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Experimental Methods for Measuring Social Networks without Censoring

Year: 2023
Type: article
Abstract: We elicit social networks among students in an Italian high school either by measuring the complete network in an incentive-compatible way or by using a truncated elicitation of at most five links. We find that truncation undercounts weak links by up to 90% but only moderately undercounts the time spent with strong friends. We use simulations to demonstrate that the measurement error induced by censoring might be particularly significant when studying phenomena such as social learning which are often thought to operate along weak ties. We then discuss how a modified network elicitation protocol might be able to reduce measurement error.
DOI: 10.1017/xps.2023.23
Institutions: New York University Abu Dhabi, New York University, Cornell University, Bocconi University, University of Siena, University of California, Los Angeles, University of Michigan
Language: English
Subfield: Safety Research
Open Access status: hybrid
Estimated APC paid: $3,255
OpenAlex ID: W4386833673
FWCI: 0.242
Cites: 29
Cited by: 1
Related to: 10