A new constrained stochastic multidimensional scaling vector model

Crystal J. Scott (College of Business, University of Michigan – Dearborn, Dearborn, Michigan, USA)
Wayne S. DeSarbo (Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania, USA)

Journal of Modelling in Management

ISSN: 1746-5664

Publication date: 22 March 2011

Abstract

Purpose

Multidimensional scaling (MDS) represents a family of various geometric models for the multidimensional representation of the structure in data as well as the corresponding set of methods for fitting such spatial models. Its major uses in business include positioning, market segmentation, new product design, consumer preference analysis, etc. The purpose of this paper is to apply a new stochastic constrained MDS vector model to examine the importance of some 45 different leadership attributes as they impact perceptions of effective leadership practice.

Design/methodology/approach

The authors present a new stochastic constrained MDS vector model for the analysis of two‐way dominance data.

Findings

This constrained vector or scalar products model represents the column objects of the input data matrix by points and row objects by vectors in a T‐dimensional derived joint space. Reparameterization options are available for row and/or column representations so as to constrain or reparameterize such objects as functions of designated features or attributes. An iterative maximum likelihood‐based algorithm is devised for efficient parameter estimation.

Originality/value

The authors present an application to a study conducted to examine the importance of leadership attributes as they impact perceptions of effective leadership practice. Implications for future research and limitations are discussed.

Keywords

Citation

Scott, C. and DeSarbo, W. (2011), "A new constrained stochastic multidimensional scaling vector model", Journal of Modelling in Management, Vol. 6 No. 1, pp. 7-32. https://doi.org/10.1108/17465661111112485

Download as .RIS

Publisher

:

Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

Please note you might not have access to this content

You may be able to access this content by login via Shibboleth, Open Athens or with your Emerald account.
If you would like to contact us about accessing this content, click the button and fill out the form.
To rent this content from Deepdyve, please click the button.