Towards unveiling individual differences in different stages of information processing: a clustering-based approach

Towards unveiling individual differences in different stages of information processing: a... Information Integration Theory (IIT) is a theory of judgment in daily life. Its principal aim is to study the cognitive rules that people use to integrate information when they make a judgment. Traditionally, the identification of individual differences in these qualitatively different integration rules requires individual designs. It also requires the grouping of individuals according to their integration rule, which can be a challenging task, particularly when the data are noisy or when the pattern involves many factors. This paper builds on the cluster analysis tradition for developing a series of clustering procedures that can be implemented for studying, not only individual differences in integration rules, but also individual differences in other stages of information processing. These procedures are intended to simplify the identification of differences in (a) the subjective valuation of information, (b) the integration of the subjective values, and (c) general attitudes before judging. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Towards unveiling individual differences in different stages of information processing: a clustering-based approach

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Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media B.V.
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-011-9529-7
Publisher site
See Article on Publisher Site

Abstract

Information Integration Theory (IIT) is a theory of judgment in daily life. Its principal aim is to study the cognitive rules that people use to integrate information when they make a judgment. Traditionally, the identification of individual differences in these qualitatively different integration rules requires individual designs. It also requires the grouping of individuals according to their integration rule, which can be a challenging task, particularly when the data are noisy or when the pattern involves many factors. This paper builds on the cluster analysis tradition for developing a series of clustering procedures that can be implemented for studying, not only individual differences in integration rules, but also individual differences in other stages of information processing. These procedures are intended to simplify the identification of differences in (a) the subjective valuation of information, (b) the integration of the subjective values, and (c) general attitudes before judging.

Journal

Quality & QuantitySpringer Journals

Published: Jun 30, 2011

References

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