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

Joeri Hofmans () and Etienne Mullet

Quality & Quantity: International Journal of Methodology, 2013, vol. 47, issue 1, 455-464

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. Copyright Springer Science+Business Media B.V. 2013

Keywords: Judgment; Information Integration Theory; Functional measurement; Individual differences; Cluster analysis; K-means (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:47:y:2013:i:1:p:455-464

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DOI: 10.1007/s11135-011-9529-7

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