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Scaling a Conditional Proximity Matrix to Symmetry
Published online by Cambridge University Press: 01 January 2025
Abstract
Two least squares procedures for symmetrization of a conditional proximity matrix are derived. The solutions provide multiplicative constants for scaling the rows or columns of the matrix to maximize symmetry. It is suggested that the symmetrization is applicable for the elimination of bias effects like response bias, or constraints on the marginal frequencies imposed by the experimental design, as in confusion matrices.
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- Copyright © 1979 The Psychometric Society
Footnotes
The application of the scaling procedure to a matrix of conditional probabilities was suggested by one of the referees, whose helpful comments are gratefully acknowledged.
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