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Asymmetric Multidimensional Scaling of N-Mode M-Way Categorical Data using a Log-Linear Model

Asymmetric Multidimensional Scaling of N-Mode M-Way Categorical Data using a Log-Linear Model Asymmetric multidimensional scaling (AMDS) is a visualization method that can be applied to asymmetric (dis)similarity data, which are adopted in several areas such as marketing research, psychometrics, and information science. Several researchers within these fields have examined and applied AMDS. However, the combination of the number of modes and ways remains fixed across these AMDS. Thus, the choice of model is largely contingent on the number of modes and ways. To overcome this problem, we propose an AMDS using a log-linear model with an m-way frequency table. Using the log-linear model, we apply AMDS to (dis)similarity data without a fixed number of modes and ways. In addition, we were able to simultaneously visualize two types of circles. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Behaviormetrika Springer Journals

Asymmetric Multidimensional Scaling of N-Mode M-Way Categorical Data using a Log-Linear Model

Tsuchida, Jun; Yadohisa, Hiroshi
Behaviormetrika , Volume 43 (2) – Jul 15, 2016
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36 pages

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Publisher
Springer Journals
Copyright
Copyright © 2016 by The Behaviormetric Society
Subject
Statistics; Statistical Theory and Methods; Statistics for Business, Management, Economics, Finance, Insurance; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
ISSN
0385-7417
eISSN
1349-6964
DOI
10.2333/bhmk.43.103
Publisher site
See Article on Publisher Site

Abstract

Asymmetric multidimensional scaling (AMDS) is a visualization method that can be applied to asymmetric (dis)similarity data, which are adopted in several areas such as marketing research, psychometrics, and information science. Several researchers within these fields have examined and applied AMDS. However, the combination of the number of modes and ways remains fixed across these AMDS. Thus, the choice of model is largely contingent on the number of modes and ways. To overcome this problem, we propose an AMDS using a log-linear model with an m-way frequency table. Using the log-linear model, we apply AMDS to (dis)similarity data without a fixed number of modes and ways. In addition, we were able to simultaneously visualize two types of circles.

Journal

Behaviormetrika – Springer Journals

Published: Jul 15, 2016

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