Investigating the variation of personal network size under unknown error conditions
Investigating the variation of personal network size under unknown error conditions
This article estimates the variation in personal network size, using respondent data containing two systematic sources of error. The data are the proportion of respondents who, on average, claim to know zero, one, and two people in various subpopulations, such as "people who are widows under the age of 65" or "people who are diabetics". The two kinds of error—transmission error (respondents are unaware that someone in their network is in a subpopulation) and barrier error (something causes a respondent to know more or less than would be expected, in a subpopulation) — are hard to quantify. The authors show how to estimate the shape of the probability density function (pdf) of the number of people known to a random individual by assuming that respondents give what they assume to be accurate responses based on incorrect knowledge. It is then possible to estimate the relative effective sizes of subpopulations and produce an internally consistent theory. These effective sizes permit an evaluation of the shape of the pdf, which, remarkably, agrees with earlier estimates.
social networks, errors, probability density function
84-112
Killworth, Peter D.
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McCarty, Christopher
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Johnsen, Eugene C.
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Bernard, H. Russell
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Shelley, Gene A.
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2006
Killworth, Peter D.
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McCarty, Christopher
0c3e32ea-59b0-437a-aa28-3cec09260655
Johnsen, Eugene C.
b5a7ccfa-d568-463c-8f71-a088028dd434
Bernard, H. Russell
ed4dc9b7-7ca7-46d8-85c7-1c82ea9e49ce
Shelley, Gene A.
d0fb3572-3623-4f94-bc25-02b22e8c40f8
Killworth, Peter D., McCarty, Christopher, Johnsen, Eugene C., Bernard, H. Russell and Shelley, Gene A.
(2006)
Investigating the variation of personal network size under unknown error conditions.
Sociological Methods and Research, 35 (1), .
(doi:10.1177/0049124106289160).
Abstract
This article estimates the variation in personal network size, using respondent data containing two systematic sources of error. The data are the proportion of respondents who, on average, claim to know zero, one, and two people in various subpopulations, such as "people who are widows under the age of 65" or "people who are diabetics". The two kinds of error—transmission error (respondents are unaware that someone in their network is in a subpopulation) and barrier error (something causes a respondent to know more or less than would be expected, in a subpopulation) — are hard to quantify. The authors show how to estimate the shape of the probability density function (pdf) of the number of people known to a random individual by assuming that respondents give what they assume to be accurate responses based on incorrect knowledge. It is then possible to estimate the relative effective sizes of subpopulations and produce an internally consistent theory. These effective sizes permit an evaluation of the shape of the pdf, which, remarkably, agrees with earlier estimates.
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Published date: 2006
Keywords:
social networks, errors, probability density function
Organisations:
National Oceanography Centre
Identifiers
Local EPrints ID: 345886
URI: http://eprints.soton.ac.uk/id/eprint/345886
ISSN: 0049-1241
PURE UUID: 0656c68e-880b-49d4-b0c9-bfc37c33ed43
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Date deposited: 04 Dec 2012 14:58
Last modified: 14 Mar 2024 12:30
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Contributors
Author:
Peter D. Killworth
Author:
Christopher McCarty
Author:
Eugene C. Johnsen
Author:
H. Russell Bernard
Author:
Gene A. Shelley
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