Snowball Sampling and Sample Selection in a Social Network
ISBN: 978-1-83867-576-9, eISBN: 978-1-83867-575-2
Publication date: 19 October 2020
Abstract
This chapter studies a snowball sampling method for social networks with endogenous peer selection. Snowball sampling is a sampling design which preserves the dependence structure of the network. It sequentially collects the information of vertices linked to the vertices collected in the previous iteration. The snowball samples suffer from a sample selection problem because of the endogenous peer selection. The author proposes a new estimation method that uses the relationship between samples in different iterations to correct selection. The author uses the snowball samples collected from Facebook to estimate the proportion of users who support the Umbrella Movement in Hong Kong.
Keywords
Acknowledgements
Acknowledgments
I am grateful to my main advisor Iván Fernández-Val for his guidance and patience. I appreciate the helpful feedback from Pierre Perron, M. Daniele Paserman, Hiroaki Kaido, Ho-Po Crystal Wong, Tak-Yuen Wong, Vladimir Yankov, and seminar participants in Econometrics seminar in Boston University. I also thank the anonymous reviewers for their insightful comments and suggestions. The views expressed here are solely the author’s and do not represent the views of Bates White, or their other employees. All errors are mine.
Citation
Chan, J.T. (2020), "Snowball Sampling and Sample Selection in a Social Network", de Paula, Á., Tamer, E. and Voia, M.-C. (Ed.) The Econometrics of Networks (Advances in Econometrics, Vol. 42), Emerald Publishing Limited, Leeds, pp. 61-80. https://doi.org/10.1108/S0731-905320200000042008
Publisher
:Emerald Publishing Limited
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