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Below are a selection of papers describing some of the work we have done
here in Melbourne on network models using snowball sampled data, including
several empirical applications. We have an ongoing project for a more
systematic study of inferential validity for Autologistic Actor Attribute
Models using snowball samples, but more to come on that in the future.



Garry





*Professor Garry Robins, FASSA,*

*Melbourne School of Psychological Sciences*

*University of Melbourne*

*Victoria 3010*

*Australia*

*Adjunct Professor, Swinburne University, Melbourne, Australia.*

Personal website: https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_site_garryrobins_&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=2WJXS_qf_AviNMJgevTnzU9wvTxJT4EBdDXtwKCFshA&s=l0P27s7AEq1mMpGrdV28oyVyHLbDLYuOnOtPVVOw5l8&e=
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Melnet website:  https://urldefense.proofpoint.com/v2/url?u=http-3A__www.melnet.org.au&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=2WJXS_qf_AviNMJgevTnzU9wvTxJT4EBdDXtwKCFshA&s=vxSJgiWCr48hh9rVm9ZQrc_VuuGcxQzlH1JpbcfIi8M&e=
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Check out my book on Social Network Research. *Doing Social Network
Research*: *Network-based research design for social scientists.*Sage books





Bryant, R. A., Gallagher, H. C., Gibbs, L., Pattison, P., MacDougall, C.,
Harms, L., ... & Richardson, J. (2017). Mental health and social networks
after disaster. *American Journal of Psychiatry*, *174*(3), 277-285.

Daraganova, G. & Pattison, P (2013). Autologistic actor attribute model
analysis of unemployment: dual importance of who you know and where you
live. In Lusher, D, Koskinen, J., & Robins, G. (Eds.) *Exponential random
graph models for social networks* (pp. 237-247). New York, NY: Cambridge.

Kashima, Y., Wilson, S., Lusher, D., Pearson, L. J., & Pearson, C. (2013).
The acquisition of perceived descriptive norms as social category learning
in social networks. *Social Networks*, *35*(4), 711-719.

Pattison, P. E., Robins, G. L., Snijders, T. A., & Wang, P. (2013).
Conditional estimation of exponential random graph models from snowball
sampling designs*. Journal of Mathematical Psychology*, *57*(6), 284-296.

Rolls, D., Wang, P., Jenkinson, R., Pattison, P., Robins, G., Sacks-Davis,
R., Daraganova, G., Hellard, M., & McBryde, E. (2013). Modelling a
disease-relevant contact network of people who inject drugs. *Social
Networks, 35, *699-710.

Rolls, D. A., & Robins, G. (2017). Minimum distance estimators of
population size from snowball samples using conditional estimation and
scaling of exponential random graph models. *Computational Statistics &
Data Analysis*, *116*, 32-48.

Stivala, A. D., Koskinen, J. H., Rolls, D. A., Wang, P., & Robins, G. L.
(2016). Snowball sampling for estimating exponential random graph models
for large networks. *Social Networks*, *47*, 167-188.







---------- Forwarded message ---------
From: *Greg Doyle* <[log in to unmask]>
Date: Wed, 1 May 2019 at 17:37
Subject: [SOCNET] Empirical research using snowball sampling to examine
power and influence
To: <[log in to unmask]>


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I am looking for sociology-related papers that describe empirical projects
examining influence or power in real-world human social networks
constructed through snowball sampling from multiple random seed nodes.

I am particularly interested in papers that discuss the implications of
missing data, the validity of centrality measures, and boundary
specification for extrapolating beyond the sample to the actual social
context it represents.

I'm trying to understand the problems that arise when making the case that
a snowball-sampled network is a reasonable proxy for a real social world -
especially around the issue of how large a sample needs to be relative the
estimate size of the total social world in question and how many seed nodes
are required. For example, if one wanted to find the most central doctor
out of all the doctors in a town is it possible to sample less than 100 %
of all the doctors and if so how to estimate the minimum % required and the
minumum number of start points.

Thanks very much

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