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Eric,

It would be great if you could share anything that you have put together on
this approach. Alexandra Marin and I have a paper coming out in Field
Methods that explores what sounds like a similar approach based on data
collected on personal support networks (special issue on personal networks
due out in the next issue or two). I have attached the title and abstract.
If you or anyone else is interested in a draft copy, please email Ali
([log in to unmask]) or myself.

Simplifying the Personal Network Name Generator: Alternatives to Traditional
Multiple and Single Name Generators

For researchers interested in the study of personal networks, measures of
network composition are often obtained through the use of name generators
and name interpreters. However, the cost of administering a survey with
multiple name generators, in terms of time and respondent motivation, is
often prohibitive. Researchers seeking to minimize respondent burden
routinely turn to time saving measures, such as the use of a single name
generator (i.e. the "important matters" generator used in the General Social
Survey (GSS)). We argue that the limitations of this approach are often
understated. In the study of social support, multiple name generators are
required to ensure that researchers sample from the full definition of
support. Putting aside issues of construct validity, we compared measures of
network composition and structure obtained from stand alone generators to
measures obtained from a six-item multiple name generator. We found that
although some single generators provided passable estimates for some
measures, all single generators failed to provide reliable estimates across
a broad spectrum of network measures, including key variables such as size
and density. In an attempt to improve the reliability of network measures,
beyond what could be obtained through single generator alternatives, and
while still reducing respondent burden, we evaluated two alternative
methods; 1) the MMG, the two most robust name generators from our first
analysis and a full set of name interpreters, and 2) the MGRI, a series of
multiple name generators with name interpreters administered to a random
subset of alters. In comparison to single name generators, both the MMG and
the MGRI provided measures that were more strongly correlated with the full
name generator model. In addition, the MGRI maintained the validity of the
full generator approach, provided a perfect measure of network size.

-Keith

----------------------------------------
Keith N. Hampton
Assistant Professor
Annenberg School for Communication
University of Pennsylvania
3620 Walnut Street
Philadelphia, PA 19104 
www.mysocialnetwork.net 


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Date:    Wed, 4 Apr 2007 11:00:04 -0400
From:    Eric Jones ECJONES4 <[log in to unmask]>
Subject: random sampling of personal networks

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Hi. For those of you working on personal networks, using EgoNet or thinking
of using it, we worked with software author MdLogix recently to update the
software and add a randomization feature that will allow the program to
randomly sample the list of alters originally solicited. 

Research shows that personal network structural measures remain similar for
randomly generated subsamples (with a minimum n, depending on which measure,
as few as 12 for some, as many as 25 for others) of the orginal personal
network (of approximately 45 individuals). 

This feature will decrease informant burden by more than half when using the
program to elicit networks, especially if you're interested in personal
network structure and not needing all of the ties. Unfortunately, I don't
think it will make it any easier to conduct methodological research on the
effect of sampling on personal networks, because once you've collected
reduced amounts of data, you don't have the data for each individual in each
personal network from which to sample--you've only got the already sampled
(and smaller) network.

Regards,
Eric

Eric C. Jones, Ph.D.
Department of Anthropology
University of North Carolina at Greensboro
426 Graham Building
Greensboro NC 27412-5001
office: 336-334-5132
[log in to unmask]

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