Networks

This article raises some questions about the use of the word "know." What exactly do we get when we generate ties in this way? Is knowing itself multiplex? And what is the measurable error which we can expect when knowing is used to define network ties?

In what follows, the researcher summarizes three studies that shed some light on this question. The first is an in-depth descriptive study that focused on defining "differences in knowing" of people. In the other two studies, these differences were tested with representative samples.

In this paper we introduce and evaluate a method for eliciting a representative sample of total personal networks. First names were used as a cue to elicit a sample of 14 alters from 712 respondents through a telephone interview. Network characteristics for each respondent were calculated as averages and proportions across the 14 alters. These were compared to other studies using more specialized network generators.

In this paper we compare two methods for estimating the size of personal networks using a national representative sample of the United States.

Most personal (egocentric) network studies describe networks using measures that are not structural, opting instead for attribute-based analyses that summarize the relationships of the respondent to network members. Those researchers that have used structural measures have done so on networks of less than 10 members who represent the network core. Although much has been learned by focusing on attribute-based analyses of personal network data, the application of structural analyses that are traditionally used on whole (sociocentric) network data may prove fruitful.

Social network analysis (SNA) is both a theoretical perspective and a set of methods. In terms of theory, SNA extends and complements traditional social science by focusing on the causes and consequences of relations between people and among sets of people rather than on the features of individuals. In terms of method, SNA focuses on the measurement of relationships between people. By quantifying the relationships between people, network analysts can apply models and techniques that are commonly used across the social and natural sciences.

Several algorithms and software packages have been developed for displaying the relationship between actors within a whole (sociocentric) network. These visualization packages use as input an adjacency matrix representing the relationship between actors, and have occasionally been applied to personal (egocentric) network data. Personal network adjacency matrices require respondents to report on all alter-alter ties. This is an enormous respondent burden when the number of alters goes much beyond 30.

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