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A Stochastic Multidimensional Unfolding Approach for Representing Phased Decision Outcomes

Published online by Cambridge University Press:  01 January 2025

Wayne S. DeSarbo ,
Donald R. Lehmann ,
Gregory Carpenter  and
Indrajit Sinha
Wayne S. DeSarbo*
Affiliation:
Marketing Department, Pennsylvania State University
Donald R. Lehmann
Affiliation:
Marketing Department, Columbia University
Gregory Carpenter
Affiliation:
Marketing Department, Northwestern University
Indrajit Sinha
Affiliation:
Marketing Department, Temple University
*
Requests for reprints should be sent to Wayne S. DeSarbo, Department of Marketing, Smeal School of Business, Pennsylvania State University, University Park PA 16802.
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Abstract

This paper presents a stochastic multidimensional unfolding (MDU) procedure to spatially represent individual differences in phased or sequential decision processes. The specific application or scenario to be discussed involves the area of consumer psychology where consumers form judgments sequentially in their awareness, consideration, and choice set compositions in a phased or sequential manner as more information about the alternative brands in a designated product/service class are collected. A brief review of the consumer psychology literature on these nested congnitive sets as stages in phased decision making is provided. The technical details of the proposed model, maximum likelihood estimation framework, and algorithm are then discussed. A small scale Monte Carlo analysis is presented to demonstrate estimation proficiency and the appropriateness of the proposed model selection heuristic. An application of the methodology to capture awareness, consideration, and choice sets in graduate school applicants is presented. Finally, directions for future research and other potential applications are given.

Keywords

consumer psychologymultidimensional scalingmaximum likelihoodconsideration setsmultidimensional unfoldingsuccessive categories analysis
Type
Original Paper
Information
Psychometrika , Volume 61 , Issue 3 , September 1996 , pp. 485 - 508
Copyright
Copyright © 1996 The Psychometric Society

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