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Sensitivity analysis for network observations with applications to inferences of social influence effects

Published online by Cambridge University Press:  19 October 2020

Ran Xu Open the ORCID record for Ran Xu [Opens in a new window]  and
Kenneth A. Frank
Ran Xu*
Affiliation:
Department of Allied Health Sciences, University of Connecticut, Storrs, CT 06269, USA
Kenneth A. Frank
Affiliation:
Department of Counseling, Educational Psychology and Special Education, Michigan State University, East Lansing, MI 48824, USA (email:kenfrank@msu.edu)
*
*Corresponding author. Email: ran.2.xu@uconn.edu
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Abstract

The validity of network observations is sometimes of concern in empirical studies, since observed networks are prone to error and may not represent the population of interest. This lack of validity is not just a result of random measurement error, but often due to systematic bias that can lead to the misinterpretation of actors’ preferences of network selections. These issues in network observations could bias the estimation of common network models (such as those pertaining to influence and selection) and lead to erroneous statistical inferences. In this study, we proposed a simulation-based sensitivity analysis method that can evaluate the robustness of inferences made in social network analysis to six forms of selection mechanisms that can cause biases in network observations—random, homophily, anti-homophily, transitivity, reciprocity, and preferential attachment. We then applied this sensitivity analysis to test the robustness of inferences for social influence effects, and we derived two sets of analytical solutions that can account for biases in network observations due to random, homophily, and anti-homophily selection.

Keywords

sensitivity analysisnetwork observationsselection mechanismssocial influence
Type
Research Article
Information
Network Science , Volume 9 , Issue 1 , March 2021 , pp. 73 - 98
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

Action Editor: Stanley Wasserman

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