Statistical modelling of congruence and association between perceptual and complete networks
Koehly, Laura Marie
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Permalink
https://hdl.handle.net/2142/20138
Description
Title
Statistical modelling of congruence and association between perceptual and complete networks
Author(s)
Koehly, Laura Marie
Issue Date
1996
Doctoral Committee Chair(s)
Wasserman, Stanley
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Psychology, Social
Statistics
Psychology, Psychometrics
Psychology, Cognitive
Language
eng
Abstract
"Structural analysis of social networks using statistical techniques has been evolving into sophisticated models for the last 60 years. These statistical models and techniques have been predominantly concerned with the structure of a global network. Statistical approaches to the analysis of cognitive networks, either full cognitive social structures or ego-centered networks, are few. The statistical analysis of cognitive. social structures has been limited to evaluating some common, or average, network from the set of perceptual networks. The premise of these approaches is to find a common network which reflects the ""cultural"" consensus. Then, upon deriving the cultural consensus network, differences between the individual perceptions and the common network are evaluated and systematic patterns in the perceptual bias are explained. These consensus approaches to perceptual networks have not attempted to describe the structure within each perceptual network or the structure of the consensus network."
This thesis defines a set of statistical models designed for a set of interrelated perceptual networks, either complete perceptual networks or ego-centered networks. Two types of models are presented. The structural models can be used to describe the structure within each perceptual network, the structure between the set of perceptual networks, and the association between each perceptual network and the global structure, or some other reference network. The congruence models provide a stochastic framework for evaluating the overall congruence and actor specific congruence between the perceptual networks and some reference structure.
This set of congruence and global association models for cognitive networks provides us with a wealth of modelling tools. We can use them to examine the interdependencies within a single cognitive structure or to explain the structure within and/or between a set of cognitive networks. We can explore the effects of assuming that the perceivers' perceptions of network structure are dependent or independent. The congruence models allow us to investigate relationships between perceptual bias and the perceiver's role in the global structure. The ego-centered congruence models allow us to ask whether individuals can accurately reconstruct relationships within their world; with the cognitive social structure models, we can ask whether these actors can accurately reconstruct the outside world, too. The statistical theory, model specification and substantive applications of the models are presented here.
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