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Reading: A Nonmetric Algorithm for Analyzing Preference Data According to the Unfolding Model

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Research Article

A Nonmetric Algorithm for Analyzing Preference Data According to the Unfolding Model

Authors:

Gerry Evers-Kiebooms ,

Department of Psychology University of Leuven Tiensestraat 102 3000 Leuven, BE
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Luc Delbeke

Department of Psychology University of Leuven Tiensestraat 102 3000 Leuven, BE
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Abstract

An algorithm for nonmetric internal unfolding analysis of a preference matrix is presented. It is based on the absolute value principle and intends to achieve a maximal mean rank-correlation between the rows of the data matrix and the corresponding rows of the distance matrix computed from the geometric representation. Using simulated data and a real data example, namely preferences for family compositions, the algorithm is compared with MINIRSA, an algorithm for unfolding based on the transformational principle.

How to Cite: Evers-Kiebooms, G. and Delbeke, L., 1982. A Nonmetric Algorithm for Analyzing Preference Data According to the Unfolding Model. Psychologica Belgica, 22(2), pp.99–119. DOI: http://doi.org/10.5334/pb.692
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Published on 01 Jan 1982.
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