parameters.
Bibliographical noteFunding Information:
The author would like to thank both the anonymous reviewers, and Carter T. Butts and Mark S. Handcock for their kind suggestions and comments. Last, the author would like thank his dad for all his help and support over the years. This work was supported in part by ONR award N00014-08-1-1015, NSF Grant OIA-1028394 and NIH/NICHD grant 1R01HD068395-01.
- Egocentric network
- Network error