Abstract
This paper introduces a new method for acquiring and interpreting data on cognitive (or perceptual) networks. The proposed method involves the collection of multiple reports on randomly chosen pairs of individuals, and statistical means for aggregating these reports into data of conventional sociometric form. We refer to the method as “perceptual tomography” to emphasize that it aggregates multiple 3rd-party data on the perceived presence or absence of individual properties and pairwise relationships. Key features of the method include its low respondent burden, flexible interpretation, as well as its ability to find “robust intransitive” ties in the form of perceived non-edges. This latter feature, in turn, allows for the application of conventional balance clustering routines to perceptual tomography data. In what follows, we will describe both the method and an example of the implementation of the method from a recent community study among Alaska Natives. Interview data from 170 community residents is used to ascribe 4446 perceived relationships (2146 perceived edges, 2300 perceived non-edges) among 393 community members, and to assert the perceived presence (or absence) of 16 community-oriented helping behaviors to each individual in the community. Using balance theory-based partitioning of the perceptual network, we show that people in the community perceive distinct helping roles as structural associations among community members. The fact that role classes can be detected in network renderings of “tomographic” perceptual information lends support to the suggestion that this method is capable of producing meaningful new kinds of data about perceptual networks.
Highlights
The way that people are classified into relational groups by knowledgeable outsiders has its own reality, a reality that says as much about where these outsiders perceive social fault lines to lie as it does the presence or absence of particular relationships [1]
In this article we present a strategy to recover some of the “heuristics” [2] people implicitly use to classify the relationships of others in their community via perceptual tomography—multiple reports on the presence or absence of social ties between randomly selected pairs of actors in a community
Ego network research provides a number of sampling options [22], and examples of large scale nationally-representative ego network data collection include the General Social Survey (GSS) [23, 24] and the National Longitudinal Study of Adolescent to Adult (Add Health) [25, 26]
Summary
The way that people are classified into relational groups by knowledgeable outsiders has its own reality, a reality that says as much about where these outsiders perceive social fault lines to lie as it does the presence or absence of particular relationships [1]. Similar issues are raised when we seek to determine perceptions of individual attributes via similar third party reporting In both situations, inferring perceived pairwise relationships (or lack thereof) and perceived individual attributes (or lack thereof) with a rigorous sense of confidence, requires very different approaches than those that rely on self-reports. These methods below yield data in conventional sociometric format, while accounting for differing numbers and discordant reports across pairs and individuals (a result of the random selection of photographs shown to a respondent) They offer means for raising or lowering the confidence threshold used to determine the presence or absence of a perceived tie, or a perceived individual attribute. The definitive absence of a perceived tie allows for balance clustering approaches in places where block modeling or other common equivalence approaches are less definitive
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