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Multidimensional successive categories scaling: A maximum likelihood method

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Abstract

A single-step maximum likelihood estimation procedure is developed for multidimensional scaling of dissimilarity data measured on rating scales. The procedure can fit the euclidian distance model to the data under various assumptions about category widths and under two distributional assumptions. The scoring algorithm for parameter estimation has been developed and implemented in the form of a computer program. Practical uses of the method are demonstrated with an emphasis on various advantages of the method as a statistical procedure.

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Reference notes

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Authors and Affiliations

  1. Department of Psychology, McGill University, 1205 Docteur Penfield Avenue, H3A 1B1, Montreal, Quebec, Canada

    Yoshio Takane

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  1. Yoshio Takane
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Additional information

The research reported here was partly supported by Grant A6394 to the author by Natural Sciences and Engineering Research Council of Canada. Portions of this research were presented at the Psychometric Society meeting in Uppsala, Sweden, in June, 1978. MAXSCAL-2.1, a program to perform the computations discussed in this paper may be obtained from the author. Thanks are due to Jim Ramsay for his helpful comments.

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Takane, Y. Multidimensional successive categories scaling: A maximum likelihood method. Psychometrika 46, 9–28 (1981). https://doi.org/10.1007/BF02293914

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  • DOI: https://doi.org/10.1007/BF02293914

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