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

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  • David Bimler

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

Suggested Citation

  • David Bimler, 2013. "Two applications of the Points-of-View model to subject variations in sorting data," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 775-790, February.
  • Handle: RePEc:spr:qualqt:v:47:y:2013:i:2:p:775-790
    DOI: 10.1007/s11135-011-9552-8
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    References listed on IDEAS

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    1. John Ross, 1966. "A remark on tucker and Messick's “points of view” analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(1), pages 27-31, March.
    2. Anthony Coxon & Charles Jones, 1974. "Occupational similarities: Subjective aspects of social stratification," Quality & Quantity: International Journal of Methodology, Springer, vol. 8(2), pages 139-158, June.
    3. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    4. Ledyard Tucker & Samuel Messick, 1963. "An individual differences model for multidimensional scaling," Psychometrika, Springer;The Psychometric Society, vol. 28(4), pages 333-367, December.
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    Cited by:

    1. Zita Oravecz & Royce Anders & William Batchelder, 2015. "Hierarchical Bayesian Modeling for Test Theory Without an Answer Key," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 341-364, June.

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