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Learning about polarization Using "How many X's do you know" surveys |
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Abstract:
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Networks--sets of objects connected by relationships--are
important in a number of fields. The study of networks has long
been central to sociology, where researchers have attempted to
understand the causes and consequences of the structure of
relationships in large groups of people. Using insight from
previous network research, Killworth et al. (1998a,b) and McCarty
et al. (2001) developed and evaluated a method for estimating the
sizes of hard-to-count populations using network data collected
from a simple random sample of Americans. In this paper we show
how, using a multilevel overdispersed Poisson regression model,
these data can also be used to estimate aspects of social
structure in the population. Our work goes beyond most previous
research by using variation as well as average responses to learn
about social networks and leads to some interesting results. We
apply our method to the McCarty et al.\ data and find that
Americans vary greatly in their number of acquaintances. Further,
Americans show great variation in propensity to form ties to
people in some groups (e.g., males in prison, the homeless, and
American Indians), but little variation for other groups (e.g.,
people named Michael or Nicole). We also explore other features of
these data and consider ways in which survey data can be used to
estimate network structure. |
Most Common Document Word Stems:
data (145), group (138), model (136), k (134), estim (116), 1 (107), network (98), know (94), overdispers (88), social (83), distribut (67), use (59), person (58), di (57), peopl (55), popul (55), 2 (54), name (53), paramet (51), 0 (50), e (49), |
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Association:
Name: American Political Science Association URL: http://www.apsanet.org
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Citation:
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MLA Citation:
| Gelman, Andrew. "Learning about polarization Using "How many X's do you know" surveys" Paper presented at the annual meeting of the American Political Science Association, Marriott Wardman Park, Omni Shoreham, Washington Hilton, Washington, DC, Sep 01, 2005 <Not Available>. 2011-03-14 <http://www.allacademic.com/meta/p40210_index.html> |
APA Citation:
| Gelman, A. , 2005-09-01 "Learning about polarization Using "How many X's do you know" surveys" Paper presented at the annual meeting of the American Political Science Association, Marriott Wardman Park, Omni Shoreham, Washington Hilton, Washington, DC Online <APPLICATION/PDF>. 2011-03-14 from http://www.allacademic.com/meta/p40210_index.html |
Publication Type: Conference Paper/Unpublished Manuscript Review Method: Peer Reviewed Abstract: Networks--sets of objects connected by relationships--are
important in a number of fields. The study of networks has long
been central to sociology, where researchers have attempted to
understand the causes and consequences of the structure of
relationships in large groups of people. Using insight from
previous network research, Killworth et al. (1998a,b) and McCarty
et al. (2001) developed and evaluated a method for estimating the
sizes of hard-to-count populations using network data collected
from a simple random sample of Americans. In this paper we show
how, using a multilevel overdispersed Poisson regression model,
these data can also be used to estimate aspects of social
structure in the population. Our work goes beyond most previous
research by using variation as well as average responses to learn
about social networks and leads to some interesting results. We
apply our method to the McCarty et al.\ data and find that
Americans vary greatly in their number of acquaintances. Further,
Americans show great variation in propensity to form ties to
people in some groups (e.g., males in prison, the homeless, and
American Indians), but little variation for other groups (e.g.,
people named Michael or Nicole). We also explore other features of
these data and consider ways in which survey data can be used to
estimate network structure. |
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| Document Type: |
application/pdf |
| Page count: |
28 |
| Word count: |
13087 |
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| “How many people do you know in prison?”: Using overdispersion in count data to estimate social structure in networks∗ Tian Zheng† Matthew J. Salganik‡ Andrew Gelman§ June 13 2005 Abstract Networks—sets of objects connected by relationships—are important in a number of fields. The study of networks has long been central to sociology where researchers have attempted to understand the causes and consequences of the structure of relationships in large groups of people. Using insight from previous network research Killworth |
| Networks 27(1) 1-29. Van Dyk D. A. and Meng X. L. (2001). The art of data augmentation (with discussion). Journal of Computational and Graphical Statistics 10 1–111. Wasserman S. and Faust K. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press. 27 Watts D. J. (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences USA 99 5766–5771. Watts D. J. Dodds P. S. and Newman M. E. J. (2002). |
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