A probabilistic multidimensional scaling vector model

DeSarbo, Wayne S.
Oliver, Richard L.
De Soete, Geert
1986
Thumbnail Image

View/Download File

v10n1p079.pdf (1.92 MB)

Persistent link to this item

https://hdl.handle.net/11299/102281
Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

A probabilistic multidimensional scaling vector model

Authors

Published Date

1986

Publisher

Type

Article

Abstract

This article presents the development of a new stochastic multidimensional scaling (MDS) method, which operates on paired comparisons data and renders a spatial representation of subjects and stimuli. Subjects are represented as vectors and stimuli as points in a T-dimensional space, where the scalar products, or projections of the stimulus points onto the subject vectors, provide respective information as to the utility (or whatever latent construct is under investigation) of the stimuli to the subjects. The psychometric literature concerning related MDS methods that also operate on paired comparisons data is reviewed, and a technical description of the new method is provided. A small monte carlo analysis performed on synthetic data with the new method is also presented. To illustrate the versatility of the model, an application measuring consumer satisfaction and investigating the impact of hypothesized determinants, using one of the optional reparameterized models, is described. Future areas of further research are identified.

Keywords

Description

Related to

Replaces

License

Collections

Applied Psychological Measurement, Volume 10, 1986

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

DeSarbo, Wayne S, Oliver, Richard L & de Soete, Geert. (1986). A probabilistic multidimensional scaling vector model. Applied Psychological Measurement, 10, 79-98. doi:10.1177/014662168601000107

Suggested citation

DeSarbo, Wayne S.; Oliver, Richard L.; De Soete, Geert. (1986). A probabilistic multidimensional scaling vector model. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/102281.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.