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Two applications of the Points-of-View model to subject variations in sorting data

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Abstract

In many of the social sciences it is useful to explore the “working models” or mental schemata that people use to organise items from some cognitive or perceptual domain. With an increasing number of items, versions of the Method of Sorting become important techniques for collecting data about inter-item similarities. Because people do not necessarily all bring the same mental model to the items, there is also the prospect that sorting data can identify a range within the population of interest, or even distinct subgroups. Anthropology provides one tool for this purpose in the form of Cultural Consensus Analysis (CCA). CCA itself proves to be a special case of the “Points of View” approach. Here factor analysis is applied to the subjects’ method-of-sorting responses, obtaining idealized or prototypal modes of organising the items—the “viewpoints”. These idealised modes account for each subject’s data by combining them in proportions given by the subject’s factor loadings. The separate organisation represented by each viewpoint can be made explicit with clustering or multidimensional scaling. The technique is illustrated with job-sorting data from occupational research, and social-network data from primate behaviour.

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Abbreviations

CCA:

Cultural consensus analysis

MDS:

Multidimensional scaling

FA:

Factor analysis

HCA:

Hierarchical clustering analysis

PCA:

Principal components analysis

PoV:

Points-of-View

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  1. School of Arts, Development and Health Education, Massey University, Private Bag 11-222, Palmerston North, New Zealand

    David Bimler

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  1. David Bimler
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Bimler, D. Two applications of the Points-of-View model to subject variations in sorting data. Qual Quant 47, 775–790 (2013). https://doi.org/10.1007/s11135-011-9552-8

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