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Psychometrika

, Volume 69, Issue 4, pp 513–545 | Cite as

Geometric representation of association between categories

  • Willem J. HeiserEmail author
2004 Presidential Address

Abstract

Categories can be counted, rated, or ranked, but they cannot be measured. Likewise, persons or individuals can be counted, rated, or ranked, but they cannot be measured either. Nevertheless, psychology has realized early on that it can take an indirect road to measurement: What can be measured is the strength of association between categories in samples or populations, and what can be quantitatively compared are counts, ratings, or rankings made under different circumstances, or originating from different persons. The strong demand for quantitative analysis of categorical data has thus created a variety of statistical methods, with substantial contributions from psychometrics and sociometrics. What is the common basis of these methods dealing with categories? The basic element they share is that the sample space has a special geometry, in which categories (or persons) are point masses forming a simplex, while distributions of counts or profiles of ratings are centers of gravity, which are also point masses. Rankings form a discrete subset in the interior of the simplex, known as the permutation polytope, and paired comparisons form another subset on the edges of the simplex. Distances between point masses form the basic tool of analysis. The paper gives some history of major concepts, which naturally leads to a new concept: the shadow point. It is then shown how loglinear models, Luce and Rasch models, unfolding models, correspondence analysis and homogeneity analysis, forced classification and classification trees, as well as other models and methods, fit into this particular geometrical framework.

Key words

Categorical data simplex triangular plot paired comparisons rank orders permutation polytope center of gravity BTL model Rasch model inertia association model variation multidimensional unfolding biplot multinomial response model loglinear model forced classification classification tree 

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© The Psychometric Society 2004

Authors and Affiliations

  1. 1.Department of PsychologyLeiden UniversityLeidenThe Netherlands

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